discussions and for agreeing to be the co-examiner of this thesis. I also wish to thank the members of the Ph.D. committee, Professor Robert Weigel, and Professor Klaus Meyer-Wegener. I would like to thank Dr. Patrick Haspel and Cadence Academic Network who assisted me greatly during my work. Furthermore, I

This e-book is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered when trying to solve them. In this book, we focus on

Berichterstatter: Priv. Doz. Dr. med. Klaus Stelter Mitberichterstatter: Prof. Dr. Carl-Peter Cornelius Mitbetreuung durch den

This paper describes a model for optimum information retrieval over a distributed document collection. The model stems from Robertson's Probability Ranking Principle: Having computed individual document rankings correlated to different subcollections, these local rankings are stepwise merged into a final ranking list where the documents are ordered according to their probability of relevance. Here, a full dissemination of subcollection-wide information is not required. The documents of different subcollections are assumed to be indexed using different indexing vocabularies. Moreover, local rankings may be computed by individual probabilistic retrieval methods. The underlying data volume is arbitrarily scalable. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model. Keywords: Information Retrieval, Probabilistic Model, Distributed Systems 1 Introduction Information retrieval (IR) methods have been developed to support the search for t...

A model for optimal information retrieval over a distributed document collection is described and experimentally evaluated. The fusion of retrieval results corresponding to document subcollections is performed according to the Probability Ranking Principle. Part of the model is a selection criterion for effectively limiting the ranking process to a subset of subcollections.

Abstract. Nowadays maintenance of database management systems (DBMSs) often requires offline operations for enhancement of function-ality or security updates. This hampers the availability of the provided services and can cause undesirable implications. Therefore it is essen-tial to minimize the downtime of DBMSs. We present the CoBRA DB (Component Based Runtime Adaptable DataBase) project that allows the adaptation and extension of a modular DBMS at runtime. In this paper we focus on the definition of an adaptation model describing the semantics of adaptation processes. 1

Berichterstatter: Prof. Dr. rer. biol. hum. Dieter Hölzel Mitberichterstatter: Prof. Dr.med. Dr. h.c. Peter C. Scriba Prof. Dr. med. Klaus Friese Mitbetreuung durch den promovierten Mitarbeiter: PD Dr. med. Jutta Engel

mittlerweile zum 25. Mal fand vom 28.5. bis 31.5.2013 der Workshop

ct the semantically rich contents of MMOs and the design of generalized interfaces for the MMOs. The multimedia object storage system (MOSS) designed at the University of Erlangen is a standalone system that allows to focus on the questions related to the storage and retrieval of MMOs. MOSS has a highly modular structure, which is ideal to compare different solutions for the issues mentioned above. Furthermore, it is integrated in a client-server architecture. 2 Access to Multimedia Objects in MOSS 2.1 Handling of Media Objects MMOs consist of identifiable parts that show different characteristics and functionality depending on the kind of medium they belong to, the so-called single-medium data objects (SMOs). 1. Chair for Database Systems, Computer Science Departement, University of Erlangen-Nürnberg, Germany 2. Institute of Software Engineering II, Faculty of Computer Science, Technical University of Dresden, Germany 2 Medium-related a

der Universität Erlangen-Nürnberg erscheint seit 1967. Begründet von Prof. Dr. Dr. h. c. mult. Wolfgang HändlerUniversität Erlangen-Nürnberg