004 Datenverarbeitung; Informatik
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Graph repair, restoring consistency of a graph, plays a prominent role in several areas of computer science and beyond: For example, in model-driven engineering, the abstract syntax of models is usually encoded using graphs. Flexible edit operations temporarily create inconsistent graphs not representing a valid model, thus requiring graph repair. Similarly, in graph databases—managing the storage and manipulation of graph data—updates may cause that a given database does not satisfy some integrity constraints, requiring also graph repair. We present a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of least-changing repairs. In our context, we formalize consistency by so-called graph conditions being equivalent to first-order logic on graphs. We present two kind of repair algorithms: State-based repair restores consistency independent of the graph update history, whereas deltabased (or incremental) repair takes this history explicitly into account. Technically, our algorithms rely on an existing model generation algorithm for graph conditions implemented in AutoGraph. Moreover, the delta-based approach uses the new concept of satisfaction (ST) trees for encoding if and how a graph satisfies a graph condition. We then demonstrate how to manipulate these STs incrementally with respect to a graph update.
This contribution presents a quantitative evaluation procedure for Information Retrieval models and the results of this procedure applied on the enhanced Topic-based Vector Space Model (eTVSM). Since the eTVSM is an ontology-based model, its effectiveness heavily depends on the quality of the underlaying ontology. Therefore the model has been tested with different ontologies to evaluate the impact of those ontologies on the effectiveness of the eTVSM. On the highest level of abstraction, the following results have been observed during our evaluation: First, the theoretically deduced statement that the eTVSM has a similar effecitivity like the classic Vector Space Model if a trivial ontology (every term is a concept and it is independet of any other concepts) is used has been approved. Second, we were able to show that the effectiveness of the eTVSM raises if an ontology is used which is only able to resolve synonyms. We were able to derive such kind of ontology automatically from the WordNet ontology. Third, we observed that more powerful ontologies automatically derived from the WordNet, dramatically dropped the effectiveness of the eTVSM model even clearly below the effectiveness level of the Vector Space Model. Fourth, we were able to show that a manually created and optimized ontology is able to raise the effectiveness of the eTVSM to a level which is clearly above the best effectiveness levels we have found in the literature for the Latent Semantic Index model with compareable document sets.
E-learning is a flexible and personalized alternative to traditional education. Nonetheless, existing e-learning systems for IT security education have difficulties in delivering hands-on experience because of the lack of proximity. Laboratory environments and practical exercises are indispensable instruction tools to IT security education, but security education in con-ventional computer laboratories poses the problem of immobility as well as high creation and maintenance costs. Hence, there is a need to effectively transform security laboratories and practical exercises into e-learning forms. This report introduces the Tele-Lab IT-Security architecture that allows students not only to learn IT security principles, but also to gain hands-on security experience by exercises in an online laboratory environment. In this architecture, virtual machines are used to provide safe user work environments instead of real computers. Thus, traditional laboratory environments can be cloned onto the Internet by software, which increases accessibilities to laboratory resources and greatly reduces investment and maintenance costs. Under the Tele-Lab IT-Security framework, a set of technical solutions is also proposed to provide effective functionalities, reliability, security, and performance. The virtual machines with appropriate resource allocation, software installation, and system configurations are used to build lightweight security laboratories on a hosting computer. Reliability and availability of laboratory platforms are covered by the virtual machine management framework. This management framework provides necessary monitoring and administration services to detect and recover critical failures of virtual machines at run time. Considering the risk that virtual machines can be misused for compromising production networks, we present security management solutions to prevent misuse of laboratory resources by security isolation at the system and network levels. This work is an attempt to bridge the gap between e-learning/tele-teaching and practical IT security education. It is not to substitute conventional teaching in laboratories but to add practical features to e-learning. This report demonstrates the possibility to implement hands-on security laboratories on the Internet reliably, securely, and economically.
Business process management experiences a large uptake by the industry, and process models play an important role in the analysis and improvement of processes. While an increasing number of staff becomes involved in actual modeling practice, it is crucial to assure model quality and homogeneity along with providing suitable aids for creating models. In this paper we consider the problem of offering recommendations to the user during the act of modeling. Our key contribution is a concept for defining and identifying so-called action patterns - chunks of actions often appearing together in business processes. In particular, we specify action patterns and demonstrate how they can be identified from existing process model repositories using association rule mining techniques. Action patterns can then be used to suggest additional actions for a process model. Our approach is challenged by applying it to the collection of process models from the SAP Reference Model.
