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Virtual 3D city models increasingly cover whole city areas; hence, the perception of complex urban structures becomes increasingly difficult. Using abstract visualization, complexity of these models can be hidden where its visibility is unnecessary, while important features are maintained and highlighted for better comprehension and communication. We present a technique to automatically generalize a given virtual 3D city model consisting of building models, an infrastructure network and optional land coverage data; this technique creates several representations of increasing levels of abstraction. Using the infrastructure network, our technique groups building models and replaces them with cell blocks, while preserving local landmarks. By computing a landmark hierarchy, we reduce the set of initial landmarks in a spatially balanced manner for use in higher levels of abstraction. In four application examples, we demonstrate smooth visualization of transitions between precomputed representations; dynamic landmark highlighting according to virtual camera distance; an implementation of a cognitively enhanced route representation, and generalization lenses to combine precomputed representations in focus + context visualization.
Interacting services play a key role to realize business process integration among different business partners by means of electronic message exchange. In order to provide seamless integration of these services, the messages exchanged as well as their dependencies must be well-defined. Service choreographies are a means to describe the allowed conversations. This article presents a requirements framework for service choreography languages, along which existing choreography languages are assessed. The requirements framework provides the basis for introducing the language BPEL4Chor, which extends the industry standard WS-BPEL with choreography-specific concepts. A validation is provided and integration with executable service orchestrations is discussed.
Process instantiation
(2009)
Although several process modeling languages allow one to specify processes with multiple start elements, the precise semantics of such models are often unclear, both from a pragmatic and from a theoretical point of view. This paper addresses the lack of research on this problem and introduces the CASU framework (from Creation, Activation, subscription, Unsubscription). The contribution of this framework is a systematic description of design alternatives for the specification of instantiation semantics of process modeling languages. We classify six prominent languages by the help of this framework. We validate the relevance of the CASU framework through empirical investigations involving a large set of process models from practice. Our work provides the basis for the design of new correctness criteria as well as for the formalization of Event-driven Process Chains (EPCs) and extension of the Business Process Modeling Notation (BPMN). It complements research such as the workflow patterns.
Many of today's distributed computing systems in the field do not Support the migration of execution entities among computing nodes (luring runtime. The relatively static association between units of processing and computing nodes makes it difficult to implement fault-tolerant behavior or load-balancing schemes. The concept of code migration may provide a solution to the above-mentioned problems. it can be defined as the movement of processes, objects, or components from one computing node to another during system runtime in a distributed environment. With the advent of the virtual machine-based NET framework, many of the cross-language heterogeneity issues have been resolved. With the commercial implementation, the shared source "Rotor", and the open-source "Mono" implementation on hand, we have focused on cross-operating system heterogeneity issues and present interoperability and migration schemes for applications distributed over different operating systems (namely Linux and Windows 2000) as well as various NET implementations. Within this paper, we describe the integration of a migration facility with the hell) of Aspect- Oriented Programming (AOP) into the NET framework. AOP is interesting as it addresses non-functional system properties on the middleware level, without the need to manipulate lower system layers like the operating system itself. Most features required to implement object or process migration (such as reflection mechanisms or a machine-independent executable format) are already present in the NET frameworks, so the integration of such a concept is a natural extension of the system capabilities. We have implemented several proof-of-concept applications for different use case scenarios. The paper contains an experimental evaluation of the performance impact of object migration in context of those applications.
Resynthesis of handshake specifications obtained e. g. from BALSA or TANGRAM with speed-independent logic synthesis from STGs is a promising approach. To deal with state-space explosion, we suggested STG decomposition; a problem is that decomposition can lead to irreducible CSC conflicts. Here, we present a new approach to solve such conflicts by introducing internal communication between the components. We give some first, very encouraging results for very large STGs concerning synthesis time and circuit area.
For synthesising efficient asynchronous circuits one has to deal with the state space explosion problem. In order to alleviate this problem one can decompose the STG into smaller components. This paper deals with the decomposition method of Vogler and Wollowski and introduces several strategies for its efficient implementations. Furthermore, this approach is combined with another method to alleviate state space explosion, which is based on Petri net unfoldings. The developed algorithms are compared by means of benchmark examples, and the experimental results show significant improvement in terms of memory usage and runtime compared with other existing methods.
Virtual machine (VM) implementations are made of intricately intertwined subsystems, interacting largely through implicit dependencies. As the degree of crosscutting present in VMs is very high, VM implementations exhibit significant internal complexity. This study proposes an architecture approach for VMs that regards a VM as a composite of service modules coordinated through explicit bidirectional interfaces. Aspect-oriented programming techniques are used to establish these interfaces, to coordinate module interaction, and to declaratively express concrete VM architectures. A VM architecture description language is presented in a case study, illustrating the application of the proposed architectural principles.
The model-driven software development paradigm requires that appropriate model transformations are applicable in different stages of the development process. The transformations have to consistently propagate changes between the different involved models and thus ensure a proper model synchronization. However, most approaches today do not fully support the requirements for model synchronization and focus only on classical one-way batch-oriented transformations. In this paper, we present our approach for an incremental model transformation which supports model synchronization. Our approach employs the visual, formal, and bidirectional transformation technique of triple graph grammars. Using this declarative specification formalism, we focus on the efficient execution of the transformation rules and how to achieve an incremental model transformation for synchronization purposes. We present an evaluation of our approach and demonstrate that due to the speedup for the incremental processing in the average case even larger models can be tackled.
In the world of model-driven engineering (MDE) support for traceability and maintenance of traceability information is essential. On the one hand, classical traceability approaches for MDE address this need by supporting automated creation of traceability information on the model element level. On the other hand, global model management approaches manually capture traceability information on the model level. However, there is currently no approach that supports comprehensive traceability, comprising traceability information on both levels, and efficient maintenance of traceability information, which requires a high-degree of automation and scalability. In this article, we present a comprehensive traceability approach that combines classical traceability approaches for MDE and global model management in form of dynamic hierarchical mega models. We further integrate efficient maintenance of traceability information based on top of dynamic hierarchical mega models. The proposed approach is further outlined by using an industrial case study and by presenting an implementation of the concepts in form of a prototype.
The application of the architectural concept of service-oriented architectures (SOA) in combination with open standards when building distributed, 3D geovisualization systems offers the potential to cover and take advantage of the opportunities and demands created by the rise of ubiquitous computer networks and the Internet as well as to overcome prevalent interoperability barriers. In this paper, based on a literature study and our own experiences, we discuss the potential and challenges that arise when building standards-based, distributed systems according to the SOA paradigm for 3D geovisualization, with a particular focus on 3D geovirtual environments and virtual 3D city models. First, we briefly introduce fundamentals of the SOA paradigm, identify requirements for service-oriented 3D geovisualization systems, and present an architectural framework that relates SOA concepts, geovisualization concepts, and standardization proposals by the Open Geospatial Consortium in a common frame of reference. Next, we discuss the potential and challenges driven by the SOA paradigm on four different levels of abstraction, namely service fundamentals, service composition, interaction services, performance, and overarching aspects, and we discuss those driven by standardization. We further exemplify and substantiate the discussion in the scope of a case study and the image-based provisioning of and interaction with visual representations of remote virtual 3D city models.
Integrated real-time visualisation of massive 3D-Point clouds and geo-referenced textured dates
(2011)
Modern communication systems are becoming increasingly dynamic and complex. In this article a novel mechanism for next generation charging and billing is presented that enables self-configurability for accounting systems consisting of heterogeneous components. The mechanism is required to be simple, effective, efficient, scalable and fault-tolerant. Based on simulation results it is shown that the proposed simple distributed mechanism is competitive with usual cost-based or random mechanisms under realistic assumptions and up to non-extreme workload situations as well as fulfilling the posed requirements.
The importance of reporting is ever increasing in today's fast-paced market environments and the availability of up-to-date information for reporting has become indispensable. Current reporting systems are separated from the online transaction processing systems (OLTP) with periodic updates pushed in. A pre-defined and aggregated subset of the OLTP data, however, does not provide the flexibility, detail, and timeliness needed for today's operational reporting. As technology advances, this separation has to be re-evaluated and means to study and evaluate new trends in data storage management have to be provided. This article proposes a benchmark for combined OLTP and operational reporting, providing means to evaluate the performance of enterprise data management systems for mixed workloads of OLTP and operational reporting queries. Such systems offer up-to-date information and the flexibility of the entire data set for reporting. We describe how the benchmark provokes the conflicts that are the reason for separating the two workloads on different systems. In this article, we introduce the concepts, logical data schema, transactions and queries of the benchmark, which are entirely based on the original data sets and real workloads of existing, globally operating enterprises.
