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Declarative languages for knowledge representation and reasoning provide constructs to define preference relations over the set of possible interpretations, so that preferred models represent optimal solutions of the encoded problem. We introduce the notion of approximation for replacing preference relations with stronger preference relations, that is, relations comparing more pairs of interpretations. Our aim is to accelerate the computation of a non-empty subset of the optimal solutions by means of highly specialized algorithms. We implement our approach in Answer Set Programming (ASP), where problems involving quantitative and qualitative preference relations can be addressed by ASPRIN, implementing a generic optimization algorithm. Unlike this, chains of approximations allow us to reduce several preference relations to the preference relations associated with ASP’s native weak constraints and heuristic directives. In this way, ASPRIN can now take advantage of several highly optimized algorithms implemented by ASP solvers for computing optimal solutions
In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can provide a cost-effective power supply alternative in cases when connectivity to the national power grid is impeded by factors such as load shedding. RCSMG architectures can be designed to handle communications over a distributed lossy network in order to minimise operation costs. However, due to the unreliable nature of lossy networks communication data can be distorted by noise additions that alter the veracity of the data. In this chapter, we consider cases in which an adversary who is internal to the RCSMG, deliberately distorts communicated data to gain an unfair advantage over the RCSMG’s users. The adversary’s goal is to mask malicious data manipulations as distortions due to additive noise due to communication channel unreliability. Distinguishing malicious data distortions from benign distortions is important in ensuring trustworthiness of the RCSMG. Perturbation data anonymisation algorithms can be used to alter transmitted data to ensure that adversarial manipulation of the data reveals no information that the adversary can take advantage of. However, because existing data perturbation anonymisation algorithms operate by using additive noise to anonymise data, using these algorithms in the RCSMG context is challenging. This is due to the fact that distinguishing benign noise additions from malicious noise additions is a difficult problem. In this chapter, we present a brief survey of cases of privacy violations due to inferences drawn from observed power consumption patterns in RCSMGs centred on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.
An energy consumption model for multiModal wireless sensor networks based on wake-up radio receivers
(2018)
Energy consumption is a major concern in Wireless Sensor Networks. A significant waste of energy occurs due to the idle listening and overhearing problems, which are typically avoided by turning off the radio, while no transmission is ongoing. The classical approach for allowing the reception of messages in such situations is to use a low-duty-cycle protocol, and to turn on the radio periodically, which reduces the idle listening problem, but requires timers and usually unnecessary wakeups. A better solution is to turn on the radio only on demand by using a Wake-up Radio Receiver (WuRx). In this paper, an energy model is presented to estimate the energy saving in various multi-hop network topologies under several use cases, when a WuRx is used instead of a classical low-duty-cycling protocol. The presented model also allows for estimating the benefit of various WuRx properties like using addressing or not.
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.
In university teaching today, it is common practice to record regular lectures and special events such as conferences and speeches. With these recordings, a large fundus of video teaching material can be created quickly and easily. Typically, lectures have a length of about one and a half hours and usually take place once or twice a week based on the credit hours. Depending on the number of lectures and other events recorded, the number of recordings available is increasing rapidly, which means that an appropriate form of provisioning is essential for the students. This is usually done in the form of lecture video platforms. In this work, we have investigated how lecture video platforms and the contained knowledge can be improved and accessed more easily by an increasing number of students. We came up with a multistep process we have applied to our own lecture video web portal that can be applied to other solutions as well.
Business process management is an acknowledged asset for running an organization in a productive and sustainable way. One of the most important aspects of business process management, occurring on a daily basis at all levels, is decision making. In recent years, a number of decision management frameworks have appeared in addition to existing business process management systems. More recently, Decision Model and Notation (DMN) was developed by the OMG consortium with the aim of complementing the widely used Business Process Model and Notation (BPMN). One of the reasons for the emergence of DMN is the increasing interest in the evolving paradigm known as the separation of concerns. This paradigm states that modeling decisions complementary to processes reduces process complexity by externalizing decision logic from process models and importing it into a dedicated decision model. Such an approach increases the agility of model design and execution. This provides organizations with the flexibility to adapt to the ever increasing rapid and dynamic changes in the business ecosystem. The research gap, identified by us, is that the separation of concerns, recommended by DMN, prescribes the externalization of the decision logic of process models in one or more separate decision models, but it does not specify this can be achieved.
