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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.
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.
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.
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.
Every year, the Hasso Plattner Institute (HPI) invites guests from industry and academia to a collaborative scientific workshop on the topic “Operating the Cloud”. Our goal is to provide a forum for the exchange of knowledge and experience between industry and academia. Hence, HPI’s Future SOC Lab is the adequate environment to host this event which is also supported by BITKOM.
On the occasion of this workshop we called for submissions of research papers and practitioners’ reports. “Operating the Cloud” aims to be a platform for productive discussions of innovative ideas, visions, and upcoming technologies in the field of cloud operation and administration.
In this workshop proceedings the results of the second HPI cloud symposium "Operating the Cloud" 2014 are published. We thank the authors for exciting presentations and insights into their current work and research. Moreover, we look forward to more interesting submissions for the upcoming symposium in 2015.
Traditionally, business process management systems only execute and monitor business process instances based on events that originate from the process engine itself or from connected client applications. However, environmental events may also influence business process execution. Recent research shows how the technological improvements in both areas, business process management and complex event processing, can be combined and harmonized. The series of technical reports included in this collection provides insights in that combination with respect to technical feasibility and improvements based on real-world use cases originating from the EU-funded GET Service project – a project targeting transport optimization and green-house gas reduction in the logistics domain. Each report is complemented by a working prototype.
This collection introduces six use cases from the logistics domain. Multiple transports – each being a single process instance – may be affected by the same events at the same point in time because of (partly) using the same transportation route, transportation vehicle or transportation mode (e.g. containers from multiple process instances on the same ship) such that these instances can be (partly) treated as batch. Thus, the first use case shows the influence of events to process instances processed in a batch. The case of sharing the entire route may be, for instance, due to origin from the same business process (e.g. transport three containers, where each is treated as single process instance because of being transported on three trucks) resulting in multi-instance process executions. The second use case shows how to handle monitoring and progress calculation in this context. Crucial to transportation processes are frequent changes of deadlines. The third use case shows how to deal with such frequent process changes in terms of propagating the changes along and beyond the process scope to identify probable deadline violations. While monitoring transport processes, disruptions may be detected which introduce some delay. Use case four shows how to propagate such delay in a non-linear fashion along the process instance to predict the end time of the instance. Non-linearity is crucial in logistics because of buffer times and missed connection on intermodal transports (a one-hour delay may result in a missed ship which is not going every hour). Finally, use cases five and six show the utilization of location-based process monitoring. Use case five enriches transport processes with real-time route and traffic event information to improve monitoring and planning capabilities. Use case six shows the inclusion of spatio-temporal events on the example of unexpected weather events.
Design and implementation of service-oriented architectures impose numerous research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Service-oriented Systems Engineering represents a symbiosis of best practices in object orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns. Service-oriented Systems Engineering denotes a current research topic in the field of IT-Systems Engineering with high potential in academic research and industrial application.
The annual Ph.D. Retreat of the Research School provides all members the opportunity to present the current state of their research and to give an outline of prospective Ph.D. projects. Due to the interdisciplinary structure of the Research School, this technical report covers a wide range of research topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment.
Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application.
Commonly used technologies, such as J2EE and .NET, form de facto standards for the realization of complex distributed systems. Evolution of component systems has lead to web services and service-based architectures. This has been manifested in a multitude of industry standards and initiatives such as XML, WSDL UDDI, SOAP, etc. All these achievements lead to a new and promising paradigm in IT systems engineering which proposes to design complex software solutions as collaboration of contractually defined software services.
Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns.
The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the Research Scholl, this technical report covers a wide range of research topics. These include but are not limited to: Self-Adaptive Service-Oriented Systems, Operating System Support for Service-Oriented Systems, Architecture and Modeling of Service-Oriented Systems, Adaptive Process Management, Services Composition and Workflow Planning, Security Engineering of Service-Based IT Systems, Quantitative Analysis and Optimization of Service-Oriented Systems, Service-Oriented Systems in 3D Computer Graphics sowie Service-Oriented Geoinformatics.
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.
Parts without a whole?
(2015)
This explorative study gives a descriptive overview of what organizations do and experience when they say they practice design thinking. It looks at how the concept has been appropriated in organizations and also describes patterns of design thinking adoption. The authors use a mixed-method research design fed by two sources: questionnaire data and semi-structured personal expert interviews. The study proceeds in six parts: (1) design thinking¹s entry points into organizations; (2) understandings of the descriptor; (3) its fields of application and organizational localization; (4) its perceived impact; (5) reasons for its discontinuation or failure; and (6) attempts to measure its success. In conclusion the report challenges managers to be more conscious of their current design thinking practice. The authors suggest a co-evolution of the concept¹s introduction with innovation capability building and the respective changes in leadership approaches. It is argued that this might help in unfolding design thinking¹s hidden potentials as well as preventing unintended side-effects such as discontented teams or the dwindling authority of managers.
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.