Context-oriented programming (COP) provides dedicated support for defining and composing variations to a basic program behavior. A variation, which is defined within a layer, can be de-/activated for the dynamic extent of a code block. While this mechanism allows for control flow-specific scoping, expressing behavior adaptations can demand alternative scopes. For instance, adaptations can depend on dynamic object structure rather than control flow. We present scenarios for behavior adaptation and identify the need for new scoping mechanisms. The increasing number of scoping mechanisms calls for new language abstractions representing them. We suggest to open the implementation of scoping mechanisms so that developers can extend the COP language core according to their specific needs. Our open implementation moves layer composition into objects to be affected and with that closer to the method dispatch to be changed. We discuss the implementation of established COP scoping mechanisms using our approach and present new scoping mechanisms developed for our enhancements to Lively Kernel.
The next generation of advanced mechatronic systems is expected to enhance their functionality and improve their performance by context-dependent behavior. Therefore, these systems require to represent information about their complex environment and changing sets of collaboration partners internally. This requirement is in contrast to the usually assumed static structures of embedded systems. In this paper, we present a model-driven approach which overcomes this situation by supporting dynamic data structures while still guaranteeing that valid worst-case execution times can be derived. It supports a flexible resource manager which avoids to operate with the prohibitive coarse worst-case boundaries but instead supports to run applications in different profiles which guarantee different resource requirements and put unused resources in a profile at other applications' disposal. By supporting the proper estimation of worst case execution time (WCET) and worst case number of iteration (WCNI) at runtime, we can further support to create new profiles, add or remove them at runtime in order to minimize the over-approximation of the resource consumption resulting from the dynamic data structures required for the outlined class of advanced systems.
With the rise of electronic integration between organizations, the need for a precise specification of interaction behavior increases. Information systems, replacing interaction previously carried out by humans via phone, faxes and emails, require a precise specification for handling all possible situations. Such interaction behavior is described in process choreographies. While many proposals for choreography languages have already been made, most of them fall into the category of interconnection models, where the observable behavior of the different partners is described and then related via message flow. As this article will show, this modeling approach fails to support fundamental design principles of choreographies and typically leads to modeling errors. This motivates an alternative modeling style, namely interaction modeling, for overcoming these limitations. While the main concepts are independent of a concrete modeling language, iBPMN is introduced as novel interaction modeling language. Formal execution semantics are provided and a comprehensive toolset implementing the approach is presented.
A business process is a set of steps designed to be executed in a certain order to achieve a business value. Such processes are often driven by and documented using process models. Nowadays, process models are also applied to drive process execution. Thus, correctness of business process models is a must. Much of the work has been devoted to check general, domain-independent correctness criteria, such as soundness. However, business processes must also adhere to and show compliance with various regulations and constraints, the so-called compliance requirements. These are domain-dependent requirements.
In many situations, verifying compliance on a model level is of great value, since violations can be resolved in an early stage prior to execution. However, this calls for using formal verification techniques, e.g., model checking, that are too complex for business experts to apply. In this paper, we utilize a visual language. BPMN-Q to express compliance requirements visually in a way similar to that used by business experts to build process models. Still, using a pattern based approach, each BPMN-Qgraph has a formal temporal logic expression in computational tree logic (CTL). Moreover, the user is able to express constraints, i.e., compliance rules, regarding control flow and data flow aspects. In order to provide valuable feedback to a user in case of violations, we depend on temporal logic querying approaches as well as BPMN-Q to visually highlight paths in a process model whose execution causes violations.
Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system specification and a corresponding workflow model used as implementation have to be consistent. Another example would be the analysis to what degree a process log of executed business operations is consistent with the corresponding normative process model. Typically, existing notions of behaviour equivalence, such as bisimulation and trace equivalence, are applied as consistency notions. Still, these notions are exponential in computation and yield a Boolean result. In many cases, however, a quantification of behavioural deviation is needed along with concepts to isolate the source of deviation.
In this article, we propose causal behavioural profiles as the basis for a consistency notion. These profiles capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities of a process model. Consistency based on these profiles is weaker than trace equivalence, but can be computed efficiently for a broad class of models. In this article, we introduce techniques for the computation of causal behavioural profiles using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S-or T-nets. We also elaborate on the findings of applying our technique to three industry model collections.
Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. In order to judge on compliance of the business processing, the degree of behavioural deviation of a case, i.e., an observed execution sequence, is quantified with respect to a process model (referred to as fitness, or recall). Recently, different compliance measures have been proposed. Still, nearly all of them are grounded on state-based techniques and the trace equivalence criterion, in particular. As a consequence, these approaches have to deal with the state explosion problem. In this paper, we argue that a behavioural abstraction may be leveraged to measure the compliance of a process log - a collection of cases. To this end, we utilise causal behavioural profiles that capture the behavioural characteristics of process models and cases, and can be computed efficiently. We propose different compliance measures based on these profiles, discuss the impact of noise in process logs on our measures, and show how diagnostic information on non-compliance is derived. As a validation, we report on findings of applying our approach in a case study with an international service provider.
Business processes are commonly modeled using a graphical modeling language. The most widespread notation for this purpose is business process diagrams in the Business Process Modeling Notation (BPMN). In this article, we use the visual query language BPMN-Q for expressing patterns that are related to possible problems in such business process diagrams. We discuss two classes of problems that can be found frequently in real-world models: sequence flow errors and model fragments that can make the model difficult to understand.
By using a query processor, a business process modeler is able to identify possible errors in business process diagrams. Moreover, the erroneous parts of the business process diagram can be highlighted when an instance of an error pattern is found. This way, the modeler gets an easy-to-understand feedback in the visual modeling language he or she is familiar with. This is an advantage over current validation methods, which usually lack this kind of intuitive feedback.
In this article, we aim to evaluate the influences of different propagation models on the results of V2X simulations. First, we analyze how the models free space propagation, Rayleigh fading, and Ricean fading in synthetic scenarios with and without background communication influence the simulation of communication in general. After that, we investigate the impact of the models on the simulated behavior of a V2X traffic efficiency application in a real inner city scenario. Our results show that the selection of the propagation model affects the number of delivered messages, but exerts no significant influence on the simulated effectiveness of a V2X traffic efficiency application in urban areas. Under those circumstances, a simplified propagation model is sufficient.
Logic synthesis of speed independent circuits based on signal transition graph (STG) decomposition is a promising approach to tackle complexity problems like state-space explosion. Unfortunately, decomposition can result in components that in isolation have irreducible complete state coding conflicts. In earlier work, the authors showed how to resolve such conflicts by introducing internal communication between components, but only for very restricted specification structures. Here, they improve their former work by presenting algorithms for identifying delay transitions and inserting gyroscopes for specifications having a much more general structure. Thus, the authors are now able to synthesise controllers from real-life specifications. For all algorithms, they present correctness proofs and show their successful application to benchmarks, including very complex STGs arising in the context of control resynthesis.
In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.
Virtual 3D city models serve as an effective medium with manifold applications in geoinformation systems and services. To date, most 3D city models are visualized using photorealistic graphics. But an effective communication of geoinformation significantly depends on how important information is designed and cognitively processed in the given application context. One possibility to visually emphasize important information is based on non-photorealistic rendering, which comprehends artistic depiction styles and is characterized by its expressiveness and communication aspects. However, a direct application of non-photorealistic rendering techniques primarily results in monotonic visualization that lacks cartographic design aspects. In this work, we present concepts for cartography-oriented visualization of virtual 3D city models. These are based on coupling non-photorealistic rendering techniques and semantics-based information for a user, context, and media-dependent representation of thematic information. This work highlights challenges for cartography-oriented visualization of 3D geovirtual environments, presents stylization techniques and discusses their applications and ideas for a standardized visualization. In particular, the presented concepts enable a real-time and dynamic visualization of thematic geoinformation.
Virtual 3D city models play an important role in the communication of complex geospatial information in a growing number of applications, such as urban planning, navigation, tourist information, and disaster management. In general, homogeneous graphic styles are used for visualization. For instance, photorealism is suitable for detailed presentations, and non-photorealism or abstract stylization is used to facilitate guidance of a viewer's gaze to prioritized information. However, to adapt visualization to different contexts and contents and to support saliency-guided visualization based on user interaction or dynamically changing thematic information, a combination of different graphic styles is necessary. Design and implementation of such combined graphic styles pose a number of challenges, specifically from the perspective of real-time 3D visualization. In this paper, the authors present a concept and an implementation of a system that enables different presentation styles, their seamless integration within a single view, and parametrized transitions between them, which are defined according to tasks, camera view, and image resolution. The paper outlines potential usage scenarios and application fields together with a performance evaluation of the implementation.