The goal of this thesis is to overcome the presented gap by developing a framework for discovering decision models in a semi-automated way from information about existing process decision making. Thus, in this thesis we develop methodologies to extract decision models from: (1) control flow and data of process models that exist in enterprises; and (2) from event logs recorded by enterprise information systems, encapsulating day-to-day operations. Furthermore, we provide an extension of the methodologies to discover decision models from event logs enriched with fuzziness, a tool dealing with partial knowledge of the process execution information. All the proposed techniques are implemented and evaluated in case studies using real-life and synthetic process models and event logs. The evaluation of these case studies shows that the proposed methodologies provide valid and accurate output decision models that can serve as blueprints for executing decisions complementary to process models. Thus, these methodologies have applicability in the real world and they can be used, for example, for compliance checks, among other uses, which could improve the organization's decision making and hence it's overall performance.
Operational decisions in business processes can be modeled by using the Decision Model and Notation (DMN). The complementary use of DMN for decision modeling and of the Business Process Model and Notation (BPMN) for process design realizes the separation of concerns principle. For supporting separation of concerns during the design phase, it is crucial to understand which aspects of decision-making enclosed in a process model should be captured by a dedicated decision model. Whereas existing work focuses on the extraction of decision models from process control flow, the connection of process-related data and decision models is still unexplored. In this paper, we investigate how process-related data used for making decisions can be represented in process models and we distinguish a set of BPMN patterns capturing such information. Then, we provide a formal mapping of the identified BPMN patterns to corresponding DMN models and apply our approach to a real-world healthcare process.
Functional dependencies (FDs) play an important role in maintaining data quality. They can be used to enforce data consistency and to guide repairs over a database. In this work, we investigate the problem of missing values and its impact on FD discovery. When using existing FD discovery algorithms, some genuine FDs could not be detected precisely due to missing values or some non-genuine FDs can be discovered even though they are caused by missing values with a certain NULL semantics. We define a notion of genuineness and propose algorithms to compute the genuineness score of a discovered FD. This can be used to identify the genuine FDs among the set of all valid dependencies that hold on the data. We evaluate the quality of our method over various real-world and semi-synthetic datasets with extensive experiments. The results show that our method performs well for relatively large FD sets and is able to accurately capture genuine FDs.
ASEDS
(2018)
The Massive adoption of social media has provided new ways for individuals to express their opinion and emotion online. In 2016, Facebook introduced a new reactions feature that allows users to express their psychological emotions regarding published contents using so-called Facebook reactions. In this paper, a framework for predicting the distribution of Facebook post reactions is presented. For this purpose, we collected an enormous amount of Facebook posts associated with their reactions labels using the proposed scalable Facebook crawler. The training process utilizes 3 million labeled posts for more than 64,000 unique Facebook pages from diverse categories. The evaluation on standard benchmarks using the proposed features shows promising results compared to previous research. The final model is able to predict the reaction distribution on Facebook posts with a recall score of 0.90 for "Joy" emotion.
Editorial
(2018)
"Never doubt that a small group of thoughtful, committed citizens can change the world; indeed, it's the only thing that ever has. - Margaret Mead."
With the last issue of this year we want to point out directions towards what will come and what challenges and opportunities lie ahead of us. More needed than ever are joint creative efforts to find ways to collaborate and innovate in order to secure the wellbeing of our earth for the next generation to come. We have found ourselves puzzled that we could assemble a sustainability issue without having a call for papers or a special issue. In fact, many of the submissions we currently receive, deal with sustainable, ecological or novel approaches to management and organizations. As creativity and innovation are undisputable necessary ingredients for reaching the sustainable development goals, empirical proof and research in this area are still in their infancy. While the role of design and design thinking has been highlighted before for solving wicked societal problems, a lot more research is needed which creative and innovative ways organisations and societies can take to find solutions to climate change, poverty, hunger and education. We would therefore like to call to you, our readers and writers to tackle these problems with your research.