An increasing demand on functionality and flexibility leads to an integration of beforehand isolated system solutions building a so-called System of Systems (SoS). Furthermore, the overall SoS should be adaptive to react on changing requirements and environmental conditions. Due SoS are composed of different independent systems that may join or leave the overall SoS at arbitrary point in times, the SoS structure varies during the systems lifetime and the overall SoS behavior emerges from the capabilities of the contained subsystems. In such complex system ensembles new demands of understanding the interaction among subsystems, the coupling of shared system knowledge and the influence of local adaptation strategies to the overall resulting system behavior arise. In this report, we formulate research questions with the focus of modeling interactions between system parts inside a SoS. Furthermore, we define our notion of important system types and terms by retrieving the current state of the art from literature. Having a common understanding of SoS, we discuss a set of typical SoS characteristics and derive general requirements for a collaboration modeling language. Additionally, we retrieve a broad spectrum of real scenarios and frameworks from literature and discuss how these scenarios cope with different characteristics of SoS. Finally, we discuss the state of the art for existing modeling languages that cope with collaborations for different system types such as SoS.
Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during process execution. Aiming at a better process understanding and improvement, this event data can be used to analyze processes using process mining techniques. Process models can be automatically discovered and the execution can be checked for conformance to specified behavior. Moreover, existing process models can be enhanced and annotated with valuable information, for example for performance analysis. While the maturity of process mining algorithms is increasing and more tools are entering the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Mapping the recorded events to activities of a given process model is essential for conformance checking, annotation and understanding of process discovery results. Current approaches try to abstract from events in an automated way that does not capture the required domain knowledge to fit business activities. Such techniques can be a good way to quickly reduce complexity in process discovery. Yet, they fail to enable techniques like conformance checking or model annotation, and potentially create misleading process discovery results by not using the known business terminology.
In this thesis, we develop approaches that abstract an event log to the same level that is needed by the business. Typically, this abstraction level is defined by a given process model. Thus, the goal of this thesis is to match events from an event log to activities in a given process model. To accomplish this goal, behavioral and linguistic aspects of process models and event logs as well as domain knowledge captured in existing process documentation are taken into account to build semiautomatic matching approaches. The approaches establish a pre--processing for every available process mining technique that produces or annotates a process model, thereby reducing the manual effort for process analysts. While each of the presented approaches can be used in isolation, we also introduce a general framework for the integration of different matching approaches.
The approaches have been evaluated in case studies with industry and using a large industry process model collection and simulated event logs. The evaluation demonstrates the effectiveness and efficiency of the approaches and their robustness towards nonconforming execution logs.
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.
Graph transformation systems are a powerful formal model to capture model transformations or systems with infinite state space, among others. However, this expressive power comes at the cost of rather limited automated analysis capabilities. The general case of unbounded many initial graphs or infinite state spaces is only supported by approaches with rather limited scalability or expressiveness. In this report we improve an existing approach for the automated verification of inductive invariants for graph transformation systems. By employing partial negative application conditions to represent and check many alternative conditions in a more compact manner, we can check examples with rules and constraints of substantially higher complexity. We also substantially extend the expressive power by supporting more complex negative application conditions and provide higher accuracy by employing advanced implication checks. The improvements are evaluated and compared with another applicable tool by considering three case studies.
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.
HPI Future SOC Lab
(2015)
Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie.
Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien.
In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2015 vorgestellt. Ausgewählte Projekte stellten ihre Ergebnisse am 15. April 2015 und 4. November 2015 im Rahmen der Future SOC Lab Tag Veranstaltungen vor.
Bei der Erstellung von Visualisierungen gibt es im Wesentlichen zwei Ansätze. Zum einen können mit geringem Aufwand schnell Standarddiagramme erstellt werden. Zum anderen gibt es die Möglichkeit, individuelle und interaktive Visualisierungen zu programmieren. Dies ist jedoch mit einem deutlich höheren Aufwand verbunden.
Flower ermöglicht eine schnelle Erstellung individueller und interaktiver Visualisierungen, indem es den Entwicklungssprozess stark vereinfacht und die Nutzer bei den einzelnen Aktivitäten wie dem Import und der Aufbereitung von Daten, deren Abbildung auf visuelle Elemente sowie der Integration von Interaktivität direkt unterstützt.
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.
Graph databases provide a natural way of storing and querying graph data. In contrast to relational databases, queries over graph databases enable to refer directly to the graph structure of such graph data. For example, graph pattern matching can be employed to formulate queries over graph data.
However, as for relational databases running complex queries can be very time-consuming and ruin the interactivity with the database. One possible approach to deal with this performance issue is to employ database views that consist of pre-computed answers to common and often stated queries. But to ensure that database views yield consistent query results in comparison with the data from which they are derived, these database views must be updated before queries make use of these database views. Such a maintenance of database views must be performed efficiently, otherwise the effort to create and maintain views may not pay off in comparison to processing the queries directly on the data from which the database views are derived.
At the time of writing, graph databases do not support database views and are limited to graph indexes that index nodes and edges of the graph data for fast query evaluation, but do not enable to maintain pre-computed answers of complex queries over graph data. Moreover, the maintenance of database views in graph databases becomes even more challenging when negation and recursion have to be supported as in deductive relational databases.
In this technical report, we present an approach for the efficient and scalable incremental graph view maintenance for deductive graph databases. The main concept of our approach is a generalized discrimination network that enables to model nested graph conditions including negative application conditions and recursion, which specify the content of graph views derived from graph data stored by graph databases. The discrimination network enables to automatically derive generic maintenance rules using graph transformations for maintaining graph views in case the graph data from which the graph views are derived change. We evaluate our approach in terms of a case study using multiple data sets derived from open source projects.