Use-cases are considered an integral part of most contemporary development processes since they describe a software system's expected behavior from the perspective of its prospective users. However, the presence of and traceability to use-cases is increasingly lost in later more code-centric development activities. Use-cases, being well-encapsulated at the level of requirements descriptions, eventually lead to crosscutting concerns in system design and source code. Tracing which parts of the system contribute to which use-cases is therefore hard and so limits understandability.
In this paper, we propose an approach to making use-cases first-class entities in both the programming language and the runtime environment. Having use-cases present in the code and the running system will allow developers, maintainers, and operators to easily associate their units of work with what matters to the users. We suggest the combination of use-cases, acceptance tests, and dynamic analysis to automatically associate source code with use-cases. We present UseCasePy, an implementation of our approach to use-case-centered development in Python, and its application to the Django Web framework.
Process-aware information systems typically involve various kinds of process stakeholders. That, in turn, leads to multiple process models that capture a common process from different perspectives and at different levels of abstraction. In order to guarantee a certain degree of uniformity, the consistency of such related process models is evaluated using formal criteria. However, it is unclear how modelling experts assess the consistency between process models, and which kind of notion they perceive to be appropriate.
In this paper, we focus on control flow aspects and investigate the adequacy of consistency notions. In particular, we report findings from an online experiment, which allows us to compare in how far trace equivalence and two notions based on behavioural profiles approximate expert perceptions on consistency. Analysing 69 expert statements from process analysts, we conclude that trace equivalence is not suited to be applied as a consistency notion, whereas the notions based on behavioural profiles approximate the perceived consistency of our subjects significantly. Therefore, our contribution is an empirically founded answer to the correlation of behaviour consistency notions and the consistency perception by experts in the field of business process modelling.
This article addresses the problem of estimating the Quality of Service (QoS) of a composite service given the QoS of the services participating in the composition. Previous solutions to this problem impose restrictions on the topology of the orchestration models, limiting their applicability to well-structured orchestration models for example. This article lifts these restrictions by proposing a method for aggregate QoS computation that deals with more general types of unstructured orchestration models. The applicability and scalability of the proposed method are validated using a collection of models from industrial practice.
There is a wide variety of drivers for business process modelling initiatives, reaching from organisational redesign to the development of information systems. Consequently, a common business process is often captured in multiple models that overlap in content due to serving different purposes. Business process management aims at flexible adaptation to changing business needs. Hence, changes of business processes occur frequently and have to be incorporated in the respective process models. Once a process model is changed, related process models have to be updated accordingly, despite the fact that those process models may only be loosely coupled. In this article, we introduce an approach that supports change propagation between related process models. Given a change in one process model, we leverage the behavioural abstraction of behavioural profiles for corresponding activities in order to determine a change region in another model. Our approach is able to cope with changes in pairs of models that are not related by hierarchical refinement and show behavioural inconsistencies. We evaluate the applicability of our approach with two real-world process model collections. To this end, we either deduce change operations from different model revisions or rely on synthetic change operations.
Graph transformation systems have been studied extensively and applied to several areas of computer science like formal language theory, the modeling of databases, concurrent or distributed systems, and visual, logical, and functional programming. In most kinds of applications it is necessary to have the possibility of restricting the applicability of rules. This is usually done by means of application conditions. In this paper, we continue the work of extending the fundamental theory of graph transformation to the case where rules may use arbitrary (nested) application conditions. More precisely, we generalize the Embedding theorem, and we study how local confluence can be checked in this context. In particular, we define a new notion of critical pair which allows us to formulate and prove a Local Confluence Theorem for the general case of rules with nested application conditions. All our results are presented, not for a specific class of graphs, but for any arbitrary M-adhesive category, which means that our results apply to most kinds of graphical structures. We demonstrate our theory on the modeling of an elevator control by a typed graph transformation system with positive and negative application conditions.
Lazy graph transformation
(2012)
Applying an attributed graph transformation rule to a given object graph always implies some kind of constraint solving. In many cases, the given constraints are almost trivial to solve. For instance, this is the case when a rule describes a transformation G double right arrow H, where the attributes of H are obtained by some simple computation from the attributes of G. However there are many other cases where the constraints to solve may be not so trivial and, moreover, may have several answers. This is the case, for instance, when the transformation process includes some kind of searching. In the current approaches to attributed graph transformation these constraints must be completely solved when defining the matching of the given transformation rule. This kind of early binding is well-known from other areas of Computer Science to be inadequate. For instance, the solution chosen for the constraints associated to a given transformation step may be not fully adequate, meaning that later, in the search for a better solution, we may need to backtrack this transformation step.
In this paper, based on our previous work on the use of symbolic graphs to deal with different aspects related with attributed graphs, including attributed graph transformation, we present a new approach that, based on the new notion of narrowing graph transformation rule, allows us to delay constraint solving when doing attributed graph transformation, in a way that resembles lazy computation. For this reason, we have called lazy this new kind of transformation. Moreover, we show that the approach is sound and complete with respect to standard attributed graph transformation. A running example, where a graph transformation system describes some basic operations of a travel agency, shows the practical interest of the approach.
Behaviour equivalence and compatibility of business process models with complex correspondences
(2012)
Once multiple models of a business process are created for different purposes or to capture different variants, verification of behaviour equivalence or compatibility is needed. Equivalence verification ensures that two business process models specify the same behaviour. Since different process models are likely to differ with respect to their assumed level of abstraction and the actions that they take into account, equivalence notions have to cope with correspondences between sets of actions and actions that exist in one process but not in the other. In this paper, we present notions of equivalence and compatibility that can handle these problems. In essence, we present a notion of equivalence that works on correspondences between sets of actions rather than single actions. We then integrate our equivalence notion with work on behaviour inheritance that copes with actions that exist in one process but not in the other, leading to notions of behaviour compatibility. Compatibility notions verify that two models have the same behaviour with respect to the actions that they have in common. As such, our contribution is a collection of behaviour equivalence and compatibility notions that are applicable in more general settings than existing ones.
This article studies the problem of transforming a process model with an arbitrary topology into an equivalent well-structured process model. While this problem has received significant attention, there is still no full characterization of the class of unstructured process models that can be transformed into well-structured ones, nor an automated method for structuring any process model that belongs to this class. This article fills this gap in the context of acyclic process models. The article defines a necessary and sufficient condition for an unstructured acyclic process model to have an equivalent well-structured process model under fully concurrent bisimulation, as well as a complete structuring method. The method has been implemented as a tool that takes process models captured in the BPMN and EPC notations as input. The article also reports on an empirical evaluation of the structuring method using a repository of process models from commercial practice.
In mobile telecommunication networks the trend for an increasing heterogeneity of access networks, the convergence with fixed networks as well as with the Internet are apparent. The resulting future converged network with an expected wide variety of services and a possibly stiff competition between the different market participants as well as legal issues will bring about requirements for charging systems that demand for more flexibility, scalability and efficiency than is available in today's systems. This article surveys recent developments in charging and billing architectures comprising both standardisation work as well as research projects. The second main contribution of this article is a comparison of key features of these developments thus giving a list of essential charging and billing ingredients for tomorrow's communication and service environments.
Intrusion Detection Systems (IDS) have been widely deployed in practice for detecting malicious behavior on network communication and hosts. False-positive alerts are a popular problem for most IDS approaches. The solution to address this problem is to enhance the detection process by correlation and clustering of alerts. To meet the practical requirements, this process needs to be finished fast, which is a challenging task as the amount of alerts in large-scale IDS deployments is significantly high. We identifytextitdata storage and processing algorithms to be the most important factors influencing the performance of clustering and correlation. We propose and implement a highly efficient alert correlation platform. For storage, a column-based database, an In-Memory alert storage, and memory-based index tables lead to significant improvements of the performance. For processing, algorithms are designed and implemented which are optimized for In-Memory databases, e.g. an attack graph-based correlation algorithm. The platform can be distributed over multiple processing units to share memory and processing power. A standardized interface is designed to provide a unified view of result reports for end users. The efficiency of the platform is tested by practical experiments with several alert storage approaches, multiple algorithms, as well as a local and a distributed deployment.