The first article in this issue addresses one of the above named challenges - the role of innovation for achieving the transition to a low-carbon energy world. In “Innovating for low-carbon energy through hydropower: Enabling a conservation charity's transition to a low-carbon community”, the authors John Gallagher, Paul Coughlan, A. Prysor Williams and Aonghus McNabola look at how an eco-design approach has supported a community transition to low-carbon. They highlight the importance of effective management as well as external collaboration and how the key for success lay in fostering an open environment for creativity and idea sharing. The second article addresses another of the grand challenges, the future of mobility and uses a design-driven approach to develop scenarios for mobility in cities. In “Designing radical innovations of meanings for society: envisioning new scenarios for smart mobility”, the authors Claudio Dell'Era, Naiara Altuna and Roberto Verganti investigate how new meanings can be designed and proposed to society rather than to individuals in the particular context of smart mobility. Through two case studies the authors argue for a multi-level perspective, taking the perspective of the society to solve societal challenges while considering the needs of the individual. The latter is needed because we will not change if our needs are not addressed. Furthermore, the authors find that both, meaning and technology need to be considered to create radical innovation for society. The role of meaning continues in the third article in this issue. The authors Marta Gasparin and William Green show in their article “Reconstructing meaning without redesigning products: The case of the Serie7 chair” how meaning changes over time even though the product remains the same. Through an in-depth retrospective study of the Serie 7 chair the authors investigate the relationship between meaning and the materiality of the object, and show the importance of materiality in constructing product meaning over long periods. Translating this meaning over the course of the innovation process is an important task of management in order to gain buy-in from all involved stakeholders. In the following article “A systematic approach for new technology development by using a biomimicry-based TRIZ contradiction matrix” the authors Byungun Yoon, Chaeguk Lim, Inchae Park and Dooseob Yoon develop a systematic process combining biomimicry and technology-based TRIZ in order to solve technological problems or develop new technologies based on completely new sources or combinations from technology and biology.
In the fifth article in this issue “Innovating via Building Absorptive Capacity: Interactive Effects of Top Management Support of Learning, Employee Learning Orientation, and Decentralization Structure” the authors Li-Yun Sun, Chenwei Li and Yuntao Dong examine the effect of learning-related personal and contextual factors on organizational absorptive capability and subsequent innovative performance. The authors find positive effects as well as a moderation influence of decentralized organizational decision-making structures. In the sixth article “Creativity within boundaries: social identity and the development of new ideas in franchise systems” the authors Fanny Simon, Catherine Allix-Desfautaux, Nabil Khelil and Anne-Laure Le Nadant address the paradox of balancing novelty and conformity for creativity in a franchise system. This research is one of the first we know to explicitly address creativity and innovation in such a rigid and pre-determined system. Using a social identity perspective, they can show that social control, which may be exerted by manipulating group identity, is an efficient lever to increase both the creation and the diffusion of the idea. Furthermore, they show that franchisees who do not conform to the norm of the group are stigmatized and must face pressure from the group to adapt their behaviors. This has important implications for future research. In the following article “Exploring employee interactions and quality of contributions in intra-organisational innovation platforms” the authors Dimitra Chasanidou, Njål Sivertstol and Jarle Hildrum examine the user interactions in an intra-organisational innovation platform, and also address the influence of user interactions for idea development. The authors find that employees communicate through the innovation platform with different interaction, contribution and collaboration types and propose three types of contribution qualities—passive, efficient and balanced contribution. In the eighth article “Ready for Take-off”: How Open Innovation influences startup success” Cristina Marullo, Elena Casprini, Alberto di Minin and Andrea Piccaluga seek to predict new venture success based on factors that can be observed in the pre-startup phase. The authors introduce different variables of founding teams and how these relate to startup success. Building on large-scale dataset of submitted business plans at UC Berkeley, they can show that teams with high skills diversity and past joint experience are a lot better able to prevent the risk of business failure at entry and to adapt the internal resources to market conditions. Furthermore, it is crucial for the team to integrate many external knowledge sources into their process (openness) in order to be successful. The crucial role of knowledge and how it is communicated and shared is the focal point of Natalya Sergeeva's and Anna Trifilova's article on “The role of storytelling in the innovation process”. They authors can show how storytelling has an important role to play when it comes to motivating employees to innovate and promoting innovation success stories inside and outside the organization. The deep human desire to hear and experience stories is also addressed in the last article in this issue “Gamification Approaches to the Early Stage of Innovation” by Rui Patricio, Antonio Moreira and Francesco Zurlo. Using gamification approaches at the early stage of innovation promises to create better team coherence, let employees experience fun and engagement, improve communication and foster knowledge exchange. Using an analytical framework, the authors analyze 15 articles that have looked at gamification in the context of innovation management before. They find that gamification indeed supports firms in becoming better at performing complex innovation tasks and managing innovation challenges. Furthermore, gamification in innovation creates a space for inspiration, improves creativity and the generation of high potential ideas.
