Dagstuhl Seminar Proceedings
About: Dagstuhl Seminar Proceedings is an academic conference. The conference publishes majorly in the area(s): Semantic grid & Software development. Over the lifetime, 1347 publication(s) have been published by the conference receiving 17442 citation(s).
Topics: Semantic grid, Software development, Common value auction, Context (language use), Optimization problem
01 Jan 2005
TL;DR: This article comprehensively reviews and provides insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapped.
Abstract: Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.
01 Jan 2013
University of Kent1, University of Potsdam2, University of Victoria3, Carnegie Mellon University4, Catholic University of Leuven5, Polytechnic University of Milan6, Hasso Plattner Institute7, University of Washington8, West Virginia University9, University of Vienna10, University of Paderborn11, University of Kassel12, Vanderbilt University13, George Mason University14, CA Technologies15, University of Trento16, Ludwig Maximilian University of Munich17, Bell Labs18
Abstract: The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.
•01 Jan 2005
TL;DR: This study illustrates how a technique such as the multiobjective genetic algorithm can be applied and exemplifies how design requirements can be refined as the algorithm runs, and demonstrates the need for preference articulation in cases where many and highly competing objectives lead to a nondominated set too large for a finite population to sample effectively.
Abstract: In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including constraint satisfaction, lexicographic optimization, and a form of goal programming. Then, the ranking of an arbitrary number of candidates is considered, and the ef- fect of preference changes on the cost surface seen by an evolutionary algorithm is illustrated graphically for a simple problem. The formulation of a multiobjective genetic algorithm based on the pro- posed decision strategy is also discussed. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Finally, an application to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine is described, which il- lustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The two instances of the problem studied demonstrate the need for pref- erence articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample ef- fectively. It is shown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to sup- ply preference information to the GA.
•01 Jan 2008
TL;DR: The BIP language for the description and composition of layered components as well as associated tools for executing and analyzing components on a dedicated platform and provides a powerful mechanism for structuring interactions involving rendezvous and broadcast are presented.
Abstract: We present a methodology for modeling heterogeneous real-time components. Components are obtained as the superposition of three layers : Behavior, specified as a set of transitions; Interactions between transitions of the behavior; Priorities, used to choose amongst possible interactions. A parameterized binary composition operator is used to compose components layer by layer. We present the BIP language for the description and composition of layered components as well as associated tools for executing and analyzing components on a dedicated platform. The language provides a powerful mechanism for structuring interactions involving rendezvous and broadcast. We show that synchronous and timed systems are particular classes of components. Finally, we provide examples showing the utility of the BIP framework in heterogeneous component modeling.
01 Jan 2005
TL;DR: An algorithm implementing semantic matching is presented, and its implementation within the S-Match system is discussed, and the results, though preliminary, look promising, in particular for what concerns precision and recall.
Abstract: We think of Match as an operator which takes two graph-like structures and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.
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