This paper presents the state of the art in the development of Semantic-Web-enabled software using object-oriented programming languages. Object triple mapping (OTM) is a frequently used method to simplify the development of such software. A case study that is based on interviews with developers of OTM frameworks is presented at the core of this paper. Following the results of the case study, the formalization of OTM is kept separate from optional but desirable extensions of OTM with regard to metadata, schema matching, and integration into the Semantic-Web infrastructure. The material that is presented is expected to not only explain the development of Semantic-Web software by the usage of OTM, but also explain what properties of Semantic-Web software made developers come up with OTM. Understanding the latter will be essential to get nonexpert software developers to use Semantic-Web technologies in their software.
Spam has posed a serious problem for users of email since its infancy. Today, automated strategies are required to deal with the massive amount of spam traffic. IPv4 networks offer a variety of solutions to reduce spam, but IPv6 networks' large address space and use of temporary addresses - both of which are particularly vulnerable to spam attacks - makes dealing with spam and the use of automated approaches much more difficult. IPv6 thus poses a unique security issue for ISPs because it's more difficult for them to differentiate between good IP addresses and those that are known to originate spam messages.
The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation (LDA) to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference (TREC) Enterprise track for 2005 and 2006 show that the proposed topic-based approach outperforms the state-of-the-art profile- and document-based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic-based approach to the improved document-based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion.
Intrusion Detection Systems are widely deployed in computer networks. As modern attacks are getting more sophisticated and the number of sensors and network nodes grow, the problem of false positives and alert analysis becomes more difficult to solve. Alert correlation was proposed to analyse alerts and to decrease false positives. Knowledge about the target system or environment is usually necessary for efficient alert correlation. For representing the environment information as well as potential exploits, the existing vulnerabilities and their Attack Graph (AG) is used. It is useful for networks to generate an AG and to organize certain vulnerabilities in a reasonable way. In this article, a correlation algorithm based on AGs is designed that is capable of detecting multiple attack scenarios for forensic analysis. It can be parameterized to adjust the robustness and accuracy. A formal model of the algorithm is presented and an implementation is tested to analyse the different parameters on a real set of alerts from a local network. To improve the speed of the algorithm, a multi-core version is proposed and a HMM-supported version can be used to further improve the quality. The parallel implementation is tested on a multi-core correlation platform, using CPUs and GPUs.
Concepts and techniques for integration, analysis and visualization of massive 3D point clouds
(2014)
Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges.
A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behaviorial constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the all, seq, and any patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.
Virtual 3D city models serve as integration platforms for complex geospatial and georeferenced information and as medium for effective communication of spatial information. In order to explore these information spaces, navigation techniques for controlling the virtual camera are required to facilitate wayfinding and movement. However, navigation is not a trivial task and many available navigation techniques do not support users effectively and efficiently with their respective skills and tasks. In this article, we present an assisting, constrained navigation technique for multiscale virtual 3D city models that is based on three basic principles: users point to navigate, users are lead by suggestions, and the exploitation of semantic, multiscale, hierarchical structurings of city models. The technique particularly supports users with low navigation and virtual camera control skills but is also valuable for experienced users. It supports exploration, search, inspection, and presentation tasks, is easy to learn and use, supports orientation, is efficient, and yields effective view properties. In particular, the technique is suitable for interactive kiosks and mobile devices with a touch display and low computing resources and for use in mobile situations where users only have restricted resources for operating the application. We demonstrate the validity of the proposed navigation technique by presenting an implementation and evaluation results. The implementation is based on service-oriented architectures, standards, and image-based representations and allows exploring massive virtual 3D city models particularly on mobile devices with limited computing resources. Results of a user study comparing the proposed navigation technique with standard techniques suggest that the proposed technique provides the targeted properties, and that it is more advantageous to novice than to expert users.
Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.
Model transformation is one of the key tasks in model-driven engineering and relies on the efficient matching and modification of graph-based data structures; its sibling graph rewriting has been used to successfully model problems in a variety of domains. Over the last years, a wide range of graph and model transformation tools have been developed all of them with their own particular strengths and typical application domains. In this paper, we give a survey and a comparison of the model and graph transformation tools that participated at the Transformation Tool Contest 2011. The reader gains an overview of the field and its tools, based on the illustrative solutions submitted to a Hello World task, and a comparison alongside a detailed taxonomy. The article is of interest to researchers in the field of model and graph transformation, as well as to software engineers with a transformation task at hand who have to choose a tool fitting to their needs. All solutions referenced in this article provide a SHARE demo. It supported the peer-review process for the contest, and now allows the reader to test the tools online.
Nested application conditions generalise the well-known negative application conditions and are important for several application domains. In this paper, we present Local Church-Rosser, Parallelism, Concurrency and Amalgamation Theorems for rules with nested application conditions in the framework of M-adhesive categories, where M-adhesive categories are slightly more general than weak adhesive high-level replacement categories. Most of the proofs are based on the corresponding statements for rules without application conditions and two shift lemmas stating that nested application conditions can be shifted over morphisms and rules.
With the growth of virtualization and cloud computing, more and more forensic investigations rely on being able to perform live forensics on a virtual machine using virtual machine introspection (VMI). Inspecting a virtual machine through its hypervisor enables investigation without risking contamination of the evidence, crashing the computer, etc. To further access to these techniques for the investigator/researcher we have developed a new VMI monitoring language. This language is based on a review of the most commonly used VMI-techniques to date, and it enables the user to monitor the virtual machine's memory, events and data streams. A prototype implementation of our monitoring system was implemented in KVM, though implementation on any hypervisor that uses the common x86 virtualization hardware assistance support should be straightforward. Our prototype outperforms the proprietary VMWare VProbes in many cases, with a maximum performance loss of 18% for a realistic test case, which we consider acceptable. Our implementation is freely available under a liberal software distribution license. (C) 2014 Digital Forensics Research Workshop. Published by Elsevier Ltd. All rights reserved.
This article addresses the transformation of a process model with an arbitrary topology into an equivalent structured process model. In particular, this article studies the subclass of process models that have no equivalent well-structured representation but which, nevertheless, can be partially structured into their maximally-structured representation. The transformations are performed under a behavioral equivalence notion that preserves the observed concurrency of tasks in equivalent process models. The article gives a full characterization of the subclass of acyclic process models that have no equivalent well-structured representation, but do have an equivalent maximally-structured one, as well as proposes a complete structuring method. Together with our previous results, this article completes the solution of the process model structuring problem for the class of acyclic process models.
The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by feedback loops. The engine often describes the adaptation by runtime models representing the adaptable software and by activities such as analysis and planning that use these models. To systematically address the interplay between runtime models and adaptation activities, runtime megamodels have been proposed. A runtime megamodel is a specific model capturing runtime models and adaptation activities. In this article, we go one step further and present an executable modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular feedback loops. Megamodels are kept alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops explicit at a higher level of abstraction and it enables solutions where multiple feedback loops interact or operate on top of each other and self-adaptation co-exists with offline adaptation for evolution.
Multiperiod robust optimization for proactive resource provisioning in virtualized data centers
(2014)
This article presents multi-perspective 3D panoramas that focus on visualizing 3D geovirtual environments (3D GeoVEs) for navigation and exploration tasks. Their key element, a multi-perspective view (MPV), seamlessly combines what is seen from multiple viewpoints into a single image. This approach facilitates the presentation of information for virtual 3D city and landscape models, particularly by reducing occlusions, increasing screen-space utilization, and providing additional context within a single image. We complement MPVs with cartographic visualization techniques to stylize features according to their semantics and highlight important or prioritized information. When combined, both techniques constitute the core implementation of interactive, multi-perspective 3D panoramas. They offer a large number of effective means for visual communication of 3D spatial information, a high degree of customization with respect to cartographic design, and manifold applications in different domains. We discuss design decisions of 3D panoramas for the exploration of and navigation in 3D GeoVEs. We also discuss a preliminary user study that indicates that 3D panoramas are a promising approach for navigation systems using 3D GeoVEs.