Modern routing algorithms reduce query time by depending heavily on preprocessed data. The recently developed Navigation Data Standard (NDS) enforces a separation between algorithms and map data, rendering preprocessing inapplicable. Furthermore, map data is partitioned into tiles with respect to their geographic coordinates. With the limited memory found in portable devices, the number of tiles loaded becomes the major factor for run time. We study routing under these restrictions and present new algorithms as well as empirical evaluations. Our results show that, on average, the most efficient algorithm presented uses more than 20 times fewer tile loads than a normal A*.
Exploring Change
(2018)
Data and metadata in datasets experience many different kinds of change. Values axe inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, etc. In such a dynamic situation, users might have many questions related to changes in the dataset, for instance which parts of the data are trustworthy and which are not? Users will wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How dirty is the data? Is data cleansing required? The fact that data changed can hint at different hidden processes or agendas: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. We show various use cases that benefit from recognizing and exploring such change. We envision a system and methods to interactively explore such change, addressing the variability dimension of big data challenges. To this end, we propose a model to capture change and the process of exploring dynamic data to identify salient changes. We provide exploration primitives along with motivational examples and measures for the volatility of data. We identify technical challenges that need to be addressed to make our vision a reality, and propose directions of future work for the data management community.
We present a system-level synthesis approach for heterogeneous multi-processor on chip, based on Answer Set Programming(ASP). Starting with a high-level description of an application, its timing constraints and the physical constraints of the target device, our goal is to produce the optimal computing infrastructure made of heterogeneous processors, peripherals, memories and communication components. Optimization aims at maximizing speed, while minimizing chip area. Also, a scheduler must be produced that fulfills the real-time requirements of the application. Even though our approach will work for application specific integrated circuits, we have chosen FPGA as target device in this work because of their reconfiguration capabilities which makes it possible to explore several design alternatives. This paper addresses the bottleneck of problem representation size by providing a direct and compact ASP encoding for automatic synthesis that is semantically equivalent to previously established ILP and ASP models. We describe a use-case in which designers specify their applications in C/C++ from which optimum systems can be derived. We demonstrate the superiority of our approach toward existing heuristics and exact methods with synthesis results on a set of realistic case studies. (C) 2018 Elsevier Inc. All rights reserved.
Modern server systems with large NUMA architectures necessitate (i) data being distributed over the available computing nodes and (ii) NUMA-aware query processing to enable effective parallel processing in database systems. As these architectures incur significant latency and throughout penalties for accessing non-local data, queries should be executed as close as possible to the data. To further increase both performance and efficiency, data that is not relevant for the query result should be skipped as early as possible. One way to achieve this goal is horizontal partitioning to improve static partition pruning. As part of our ongoing work on workload-driven partitioning, we have implemented a recent approach called aggressive data skipping and extended it to handle both analytical as well as transactional access patterns. In this paper, we evaluate this approach with the workload and data of a production enterprise system of a Global 2000 company. The results show that over 80% of all tuples can be skipped in average while the resulting partitioning schemata are surprisingly stable over time.
Spatio-temporal data denotes a category of data that contains spatial as well as temporal components. For example, time-series of geo-data, thematic maps that change over time, or tracking data of moving entities can be interpreted as spatio-temporal data.
In today's automated world, an increasing number of data sources exist, which constantly generate spatio-temporal data. This includes for example traffic surveillance systems, which gather movement data about human or vehicle movements, remote-sensing systems, which frequently scan our surroundings and produce digital representations of cities and landscapes, as well as sensor networks in different domains, such as logistics, animal behavior study, or climate research.
For the analysis of spatio-temporal data, in addition to automatic statistical and data mining methods, exploratory analysis methods are employed, which are based on interactive visualization. These analysis methods let users explore a data set by interactively manipulating a visualization, thereby employing the human cognitive system and knowledge of the users to find patterns and gain insight into the data.
