Über die Pragmatik der Graphischen Modellierung

Graphical models help to understand complex systems. However, with the user interaction paradigms established today, activities such as creating, maintaining or browsing graphical models can be very tedious. This thesis presents an approach to enhance productivity by focusing on the pragmatics of model-based design. The contribution includes an interpretation of the notion of pragmatics, orthogonal to syntax and semantics in Model-Driven Engineering (MDE). A proposal on pragmatics-aware modeling is given, employing sophisticated automated layout algorithms to close the gap between MDE and graph drawing theory. Thus, a view management logic presents customized views on models. These concepts get illustrated with the open source Kiel Integrated Environment for Layout Eclipse Rich Client (KIELER) with multiple applications including editing and simulation and shows how view management helps to tame complexity

Relationship analysis : improving the systems analysis process

A significant aspect of systems analysis involves discovering and representing entities and their inter-relationships. Guidelines exist to identify entities but do not provide a rigorous and comprehensive process to explicitly capture the relationship structure of the problem domain. Whereas, other analysis techniques lightly address the relationship discovery process, Relationship Analysis is the only systematic, domain-independent analysis technique focusing exclusively on a domain\u27s relationship structure. The quality of design artifacts, such as class diagrams, and development time necessary to generate these artifacts can be improved by first representing the complete relationship structure of the problem domain. The Relationship Analysis Model is the first theory-based taxonomy to classify relationships. A rigorous evaluation was conducted, including a formal experiment comparing novice and experienced analysts with and without Relationship Analysis. It was shown that the Relationship Analysis Process based on the model does provide a fuller and richer systems analysis, resulting in improved quality of and reduced time in generating class diagrams. It also was shown that Relationship Analysis enables analysts of varying experience levels to achieve a similar level of quality of class diagrams. Relationship Analysis significantly enhances the systems analyst\u27s effectiveness, especially in the area of relationship discovery and documentation resulting in improved analysis and design artifacts

Scalable Data Integration for Linked Data

Linked Data describes an extensive set of structured but heterogeneous datasources where entities are connected by formal semantic descriptions. In thevision of the Semantic Web, these semantic links are extended towards theWorld Wide Web to provide as much machine-readable data as possible forsearch queries. The resulting connections allow an automatic evaluation to findnew insights into the data. Identifying these semantic connections betweentwo data sources with automatic approaches is called link discovery. We derivecommon requirements and a generic link discovery workflow based on similaritiesbetween entity properties and associated properties of ontology concepts. Mostof the existing link discovery approaches disregard the fact that in times ofBig Data, an increasing volume of data sources poses new demands on linkdiscovery. In particular, the problem of complex and time-consuming linkdetermination escalates with an increasing number of intersecting data sources.To overcome the restriction of pairwise linking of entities, holistic clusteringapproaches are needed to link equivalent entities of multiple data sources toconstruct integrated knowledge bases. In this context, the focus on efficiencyand scalability is essential. For example, reusing existing links or backgroundinformation can help to avoid redundant calculations. However, when dealingwith multiple data sources, additional data quality problems must also be dealtwith. This dissertation addresses these comprehensive challenges by designingholistic linking and clustering approaches that enable reuse of existing links.Unlike previous systems, we execute the complete data integration workflowvia a distributed processing system. At first, the LinkLion portal will beintroduced to provide existing links for new applications. These links act asa basis for a physical data integration process to create a unified representationfor equivalent entities from many data sources. We then propose a holisticclustering approach to form consolidated clusters for same real-world entitiesfrom many different sources. At the same time, we exploit the semantic typeof entities to improve the quality of the result. The process identifies errorsin existing links and can find numerous additional links. Additionally, theentity clustering has to react to the high dynamics of the data. In particular,this requires scalable approaches for continuously growing data sources withmany entities as well as additional new sources. Previous entity clusteringapproaches are mostly static, focusing on the one-time linking and clustering ofentities from few sources. Therefore, we propose and evaluate new approaches for incremental entity clustering that supports the continuous addition of newentities and data sources. To cope with the ever-increasing number of LinkedData sources, efficient and scalable methods based on distributed processingsystems are required. Thus we propose distributed holistic approaches to linkmany data sources based on a clustering of entities that represent the samereal-world object. The implementation is realized on Apache Flink. In contrastto previous approaches, we utilize efficiency-enhancing optimizations for bothdistributed static and dynamic clustering. An extensive comparative evaluationof the proposed approaches with various distributed clustering strategies showshigh effectiveness for datasets from multiple domains as well as scalability on amulti-machine Apache Flink cluster

Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project

Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic

Real-time programming and the big ideas of computational literacy

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.Includes bibliographical references (p. 115-121).Though notoriously difficult, real-time programming offers children a rich new set of applications, and the opportunity to engage bodily knowledge and experience more centrally in intellectual enterprises. Moreover, the seemingly specialized problems of real-time programming can be seen as keys to longstanding difficulties of programming in general. I report on a critical design inquiry into the nature and potential of real-time programming by children. A cyclical process of design, prototyping and testing of computational environments has led to two design innovations: a language in which declarative and procedural descriptions of computation are given equal status, and can subsume each other to arbitrary levels of nesting [and] a "live text" environment, in which real-time display of, and intervention in, program execution are accomplished within the program text itself. Based on children's use of these tools, as well as comparative evidence from other media and domains, I argue that the coordination of discrete and continuous process should be considered a central Big Idea in programming and beyond. In addition, I offer the theoretical notion of the "steady frame" as a way to clarify the user interface requirements of real-time programming, and also to understand the role of programming in learning to construct dynamic models, theories, and representations. Implications for the role of programming in education and for the future of computational literacy are discussed.by Christopher Michael Hancock.Ph.D