Duplicate detection is the task of identifying all groups of records within a data set that represent the same real-world entity, respectively. This task is difficult, because (i) representations might differ slightly, so some similarity measure must be defined to compare pairs of records and (ii) data sets might have a high volume making a pair-wise comparison of all records infeasible. To tackle the second problem, many algorithms have been suggested that partition the data set and compare all record pairs only within each partition. One well-known such approach is the Sorted Neighborhood Method (SNM), which sorts the data according to some key and then advances a window over the data comparing only records that appear within the same window. We propose several variations of SNM that have in common a varying window size and advancement. The general intuition of such adaptive windows is that there might be regions of high similarity suggesting a larger window size and regions of lower similarity suggesting a smaller window size. We propose and thoroughly evaluate several adaption strategies, some of which are provably better than the original SNM in terms of efficiency (same results with fewer comparisons).
Unique column combinations of a relational database table are sets of columns that contain only unique values. Discovering such combinations is a fundamental research problem and has many different data management and knowledge discovery applications. Existing discovery algorithms are either brute force or have a high memory load and can thus be applied only to small datasets or samples. In this paper, the wellknown GORDIAN algorithm and "Apriori-based" algorithms are compared and analyzed for further optimization. We greatly improve the Apriori algorithms through efficient candidate generation and statistics-based pruning methods. A hybrid solution HCAGORDIAN combines the advantages of GORDIAN and our new algorithm HCA, and it significantly outperforms all previous work in many situations.
Für die vorliegende Studie »Qualitative Untersuchung zur Akzeptanz des neuen Personalausweises und Erarbeitung von Vorschlägen zur Verbesserung der Usability der Software AusweisApp« arbeitete ein Innovationsteam mit Hilfe der Design Thinking Methode an der Aufgabenstellung »Wie können wir die AusweisApp für Nutzer intuitiv und verständlich gestalten?« Zunächst wurde die Akzeptanz des neuen Personalausweises getestet. Bürger wurden zu ihrem Wissensstand und ihren Erwartungen hinsichtlich des neuen Personalausweises befragt, darüber hinaus zur generellen Nutzung des neuen Personalausweises, der Nutzung der Online-Ausweisfunktion sowie der Usability der AusweisApp. Weiterhin wurden Nutzer bei der Verwendung der aktuellen AusweisApp beobachtet und anschließend befragt. Dies erlaubte einen tiefen Einblick in ihre Bedürfnisse. Die Ergebnisse aus der qualitativen Untersuchung wurden verwendet, um Verbesserungsvorschläge für die AusweisApp zu entwickeln, die den Bedürfnissen der Bürger entsprechen. Die Vorschläge zur Optimierung der AusweisApp wurden prototypisch umgesetzt und mit potentiellen Nutzern getestet. Die Tests haben gezeigt, dass die entwickelten Neuerungen den Bürgern den Zugang zur Nutzung der Online-Ausweisfunktion deutlich vereinfachen. Im Ergebnis konnte festgestellt werden, dass der Akzeptanzgrad des neuen Personalausweises stark divergiert. Die Einstellung der Befragten reichte von Skepsis bis hin zu Befürwortung. Der neue Personalausweis ist ein Thema, das den Bürger polarisiert. Im Rahmen der Nutzertests konnten zahlreiche Verbesserungspotenziale des bestehenden Service Designs sowohl rund um den neuen Personalausweis, als auch im Zusammenhang mit der verwendeten Software aufgedeckt werden. Während der Nutzertests, die sich an die Ideen- und Prototypenphase anschlossen, konnte das Innovtionsteam seine Vorschläge iterieren und auch verifizieren. Die ausgearbeiteten Vorschläge beziehen sich auf die AusweisApp. Die neuen Funktionen umfassen im Wesentlichen: · den direkten Zugang zu den Diensteanbietern, · umfangreiche Hilfestellungen (Tooltips, FAQ, Wizard, Video), · eine Verlaufsfunktion, · einen Beispieldienst, der die Online-Ausweisfunktion erfahrbar macht. Insbesondere gilt es, den Nutzern mit der neuen Version der AusweisApp Anwendungsfelder für ihren neuen Personalausweis und einen Mehrwert zu bieten. Die Ausarbeitung von weiteren Funktionen der AusweisApp kann dazu beitragen, dass der neue Personalausweis sein volles Potenzial entfalten kann.