This thesis describes a software framework for the visualization of spatio-temporal data, which consists of GPU-based techniques to enable the interactive visualization and exploration of large spatio-temporal data sets. The developed techniques include data management, processing, and rendering, facilitating real-time processing and visualization of large geo-temporal data sets. It includes three main contributions:
- Concept and Implementation of a GPU-Based Visualization Pipeline.
The developed visualization methods are based on the concept of a GPU-based visualization pipeline, in which all steps -- processing, mapping, and rendering -- are implemented on the GPU. With this concept, spatio-temporal data is represented directly in GPU memory, using shader programs to process and filter the data, apply mappings to visual properties, and finally generate the geometric representations for a visualization during the rendering process. Data processing, filtering, and mapping are thereby executed in real-time, enabling dynamic control over the mapping and a visualization process which can be controlled interactively by a user.
- Attributed 3D Trajectory Visualization.
A visualization method has been developed for the interactive exploration of large numbers of 3D movement trajectories. The trajectories are visualized in a virtual geographic environment, supporting basic geometries such as lines, ribbons, spheres, or tubes. Interactive mapping can be applied to visualize the values of per-node or per-trajectory attributes, supporting shape, height, size, color, texturing, and animation as visual properties. Using the dynamic mapping system, several kind of visualization methods have been implemented, such as focus+context visualization of trajectories using interactive density maps, and space-time cube visualization to focus on the temporal aspects of individual movements.
- Geographic Network Visualization.
A method for the interactive exploration of geo-referenced networks has been developed, which enables the visualization of large numbers of nodes and edges in a geographic context. Several geographic environments are supported, such as a 3D globe, as well as 2D maps using different map projections, to enable the analysis of networks in different contexts and scales. Interactive filtering, mapping, and selection can be applied to analyze these geographic networks, and visualization methods for specific types of networks, such as coupled 3D networks or temporal networks have been implemented.
As a demonstration of the developed visualization concepts, interactive visualization tools for two distinct use cases have been developed. The first contains the visualization of attributed 3D movement trajectories of airplanes around an airport. It allows users to explore and analyze the trajectories of approaching and departing aircrafts, which have been recorded over the period of a month. By applying the interactive visualization methods for trajectory visualization and interactive density maps, analysts can derive insight from the data, such as common flight paths, regular and irregular patterns, or uncommon incidents such as missed approaches on the airport.
The second use case involves the visualization of climate networks, which are geographic networks in the climate research domain. They represent the dynamics of the climate system using a network structure that expresses statistical interrelationships between different regions. The interactive tool allows climate analysts to explore these large networks, analyzing the network's structure and relating it to the geographic background. Interactive filtering and selection enables them to find patterns in the climate data and identify e.g. clusters in the networks or flow patterns.
Human actuation
(2018)
Ever since the conception of the virtual reality headset in 1968, many researchers have argued that the next step in virtual reality is to allow users to not only see and hear, but also feel virtual worlds. One approach is to use mechanical equipment to provide haptic feedback, e.g., robotic arms, exoskeletons and motion platforms. However, the size and the weight of such mechanical equipment tends to be proportional to its target’s size and weight, i.e., providing human-scale haptic feedback requires human-scale equipment, often restricting them to arcades and lab environments.
The key idea behind this dissertation is to bypass mechanical equipment by instead leveraging human muscle power. We thus create software systems that orchestrate humans in doing such mechanical labor—this is what we call human actuation. A potential benefit of such systems is that humans are more generic, flexible, and versatile than machines. This brings a wide range of haptic feedback to modern virtual reality systems.
We start with a proof-of-concept system—Haptic Turk, focusing on delivering motion experiences just like a motion platform. All Haptic Turk setups consist of a user who is supported by one or more human actuators. The user enjoys an interactive motion simulation such as a hang glider experience, but the motion is generated by those human actuators who manually lift, tilt, and push the user’s limbs or torso. To get the timing and force right, timed motion instructions in a format familiar from rhythm games are generated by the system.
Next, we extend the concept of human actuation from 3-DoF to 6-DoF virtual reality where users have the freedom to walk around. TurkDeck tackles this problem by orchestrating a group of human actuators to reconfigure a set of passive props on the fly while the user is progressing in the virtual environment. TurkDeck schedules human actuators by their distances from the user, and instructs them to reconfigure the props to the right place on the right time using laser projection and voice output.