Texture mapping is a key technology in computer graphics. For the visual design of 3D scenes, in particular, effective texturing depends significantly on how important contents are expressed, e.g., by preserving global salient structures, and how their depiction is cognitively processed by the user in an application context. Edge-preserving image filtering is one key approach to address these concerns. Much research has focused on applying image filters in a post-process stage to generate artistically stylized depictions. However, these approaches generally do not preserve depth cues, which are important for the perception of 3D visualization (e.g., texture gradient). To this end, filtering is required that processes texture data coherently with respect to linear perspective and spatial relationships. In this work, we present an approach for texturing 3D scenes with perspective coherence by arbitrary image filters. We propose decoupled deferred texturing with (1) caching strategies to interactively perform image filtering prior to texture mapping and (2) for each mipmap level separately to enable a progressive level of abstraction, using (3) direct interaction interfaces to parameterize the visualization according to spatial, semantic, and thematic data. We demonstrate the potentials of our method by several applications using touch or natural language inputs to serve the different interests of users in specific information, including illustrative visualization, focus+context visualization, geometric detail removal, and semantic depth of field. The approach supports frame-to-frame coherence, order-independent transparency, multitexturing, and content-based filtering. In addition, it seamlessly integrates into real-time rendering pipelines and is extensible for custom interaction techniques. (C) 2015 Elsevier Ltd. All rights reserved.
Cartography-Oriented Design of 3D Geospatial Information Visualization - Overview and Techniques
(2015)
In economy, society and personal life map-based interactive geospatial visualization becomes a natural element of a growing number of applications and systems. The visualization of 3D geospatial information, however, raises the question how to represent the information in an effective way. Considerable research has been done in technology-driven directions in the fields of cartography and computer graphics (e.g., design principles, visualization techniques). Here, non-photorealistic rendering (NPR) represents a promising visualization category - situated between both fields - that offers a large number of degrees for the cartography-oriented visual design of complex 2D and 3D geospatial information for a given application context. Still today, however, specifications and techniques for mapping cartographic design principles to the state-of-the-art rendering pipeline of 3D computer graphics remain to be explored. This paper revisits cartographic design principles for 3D geospatial visualization and introduces an extended 3D semiotic model that complies with the general, interactive visualization pipeline. Based on this model, we propose NPR techniques to interactively synthesize cartographic renditions of basic feature types, such as terrain, water, and buildings. In particular, it includes a novel iconification concept to seamlessly interpolate between photorealistic and cartographic representations of 3D landmarks. Our work concludes with a discussion of open challenges in this field of research, including topics, such as user interaction and evaluation.
Business processes are vital to managing organizations as they sustain a company's competitiveness. Consequently, these organizations maintain collections of hundreds or thousands of process models for streamlining working procedures and facilitating process implementation. Yet, the management of large process model collections requires effective searching capabilities. Recent research focused on similarity search of process models, but querying process models is still a largely open topic. This article presents an approach to querying process models that takes a process example as input and discovers all models that allow replaying the behavior of the query. To this end, we provide a notion of behavioral inclusion that is based on trace semantics and abstraction. Additional to deciding a match, a closeness score is provided that describes how well the behavior of the query is represented in the model and can be used for ranking. The article introduces the formal foundations of the approach and shows how they are applied to querying large process model collections. An experimental evaluation has been conducted that confirms the suitability of the solution as well as its applicability and scalability in practice.
During the execution of business processes several events happen that are recorded in the company's information systems. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the run time of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process monitoring and analysis in terms of a large event log. In this paper, we present the concept to utilize information from these object state transition events for capturing process progress. Furthermore, we discuss a methodology to create the required design time artifacts that then are used for monitoring at run time. In a proof-of-concept implementation, we show how the design time and run time side work and prove applicability of the introduced concept of object state transition events. (C) 2015 Elsevier B.V. All rights reserved.
We present Pycket, a high-performance tracing JIT compiler for Racket. Pycket supports a wide variety of the sophisticated features in Racket such as contracts, continuations, classes, structures, dynamic binding, and more. On average, over a standard suite of benchmarks, Pycket outperforms existing compilers, both Racket's JIT and other highly-optimizing Scheme compilers. Further, Pycket provides much better performance for Racket proxies than existing systems, dramatically reducing the overhead of contracts and gradual typing. We validate this claim with performance evaluation on multiple existing benchmark suites.
The Pycket implementation is of independent interest as an application of the RPython meta-tracing framework (originally created for PyPy), which automatically generates tracing JIT compilers from interpreters. Prior work on meta-tracing focuses on bytecode interpreters, whereas Pycket is a high-level interpreter based on the CEK abstract machine and operates directly on abstract syntax trees. Pycket supports proper tail calls and first-class continuations. In the setting of a functional language, where recursion and higher-order functions are more prevalent than explicit loops, the most significant performance challenge for a tracing JIT is identifying which control flows constitute a loop-we discuss two strategies for identifying loops and measure their impact.
Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts. (C) 2015 Elsevier Ltd. All rights reserved.
We examine a special class of dynamic pricing and advertising models for the sale of perishable goods, including marginal unit costs and inventory holding costs. The time horizon is assumed to be finite and we allow several model parameters to be dependent on time. For the stochastic version of the model, we derive closed-form expressions of the value function as well as of the optimal pricing and advertising policy in feedback form. Moreover, we show that for small unit shares, the model converges to a deterministic version of the problem, whose explicit solution is characterized by an overage and an underage case. We quantify the close relationship between the open-loop solution of the deterministic model and the expected evolution of optimally controlled stochastic sales processes. For both models, we derive sensitivity results. We find that in the case of positive holding costs, on average, optimal prices increase in time and advertising rates decrease. Furthermore, we analytically verify the excellent quality of optimal feedback policies of deterministic models applied in stochastic models. (C) 2015 Elsevier B.V. All rights reserved.
Companies need to efficiently manage their business processes to deliver products and services in time. Therefore, they monitor the progress of individual cases to be able to timely detect undesired deviations and to react accordingly. For example, companies can decide to speed up process execution by raising alerts or by using additional resources, which increases the chance that a certain deadline or service level agreement can be met. Central to such process control is accurate prediction of the remaining time of a case and the estimation of the risk of missing a deadline.
To achieve this goal, we use a specific kind of stochastic Petri nets that can capture arbitrary duration distributions. Thereby, we are able to achieve higher prediction accuracy than related approaches. Further, we evaluate the approach in comparison to state of the art approaches and show the potential of exploiting a so far untapped source of information: the elapsed time since the last observed event. Real-world case studies in the financial and logistics domain serve to illustrate and evaluate the approach presented. (C) 2015 Elsevier Ltd. All rights reserved.
We report our experience in implementing SqueakJS, a bitcompatible implementation of Squeak/Smalltalk written in pure JavaScript. SqueakJS runs entirely in theWeb browser with a virtual file system that can be directed to a server or client-side storage. Our implementation is notable for simplicity and performance gained through adaptation to the host object memory and deployment leverage gained through the Lively Web development environment. We present several novel techniques as well as performance measurements for the resulting virtual machine. Much of this experience is potentially relevant to preserving other dynamic language systems and making them available in a browser-based environment.
We present object versioning as a generic approach to preserve access to previous development and application states. Version-aware references can manage the modifications made to the target object and record versions as desired. Such references can be provided without modifications to the virtual machine. We used proxies to implement the proposed concepts and demonstrate the Lively Kernel running on top of this object versioning layer. This enables Lively users to undo the effects of direct manipulation and other programming actions.
Biologists often pose queries to search engines and biological databases to obtain answers related to ongoing experiments. This is known to be a time consuming, and sometimes frustrating, task in which more than one query is posed and many databases are consulted to come to possible answers for a single fact. Question answering comes as an alternative to this process by allowing queries to be posed as questions, by integrating various resources of different nature and by returning an exact answer to the user. We have surveyed the current solutions on question answering for Biology, present an overview on the methods which are usually employed and give insights on how to boost performance of systems in this domain. (C) 2014 Elsevier Inc. All rights reserved.
Network Topology Discovery and Inventory Listing are two of the primary features of modern network monitoring systems (NMS). Current NMSs rely heavily on active scanning techniques for discovering and mapping network information. Although this approach works, it introduces some major drawbacks such as the performance impact it can exact, specially in larger network environments. As a consequence, scans are often run less frequently which can result in stale information being presented and used by the network monitoring system. Alternatively, some NMSs rely on their agents being deployed on the hosts they monitor. In this article, we present a new approach to Network Topology Discovery and Network Inventory Listing using only passive monitoring and scanning techniques. The proposed techniques rely solely on the event logs produced by the hosts and network devices present within a network. Finally, we discuss some of the advantages and disadvantages of our approach.
