Search

Das Buch Vom Data Warehouse zum Corporate Knowledge Center vermittelt einen tiefen Einblick in den State-of-the-Art sowie die Zukunftsperspektiven im Bereich der integrierten Informationslogistik. Hierbei wird zum einen betrachtet, inwieweit sich bisherige Ansätze zum Data Warehousing mittelfristig technisch, organisatorisch und wirtschaftlich als geeignete Lösungen erwiesen haben. Die Kernthemen hierbei sind Architekturen, Vorgehensmodelle, BI, OLAP und DSS/EIS. Zum anderen werden neuere Ansätze vorgestellt, die die Integration des Data Warehouse in die Gesamt-Informationslogistik zum Ziel haben und so die Realisierung neuer Applikationstypen und Geschäftsmodelle ermöglichen. Hierbei werden insbesondere die Themenbereiche CRM und Metadatenmanagement angesprochen.

This edited volume comprises invited chapters that cover five areas of the current and the future development of intelligent systems and information sciences. Half of the chapters were presented as invited talks at the Workshop'Future Directions for Intelligent Systems and Information Sciences'held in Dunedin, New Zealand, 22-23 November 1999 after the International Conference on Neuro-Information Processing (lCONIPI ANZIISI ANNES'99) held in Perth, Australia. In order to make this volume useful for researchers and academics in the broad area of information sciences I invited prominent researchers to submit materials and present their view about future paradigms, future trends and directions. Part I contains chapters on adaptive, evolving, learning systems. These are systems that learn in a life-long, on-line mode and in a changing environment. The first chapter, written by the editor, presents briefly the paradigm of Evolving Connectionist Systems (ECOS) and some of their applications. The chapter by Sung-Bae Cho presents the paradigms of artificial life and evolutionary programming in the context of several applications (mobile robots, adaptive agents of the WWW). The following three chapters written by R.Duro, J.Santos and J.A.Becerra (chapter 3), GCoghill. (chapter 4), Y.Maeda (chapter 5) introduce new techniques for building adaptive, learning robots.

The Web is the nervous system of information society. As such, it has a pervasive influence on our daily lives. And yet, in some ways the Web does not have a high MIQ (Machine IQ). What can be done to enhance it? This is the leitmotif of'Intelligent Exploration of the Web,'(lEW)--a collection of articles co-edited by Drs. Szczepaniak, Segovia, Kacprzyk and, to a small degree, myself. The articles that comprise lEW address many basic problems ranging from structure analysis of Internet documents and Web dialogue management to intelligent Web agents for extraction of information, and bootstrapping an ontology-based information extraction system. Among the basic problems, one that stands out in importance is the problem of search. Existing search engines have many remarkable capabilities. But what is not among them is the deduction capability--the capability to answer a query by drawing on information which resides in various parts of the knowledge base. An example of a query might be'How many Ph.D. degrees in computer science were granted by European universities in 1996?'No existing search engine is capable of dealing with queries of comparable or even much lower complexity. Basically, what we would like to do is to add deduction capability to a search engine, with the aim of transforming it into a question-answering system, or a QI A system, for short. This is a problem that is of major importance and a challenge that is hard to meet.