Our studies in Haptic Turk and TurkDeck showed that human actuators enjoyed the experience but not as much as users. To eliminate the need of dedicated human actuators, Mutual Turk makes everyone a user by exchanging mechanical actuation between two or more users. Mutual Turk’s main functionality is that it orchestrates the users so as to actuate props at just the right moment and with just the right force to produce the correct feedback in each other's experience.
Finally, we further eliminate the need of another user, making human actuation applicable to single-user experiences. iTurk makes the user constantly reconfigure and animate otherwise passive props. This allows iTurk to provide virtual worlds with constantly varying or even animated haptic effects, even though the only animate entity present in the system is the user. Our demo experience features one example each of iTurk’s two main types of props, i.e., reconfigurable props (the foldable board from TurkDeck) and animated props (the pendulum).
We conclude this dissertation by summarizing the findings of our explorations and pointing out future directions. We discuss the development of human actuation compare to traditional machine actuation, the possibility of combining human and machine actuators and interaction models that involve more human actuators.
Business processes constantly generate, manipulate, and consume data that are managed by organizational databases. Despite being central to process modeling and execution, the link between processes and data is often handled by developers when the process is implemented, thus leaving the connection unexplored during the conceptual design. In this paper, we introduce, formalize, and evaluate a novel conceptual view that bridges the gap between process and data models, and show some kinds of interesting insights that can be derived from this novel proposal.
The problem of constructing and maintaining a tree topology in a distributed manner is a challenging task in WSNs. This is because the nodes have limited computational and memory resources and the network changes over time. We propose the Dynamic Gallager-Humblet-Spira (D-GHS) algorithm that builds and maintains a minimum spanning tree. To do so, we divide D-GHS into four phases, namely neighbor discovery, tree construction, data collection, and tree maintenance. In the neighbor discovery phase, the nodes collect information about their neighbors and the link quality. In the tree construction, D-GHS finds the minimum spanning tree by executing the Gallager-Humblet-Spira algorithm. In the data collection phase, the sink roots the minimum spanning tree at itself, and each node sends data packets. In the tree maintenance phase, the nodes repair the tree when communication failures occur. The emulation results show that D-GHS reduces the number of control messages and the energy consumption, at the cost of a slight increase in memory size and convergence time.
3D point cloud technology facilitates the automated and highly detailed digital acquisition of real-world environments such as assets, sites, cities, and countries; the acquired 3D point clouds represent an essential category of geodata used in a variety of geoinformation applications and systems. In this paper, we present a web-based system for the interactive and collaborative exploration and inspection of arbitrary large 3D point clouds. Our approach is based on standard WebGL on the client side and is able to render 3D point clouds with billions of points. It uses spatial data structures and level-of-detail representations to manage the 3D point cloud data and to deploy out-of-core and web-based rendering concepts. By providing functionality for both, thin-client and thick-client applications, the system scales for client devices that are vastly different in computing capabilities. Different 3D point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering and highlighting, e.g., based on per-point surface categories or temporal information. A set of interaction techniques allows users to collaboratively work with the data, e.g., by measuring distances and areas, by annotating, or by selecting and extracting data subsets. Additional value is provided by the system's ability to display additional, context-providing geodata alongside 3D point clouds and to integrate task-specific processing and analysis operations. We have evaluated the presented techniques and the prototype system with different data sets from aerial, mobile, and terrestrial acquisition campaigns with up to 120 billion points to show their practicality and feasibility.
Live migration is an important feature in modern software-defined datacenters and cloud computing environments. Dynamic resource management, load balance, power saving and fault tolerance are all dependent on the live migration feature. Despite the importance of live migration, the cost of live migration cannot be ignored and may result in service availability degradation. Live migration cost includes the migration time, downtime, CPU overhead, network and power consumption. There are many research articles that discuss the problem of live migration cost with different scopes like analyzing the cost and relate it to the parameters that control it, proposing new migration algorithms that minimize the cost and also predicting the migration cost. For the best of our knowledge, most of the papers that discuss the migration cost problem focus on open source hypervisors. For the research articles focus on VMware environments, none of the published articles proposed migration time, network overhead and power consumption modeling for single and multiple VMs live migration. In this paper, we propose empirical models for the live migration time, network overhead and power consumption for single and multiple VMs migration. The proposed models are obtained using a VMware based testbed.