3D point clouds are a digital representation of our world and used in a variety of applications. They are captured with LiDAR or derived by image-matching approaches to get surface information of objects, e.g., indoor scenes, buildings, infrastructures, cities, and landscapes. We present novel interaction and visualization techniques for heterogeneous, time variant, and semantically rich 3D point clouds. Interactive and view-dependent see-through lenses are introduced as exploration tools to enhance recognition of objects, semantics, and temporal changes within 3D point cloud depictions. We also develop filtering and highlighting techniques that are used to dissolve occlusion to give context-specific insights. All techniques can be combined with an out-of-core real-time rendering system for massive 3D point clouds. We have evaluated the presented approach with 3D point clouds from different application domains. The results show the usability and how different visualization and exploration tasks can be improved for a variety of domain-specific applications.
We contribute to the theoretical understanding of randomized search heuristics by investigating their optimization behavior on satisfiable random k-satisfiability instances both in the planted solution model and the uniform model conditional on satisfiability. Denoting the number of variables by n, our main technical result is that the simple () evolutionary algorithm with high probability finds a satisfying assignment in time when the clause-variable density is at least logarithmic. For low density instances, evolutionary algorithms seem to be less effective, and all we can show is a subexponential upper bound on the runtime for densities below . We complement these mathematical results with numerical experiments on a broader density spectrum. They indicate that, indeed, the () EA is less efficient on lower densities. Our experiments also suggest that the implicit constants hidden in our main runtime guarantee are low. Our main result extends and considerably improves the result obtained by Sutton and Neumann (Lect Notes Comput Sci 8672:942-951, 2014) in terms of runtime, minimum density, and clause length. These improvements are made possible by establishing a close fitness-distance correlation in certain parts of the search space. This approach might be of independent interest and could be useful for other average-case analyses of randomized search heuristics. While the notion of a fitness-distance correlation has been around for a long time, to the best of our knowledge, this is the first time that fitness-distance correlation is explicitly used to rigorously prove a performance statement for an evolutionary algorithm.
Currently available wearables are usually based on a single sensor node with integrated capabilities for classifying different activities. The next generation of cooperative wearables could be able to identify not only activities, but also to evaluate them qualitatively using the data of several sensor nodes attached to the body, to provide detailed feedback for the improvement of the execution. Especially within the application domains of sports and health-care, such immediate feedback to the execution of body movements is crucial for (re-) learning and improving motor skills. To enable such systems for a broad range of activities, generalized approaches for human motion assessment within sensor networks are required. In this paper, we present a generalized trainable activity assessment chain (AAC) for the online assessment of periodic human activity within a wireless body area network. AAC evaluates the execution of separate movements of a prior trained activity on a fine-grained quality scale. We connect qualitative assessment with human knowledge by projecting the AAC on the hierarchical decomposition of motion performed by the human body as well as establishing the assessment on a kinematic evaluation of biomechanically distinct motion fragments. We evaluate AAC in a real-world setting and show that AAC successfully delimits the movements of correctly performed activity from faulty executions and provides detailed reasons for the activity assessment.
Distributed applications are hard to debug because timing-dependent network communication is a source of non-deterministic behavior. Current approaches to debug non deterministic failures include post-mortem debugging as well as record and replay. However, the first impairs system performance to gather data, whereas the latter requires developers to understand the timing-dependent communication at a lower level of abstraction than they develop at. Furthermore, both approaches require intrusive core library modifications to gather data from live systems. In this paper, we present the Peek-At-Talk debugger for investigating non-deterministic failures with low overhead in a systematic, top-down method, with a particular focus on tool-building issues in the following areas: First, we show how our debugging framework Path Tools guides developers from failures to their root causes and gathers run-time data with low overhead. Second, we present Peek-At-Talk, an extension to our Path Tools framework to record non-deterministic communication and refine behavioral data that connects source code with network events. Finally, we scope changes to the core library to record network communication without impacting other network applications.
Linked Data on the Web represents an immense source of knowledge suitable to be automatically processed and queried. In this respect, there are different approaches for Linked Data querying that differ on the degree of centralization adopted. On one hand, the SPARQL query language, originally defined for querying single datasets, has been enhanced with features to query federations of datasets; however, this attempt is not sufficient to cope with the distributed nature of data sources available as Linked Data. On the other hand, extensions or variations of SPARQL aim to find trade-offs between centralized and fully distributed querying. The idea is to partially move the computational load from the servers to the clients. Despite the variety and the relative merits of these approaches, as of today, there is no standard language for querying Linked Data on theWeb. A specific requirement for such a language to capture the distributed, graph-like nature of Linked Data sources on the Web is a support of graph navigation. Recently, SPARQL has been extended with a navigational feature called property paths (PPs). However, the semantics of SPARQL restricts the scope of navigation via PPs to single RDF graphs. This restriction limits the applicability of PPs for querying distributed Linked Data sources on the Web. To fill this gap, in this paper we provide formal foundations for evaluating PPs on the Web, thus contributing to the definition of a query language for Linked Data. We first introduce a family of reachability-based query semantics for PPs that distinguish between navigation on the Web and navigation at the data level. Thereafter, we consider another, alternative query semantics that couples Web graph navigation and data level navigation; we call it context-based semantics. Given these semantics, we find that for some PP-based SPARQL queries a complete evaluation on the Web is not possible. To study this phenomenon we introduce a notion of Web-safeness of queries, and prove a decidable syntactic property that enables systems to identify queries that areWeb-safe. In addition to establishing these formal foundations, we conducted an experimental comparison of the context-based semantics and a reachability- based semantics. Our experiments show that when evaluating a PP-based query under the context-based semantics one experiences a significantly smaller number of dereferencing operations, but the computed query result may contain less solutions.
Securing e-prescription from medical identity theft using steganography and antiphishing techniques
(2017)
Drug prescription is among the health care process that usually makes references to the patients’ medical and insurance information among other personal data, because this information is very vital and delicate, it should be adequately protected from identity thieves. This article aims at securing Electronic Prescription (EP) in order to minimize patient’s data theft and foster patients’ trust of EP system.
This paper presents a steganography and antiphishing technique for preventing medical identity theft in EP. The proposed EP system design focused on the security features in the prescriber and dispensers’ modules of EP by ensuring the prescriber sends the prescription of the patient in a safe manner and to the right dispenser without the interference of fake third parties. Hexadecimal steganography image system is used to cover and secure the
sent prescription details. Malicious electronic dispensing system is prevented through an authentication technique where a dispenser uses a captcha together with a one-time password, and the web server encrypted token for prescriber’s device authentication. The steganography system is evaluated using Peak Signal to Noise Ratio (PSNR).
The system implementation results showed that steganography
and antiphishing techniques are capable of providing a secure EP systems.
Creation, collection and retention of knowledge in digital communities is an activity that currently requires being explicitly targeted as a secure method of keeping intellectual capital growing in the digital era. In particular, we consider it relevant to analyze and evaluate the empathetic cognitive personalities and behaviors that individuals now have with the change from face-to-face communication (F2F) to computer-mediated communication (CMC) online. This document proposes a cyber-humanistic approach to enhance the traditional SECI knowledge management model. A cognitive perception is added to its cyclical process following design thinking interaction, exemplary for improvement of the method in which knowledge is continuously created, converted and shared. In building a cognitive-centered model, we specifically focus on the effective identification and response to cognitive stimulation of individuals, as they are the intellectual generators and multiplicators of knowledge in the online environment. Our target is to identify how geographically distributed-digital-organizations should align the individual's cognitive abilities to promote iteration and improve interaction as a reliable stimulant of collective intelligence. The new model focuses on analyzing the four different stages of knowledge processing, where individuals with sympathetic cognitive personalities can significantly boost knowledge creation in a virtual social system. For organizations, this means that multidisciplinary individuals can maximize their extensive potential, by externalizing their knowledge in the correct stage of the knowledge creation process, and by collaborating with their appropriate sympathetically cognitive remote peers.
After almost two decades of development, modern Security Information and Event Management (SIEM) systems still face issues with normalisation of heterogeneous data sources, high number of false positive alerts and long analysis times, especially in large-scale networks with high volumes of security events. In this paper, we present our own prototype of SIEM system, which is capable of dealing with these issues. For efficient data processing, our system employs in-memory data storage (SAP HANA) and our own technologies from the previous work, such as the Object Log Format (OLF) and high-speed event normalisation. We analyse normalised data using a combination of three different approaches for security analysis: misuse detection, query-based analytics, and anomaly detection. Compared to the previous work, we have significantly improved our unsupervised anomaly detection algorithms. Most importantly, we have developed a novel hybrid outlier detection algorithm that returns ranked clusters of anomalies. It lets an operator of a SIEM system to concentrate on the several top-ranked anomalies, instead of digging through an unsorted bundle of suspicious events. We propose to use anomaly detection in a combination with signatures and queries, applied on the same data, rather than as a full replacement for misuse detection. In this case, the majority of attacks will be captured with misuse detection, whereas anomaly detection will highlight previously unknown behaviour or attacks. We also propose that only the most suspicious event clusters need to be checked by an operator, whereas other anomalies, including false positive alerts, do not need to be explicitly checked if they have a lower ranking. We have proved our concepts and algorithms on a dataset of 160 million events from a network segment of a big multinational company and suggest that our approach and methods are highly relevant for modern SIEM systems.