Version Control Systems (VCS) allow developers to manage changes to software artifacts. Developers interact with VCSs through a variety of client programs, such as graphical front-ends or command line tools. It is desirable to use the same version control client program against different VCSs. Unfortunately, no established abstraction over VCS concepts exists. Instead, VCS client programs implement ad-hoc solutions to support interaction with multiple VCSs. This thesis presents Pur, an abstraction over version control concepts that allows building rich client programs that can interact with multiple VCSs. We provide an implementation of this abstraction and validate it by implementing a client application.
1 Introduction 1.1 Project formulation 1.2 Our contribution 2 Pedagogical Aspect 4 2.1 Modern teaching 2.2 Our Contribution 2.2.1 Autonomous and exploratory learning 2.2.2 Human machine interaction 2.2.3 Short multimedia clips 3 Ontology Aspect 3.1 Ontology driven expert systems 3.2 Our contribution 3.2.1 Ontology language 3.2.2 Concept Taxonomy 3.2.3 Knowledge base annotation 3.2.4 Description Logics 4 Natural language approach 4.1 Natural language processing in computer science 4.2 Our contribution 4.2.1 Explored strategies 4.2.2 Word equivalence 4.2.3 Semantic interpretation 4.2.4 Various problems 5 Information Retrieval Aspect 5.1 Modern information retrieval 5.2 Our contribution 5.2.1 Semantic query generation 5.2.2 Semantic relatedness 6 Implementation 6.1 Prototypes 6.2 Semantic layer architecture 6.3 Development 7 Experiments 7.1 Description of the experiments 7.2 General characteristics of the three sessions, instructions and procedure 7.3 First Session 7.4 Second Session 7.5 Third Session 7.6 Discussion and conclusion 8 Conclusion and future work 8.1 Conclusion 8.2 Open questions A Description Logics B Probabilistic context-free grammars
The noble way to substantiate decisions that affect many people is to ask these people for their opinions. For governments that run whole countries, this means asking all citizens for their views to consider their situations and needs.
Organizations such as Africa's Voices Foundation, who want to facilitate communication between decision-makers and citizens of a country, have difficulty mediating between these groups. To enable understanding, statements need to be summarized and visualized. Accomplishing these goals in a way that does justice to the citizens' voices and situations proves challenging. Standard charts do not help this cause as they fail to create empathy for the people behind their graphical abstractions. Furthermore, these charts do not create trust in the data they are representing as there is no way to see or navigate back to the underlying code and the original data. To fulfill these functions, visualizations would highly benefit from interactions to explore the displayed data, which standard charts often only limitedly provide.
To help improve the understanding of people's voices, we developed and categorized 80 ideas for new visualizations, new interactions, and better connections between different charts, which we present in this report. From those ideas, we implemented 10 prototypes and two systems that integrate different visualizations. We show that this integration allows consistent appearance and behavior of visualizations. The visualizations all share the same main concept: representing each individual with a single dot. To realize this idea, we discuss technologies that efficiently allow the rendering of a large number of these dots. With these visualizations, direct interactions with representations of individuals are achievable by clicking on them or by dragging a selection around them. This direct interaction is only possible with a bidirectional connection from the visualization to the data it displays. We discuss different strategies for bidirectional mappings and the trade-offs involved. Having unified behavior across visualizations enhances exploration. For our prototypes, that includes grouping, filtering, highlighting, and coloring of dots. Our prototyping work was enabled by the development environment Lively4. We explain which parts of Lively4 facilitated our prototyping process. Finally, we evaluate our approach to domain problems and our developed visualization concepts.
Our work provides inspiration and a starting point for visualization development in this domain. Our visualizations can improve communication between citizens and their government and motivate empathetic decisions. Our approach, combining low-level entities to create visualizations, provides value to an explorative and empathetic workflow. We show that the design space for visualizing this kind of data has a lot of potential and that it is possible to combine qualitative and quantitative approaches to data analysis.