Applications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk and resources of the data center without affecting the service quality of applications.
Background Heart failure (HF) is a complex, chronic condition that is associated with debilitating symptoms, all of which necessitate close follow-up by health care providers. Lack of disease monitoring may result in increased mortality and more frequent hospital readmissions for decompensated HF. Remote patient management (RPM) in this patient population may help to detect early signs and symptoms of cardiac decompensation, thus enabling a prompt initiation of the appropriate treatment and care before a manifestation of HF decompensation. Objective The objective of the present article is to describe the design of a new trial investigating the impact of RPM on unplanned cardiovascular hospitalisations and mortality in HF patients. Methods The TIM-HF2 trial is designed as a prospective, randomised, controlled, parallel group, open (with randomisation concealment), multicentre trial with pragmatic elements introduced for data collection. Eligible patients with HF are randomised (1:1) to either RPM + usual care or to usual care only and are followed for 12 months. The primary outcome is the percentage of days lost due to unplanned cardiovascular hospitalisations or all-cause death. The main secondary outcomes are all-cause and cardiovascular mortality. Conclusion The TIM-HF2 trial will provide important prospective data on the potential beneficial effect of telemedical monitoring and RPM on unplanned cardiovascular hospitalisations and mortality in HF patients.
Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.
In this paper, we introduce an alternative approach to Temporal Answer Set Programming that relies on a variation of Temporal Equilibrium Logic (TEL) for finite traces. This approach allows us to even out the expressiveness of TEL over infinite traces with the computational capacity of (incremental) Answer Set Programming (ASP). Also, we argue that finite traces are more natural when reasoning about action and change. As a result, our approach is readily implementable via multi-shot ASP systems and benefits from an extension of ASP's full-fledged input language with temporal operators. This includes future as well as past operators whose combination offers a rich temporal modeling language. For computation, we identify the class of temporal logic programs and prove that it constitutes a normal form for our approach. Finally, we outline two implementations, a generic one and an extension of the ASP system clingo.
Under consideration for publication in Theory and Practice of Logic Programming (TPLP)
We introduce the asprilo1 framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return, asprilo allows users to study alternative solutions as regards effectiveness and scalability. Although asprilo relies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely, asprilo consists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites.
Given a query record, record matching is the problem of finding database records that represent the same real-world object. In the easiest scenario, a database record is completely identical to the query. However, in most cases, problems do arise, for instance, as a result of data errors or data integrated from multiple sources or received from restrictive form fields. These problems are usually difficult, because they require a variety of actions, including field segmentation, decoding of values, and similarity comparisons, each requiring some domain knowledge. In this article, we study the problem of matching records that contain address information, including attributes such as Street-address and City. To facilitate this matching process, we propose a domain-specific procedure to, first, enrich each record with a more complete representation of the address information through geocoding and reverse-geocoding and, second, to select the best similarity measure per each address attribute that will finally help the classifier to achieve the best f-measure. We report on our experience in selecting geocoding services and discovering similarity measures for a concrete but common industry use-case.
Graphs are ubiquitous in computer science. Moreover, in various application fields, graphs are equipped with attributes to express additional information such as names of entities or weights of relationships. Due to the pervasiveness of attributed graphs, it is highly important to have the means to express properties on attributed graphs to strengthen modeling capabilities and to enable analysis. Firstly, we introduce a new logic of attributed graph properties, where the graph part and attribution part are neatly separated. The graph part is equivalent to first-order logic on graphs as introduced by Courcelle. It employs graph morphisms to allow the specification of complex graph patterns. The attribution part is added to this graph part by reverting to the symbolic approach to graph attribution, where attributes are represented symbolically by variables whose possible values are specified by a set of constraints making use of algebraic specifications. Secondly, we extend our refutationally complete tableau-based reasoning method as well as our symbolic model generation approach for graph properties to attributed graph properties. Due to the new logic mentioned above, neatly separating the graph and attribution parts, and the categorical constructions employed only on a more abstract level, we can leave the graph part of the algorithms seemingly unchanged. For the integration of the attribution part into the algorithms, we use an oracle, allowing for flexible adoption of different available SMT solvers in the actual implementation. Finally, our automated reasoning approach for attributed graph properties is implemented in the tool AutoGraph integrating in particular the SMT solver Z3 for the attribute part of the properties. We motivate and illustrate our work with a particular application scenario on graph database query validation.
Random walks are frequently used in randomized algorithms. We study a derandomized variant of a random walk on graphs called the rotor-router model. In this model, instead of distributing tokens randomly, each vertex serves its neighbors in a fixed deterministic order. For most setups, both processes behave in a remarkably similar way: Starting with the same initial configuration, the number of tokens in the rotor-router model deviates only slightly from the expected number of tokens on the corresponding vertex in the random walk model. The maximal difference over all vertices and all times is called single vertex discrepancy. Cooper and Spencer [Combin. Probab. Comput., 15 (2006), pp. 815-822] showed that on Z(d), the single vertex discrepancy is only a constant c(d). Other authors also determined the precise value of c(d) for d = 1, 2. All of these results, however, assume that initially all tokens are only placed on one partition of the bipartite graph Z(d). We show that this assumption is crucial by proving that, otherwise, the single vertex discrepancy can become arbitrarily large. For all dimensions d >= 1 and arbitrary discrepancies l >= 0, we construct configurations that reach a discrepancy of at least l.
Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing, and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM (Long Short-Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long-term visual-language interactions by making use of history and future context information at high-level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different ways to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale, and vertical mirror are proposed to prevent over-fitting in training deep models. To understand how our models "translate" image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO, and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model). Our experiments also prove that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate the performance of transfer learning of the Bi-LSTM model significantly outperforms previous methods on the Pascal1K dataset.
Wendepunkt für Gesundheit
(2019)
Ubiquitous computing has proven its relevance and efficiency in improving the user experience across a myriad of situations. It is now the ineluctable solution to keep pace with the ever-changing environments in which current systems operate. Despite the achievements of ubiquitous computing, this discipline is still overlooked in business process management. This is surprising, since many of today’s challenges, in this domain, can be addressed by methods and techniques from ubiquitous computing, for instance user context and dynamic aspects of resource locations. This paper takes a first step to integrate methods and techniques from ubiquitous computing in business process management. To do so, we propose discovering commute patterns via process mining. Through our proposition, we can deduce the users’ significant locations, routes, travel times and travel modes. This information can be a stepping-stone toward helping the business process management community embrace the latest achievements in ubiquitous computing, mainly in location-based service. To corroborate our claims, a user study was conducted. The significant places, routes, travel modes and commuting times of our test subjects were inferred with high accuracies. All in all, ubiquitous computing can enrich the processes with new capabilities that go beyond what has been established in business process management so far.
Kyub
(2019)
We present an interactive editing system for laser cutting called kyub. Kyub allows users to create models efficiently in 3D, which it then unfolds into the 2D plates laser cutters expect. Unlike earlier systems, such as FlatFitFab, kyub affords construction based on closed box structures, which allows users to turn very thin material, such as 4mm plywood, into objects capable of withstanding large forces, such as chairs users can actually sit on. To afford such sturdy construction, every kyub project begins with a simple finger-joint "boxel"-a structure we found to be capable of withstanding over 500kg of load. Users then extend their model by attaching additional boxels. Boxels merge automatically, resulting in larger, yet equally strong structures. While the concept of stacking boxels allows kyub to offer the strong affordance and ease of use of a voxel-based editor, boxels are not confined to a grid and readily combine with kuyb's various geometry deformation tools. In our technical evaluation, objects built with kyub withstood hundreds of kilograms of loads. In our user study, non-engineers rated the learnability of kyub 6.1/7.
In this paper, we establish the underlying foundations of mechanisms that are composed of cell structures-known as metamaterial mechanisms. Such metamaterial mechanisms were previously shown to implement complete mechanisms in the cell structure of a 3D printed material, without the need for assembly. However, their design is highly challenging. A mechanism consists of many cells that are interconnected and impose constraints on each other. This leads to unobvious and non-linear behavior of the mechanism, which impedes user design. In this work, we investigate the underlying topological constraints of such cell structures and their influence on the resulting mechanism. Based on these findings, we contribute a computational design tool that automatically creates a metamaterial mechanism from user-defined motion paths. This tool is only feasible because our novel abstract representation of the global constraints highly reduces the search space of possible cell arrangements.
Selfish Network Creation focuses on modeling real world networks from a game-theoretic point of view. One of the classic models by Fabrikant et al. (2003) is the network creation game, where agents correspond to nodes in a network which buy incident edges for the price of alpha per edge to minimize their total distance to all other nodes. The model is well-studied but still has intriguing open problems. The most famous conjectures state that the price of anarchy is constant for all alpha and that for alpha >= n all equilibrium networks are trees. We introduce a novel technique for analyzing stable networks for high edge-price alpha and employ it to improve on the best known bound for the latter conjecture. In particular we show that for alpha > 4n - 13 all equilibrium networks must be trees, which implies a constant price of anarchy for this range of alpha. Moreover, we also improve the constant upper bound on the price of anarchy for equilibrium trees.
Duplicate detection algorithms produce clusters of database records, each cluster representing a single real-world entity. As most of these algorithms use pairwise comparisons, the resulting (transitive) clusters can be inconsistent: Not all records within a cluster are sufficiently similar to be classified as duplicate. Thus, one of many subsequent clustering algorithms can further improve the result. <br /> We explain in detail, compare, and evaluate many of these algorithms and introduce three new clustering algorithms in the specific context of duplicate detection. Two of our three new algorithms use the structure of the input graph to create consistent clusters. Our third algorithm, and many other clustering algorithms, focus on the edge weights, instead. For evaluation, in contrast to related work, we experiment on true real-world datasets, and in addition examine in great detail various pair-selection strategies used in practice. While no overall winner emerges, we are able to identify best approaches for different situations. In scenarios with larger clusters, our proposed algorithm, Extended Maximum Clique Clustering (EMCC), and Markov Clustering show the best results. EMCC especially outperforms Markov Clustering regarding the precision of the results and additionally has the advantage that it can also be used in scenarios where edge weights are not available.
3D point cloud technology facilitates the automated and highly detailed acquisition of real-world environments such as assets, sites, and countries. We present a web-based system for the interactive exploration and inspection of arbitrary large 3D point clouds. Our approach is able to render 3D point clouds with billions of points using spatial data structures and level-of-detail representations. Point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering, e.g., based on semantics. A set of interaction techniques allows users to collaboratively work with the data (e.g., measuring distances and annotating). Additional value is provided by the system’s ability to display additional, context-providing geodata alongside 3D point clouds and to integrate processing and analysis operations. We have evaluated the presented techniques and in case studies and with different data sets from aerial, mobile, and terrestrial acquisition with up to 120 billion points to show their practicality and feasibility.
Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels
(2019)
Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.
We elaborate on the possibilities and needs to integrate design thinking into requirements engineering, drawing from our research and project experiences. We suggest three approaches for tailoring and integrating design thinking and requirements engineering with complementary synergies and point at open challenges for research and practice.
From face to face
(2019)
Despite advances in the conceptualisation of facial mimicry, its role in the processing of social information is a matter of debate. In the present study, we investigated the relationship between mimicry and cognitive and emotional empathy. To assess mimicry, facial electromyography was recorded for 70 participants while they completed the Multifaceted Empathy Test, which presents complex context-embedded emotional expressions. As predicted, inter-individual differences in emotional and cognitive empathy were associated with the level of facial mimicry. For positive emotions, the intensity of the mimicry response scaled with the level of state emotional empathy. Mimicry was stronger for the emotional empathy task compared to the cognitive empathy task. The specific empathy condition could be successfully detected from facial muscle activity at the level of single individuals using machine learning techniques. These results support the view that mimicry occurs depending on the social context as a tool to affiliate and it is involved in cognitive as well as emotional empathy.
Indexes are essential for the efficient processing of database workloads. Proposed solutions for the relevant and challenging index selection problem range from metadata-based simple heuristics, over sophisticated multi-step algorithms, to approaches that yield optimal results. The main challenges are (i) to accurately determine the effect of an index on the workload cost while considering the interaction of indexes and (ii) a large number of possible combinations resulting from workloads containing many queries and massive schemata with possibly thousands of attributes. <br /> In this work, we describe and analyze eight index selection algorithms that are based on different concepts and compare them along different dimensions, such as solution quality, runtime, multi-column support, solution granularity, and complexity. In particular, we analyze the solutions of the algorithms for the challenging analytical Join Order, TPC-H, and TPC-DS benchmarks. Afterward, we assess strengths and weaknesses, infer insights for index selection in general and each approach individually, before we give recommendations on when to use which approach.
MDedup
(2020)
Duplicate detection is an integral part of data cleaning and serves to identify multiple representations of same real-world entities in (relational) datasets. Existing duplicate detection approaches are effective, but they are also hard to parameterize or require a lot of pre-labeled training data. Both parameterization and pre-labeling are at least domain-specific if not dataset-specific, which is a problem if a new dataset needs to be cleaned.
For this reason, we propose a novel, rule-based and fully automatic duplicate detection approach that is based on matching dependencies (MDs). Our system uses automatically discovered MDs, various dataset features, and known gold standards to train a model that selects MDs as duplicate detection rules. Once trained, the model can select useful MDs for duplicate detection on any new dataset. To increase the generally low recall of MD-based data cleaning approaches, we propose an additional boosting step. Our experiments show that this approach reaches up to 94% F-measure and 100% precision on our evaluation datasets, which are good numbers considering that the system does not require domain or target data-specific configuration.
In many revenue management applications risk-averse decision-making is crucial. In dynamic settings, however, it is challenging to find the right balance between maximizing expected rewards and minimizing various kinds of risk. In existing approaches utility functions, chance constraints, or (conditional) value at risk considerations are used to influence the distribution of rewards in a preferred way. Nevertheless, common techniques are not flexible enough and typically numerically complex. In our model, we exploit the fact that a distribution is characterized by its mean and higher moments. We present a multi-valued dynamic programming heuristic to compute risk-sensitive feedback policies that are able to directly control the moments of future rewards. Our approach is based on recursive formulations of higher moments and does not require an extension of the state space. Finally, we propose a self-tuning algorithm, which allows to identify feedback policies that approximate predetermined (risk-sensitive) target distributions. We illustrate the effectiveness and the flexibility of our approach for different dynamic pricing scenarios. (C) 2020 Elsevier Ltd. All rights reserved.
Data errors represent a major issue in most application workflows. Before any important task can take place, a certain data quality has to be guaranteed by eliminating a number of different errors that may appear in data. Typically, most of these errors are fixed with data preparation methods, such as whitespace removal. However, the particular error of duplicate records, where multiple records refer to the same entity, is usually eliminated independently with specialized techniques. Our work is the first to bring these two areas together by applying data preparation operations under a systematic approach prior to performing duplicate detection. <br /> Our process workflow can be summarized as follows: It begins with the user providing as input a sample of the gold standard, the actual dataset, and optionally some constraints to domain-specific data preparations, such as address normalization. The preparation selection operates in two consecutive phases. First, to vastly reduce the search space of ineffective data preparations, decisions are made based on the improvement or worsening of pair similarities. Second, using the remaining data preparations an iterative leave-one-out classification process removes preparations one by one and determines the redundant preparations based on the achieved area under the precision-recall curve (AUC-PR). Using this workflow, we manage to improve the results of duplicate detection up to 19% in AUC-PR.
Challenges for self-driving database systems, which tune their physical design and configuration autonomously, are manifold: Such systems have to anticipate future workloads, find robust configurations efficiently, and incorporate knowledge gained by previous actions into later decisions. We present a component-based framework for self-driving database systems that enables database integration and development of self-managing functionality with low overhead by relying on separation of concerns. By keeping the components of the framework reusable and exchangeable, experiments are simplified, which promotes further research in that area. Moreover, to optimize multiple mutually dependent features, e.g., index selection and compression configurations, we propose a linear programming (LP) based algorithm to derive an efficient tuning order automatically. Afterwards, we demonstrate the applicability and scalability of our approach with reproducible examples.
Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term "AI" and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a "Naturalness Hypothesis" for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models.