Joachim W. Schmidt – författare

This volume represents the outcome of the International Conference on "Extending Database Technology", held in Venice, Italy, in March 1988. It contains the major dimensions into which database technology is currently being pushed by the needs of new applications and pulled by the chances of novel developments in hardware and systems architecture. The two broad areas covered are: extended database semantics: data models and data types, databases and logic, complex objects, and expert system approaches to databases; and new architectures and increased database systems support: novel transaction models, data distribution and replication, database administration and access efficiency. Since an increasing amount of database research and development is done through international cooperation and within joint projects (ESPRIT, EUREKA, industrial cooperations), a substantial part of the book is dedicated to representing about a dozen advanced database projects on data- and knowledge-based systems and on distributed database applications by their aims, status, and results.

The series of International Conferences on Extending Database Technology (EDBT) is an established and prestigious forum for the exchange of the latest research results in data management. It provides unique opportunities for database researchers, practitioners, developers, and users to explore new ideas, techniques, and tools, and to exchange experiences. This volume contains the proceedings of the 10th EDBT Conference, held in Munich, Germany, March 27-29, 2006. The conference included 3 keynote talks, 56 full-size and 4 half-size research papers in 20 sessions, 8 industrial presentations in 3 sessions, 1 panel session, 5 tutorials in 7 sessions, and 20 demonstrations in 4 sessions. All of the research papers as well as papers and abstracts from most of the other sessions are included here. Distinguished members of the database and information-retrieval communities delivered the three keynotes, which were all in the spirit of the banner theme chosen for EDBT 2006: “From Database Systems to Universal Data Management. ” Martin Kersten, a pioneer in the area of database support for ambient application environments and the investigator of several kernel database architectures, discussed various hard issues that arise in organic database systems, i. e. , systems that can be embedded in several hardware applications and have autonomic behavior. Alan Smeaton, a leader in content-based retrieval of information in a wide variety of media, introduced us to the world of digital video libraries and challenged us with several open problems associated with their effective management.

The series of International Conferences on Extending Database Technology (EDBT) is an established and prestigious forum for the exchange of the latest research results in data management. It provides unique opportunities for database researchers, practitioners, developers, and users to explore new ideas, techniques, and tools, and to exchange experiences. This volume contains the proceedings of the 10th EDBT Conference, held in Munich, Germany, March 27-29, 2006. The conference included 3 keynote talks, 56 full-size and 4 half-size research papers in 20 sessions, 8 industrial presentations in 3 sessions, 1 panel session, 5 tutorials in 7 sessions, and 20 demonstrations in 4 sessions. All of the research papers as well as papers and abstracts from most of the other sessions are included here. Distinguished members of the database and information-retrieval communities delivered the three keynotes, which were all in the spirit of the banner theme chosen for EDBT 2006: “From Database Systems to Universal Data Management. ” Martin Kersten, a pioneer in the area of database support for ambient application environments and the investigator of several kernel database architectures, discussed various hard issues that arise in organic database systems, i. e. , systems that can be embedded in several hardware applications and have autonomic behavior. Alan Smeaton, a leader in content-based retrieval of information in a wide variety of media, introduced us to the world of digital video libraries and challenged us with several open problems associated with their effective management.

Currently, the field of information systems technology is rapidly extending into several dimensions. There is the semantic dimension (including object orientation, data deduction and extended knowledge representation schemes), there is improved systems integration, and there are new tools. All these extensions aim to provide semantically richer and better engineered information systems that allow for more adequate and complete representations and thus extend the effective use of database technology to a wider class of applications. Database researchers and developers, whether they are committed to application or to system construction, are convinced that next-generation information system technology will be heavily determined by a handful of new concepts that they have to understand and work out in detail now. This volume concentrates on the following topics: - Extended data types and data models, database programming languages; - Rule-based data deduction, expert systems, knowledge bases; - Object orientation and semantic data modelling; - DB application development, methodologies and tools; - Interface technology, parallelism, interoperability, ...; - New database applications.

After a long period of research, development, test and trial, relational database management systems are at last being marketed in force. The feedback from early installations of these systems is overwhelmingly positive. The most frequent comment by users is that productivity has been increased by a significant factor (from 5 to 20 times what it was using previous approaches). Another comment is that, in many cases, end users can now handle their own problems by direct use of the system instead of using application programmers as mediators between them and the system. As the reputation of relational systems for ease of use and enhanced productivity has grown, there has been a strong temptation for vendors of other approaches to exploit the label "relational" somewhat indiscriminately. In some cases the label is being misapplied to a whole data system; in others it is being misapplied to an interface. It is therefore worth developing criteria which database management systems (DBMSs) should have in order to be called "relational". The Relational Task Group (RTG) of the American National Standards Institute (ANSI) undertook such an effort by developing a characterization of RDBMSs and analyzing fourteen DBMSs per this characterization. The result of this work is presented in this book. The conclusions of the RTG are in agreement with my view that a DBMS should not be called "relational" unless it satisfies at least the following conditions: 1. All information in the database is represented as values in tables.

After a long period of research, development, test and trial, relational database management systems are at last being marketed in force. The feedback from early installations of these systems is overwhelmingly positive. The most frequent comment by users is that productivity has been increased by a significant factor (from 5 to 20 times what it was using previous approaches). Another comment is that, in many cases, end users can now handle their own problems by direct use of the system instead of using application programmers as mediators between them and the system. As the reputation of relational systems for ease of use and enhanced productivity has grown, there has been a strong temptation for vendors of other approaches to exploit the label "relational" somewhat indiscriminately. In some cases the label is being misapplied to a whole data system; in others it is being misapplied to an interface. It is therefore worth developing criteria which database management systems (DBMSs) should have in order to be called "relational". The Relational Task Group (RTG) of the American National Standards Institute (ANSI) undertook such an effort by developing a characterization of RDBMSs and analyzing fourteen DBMSs per this characterization. The result of this work is presented in this book. The conclusions of the RTG are in agreement with my view that a DBMS should not be called "relational" unless it satisfies at least the following conditions: 1. All information in the database is represented as values in tables.

In the past, applied artificial intelligence systems were built with particular emphasis on general reasoning methods intended to function efficiently, even when only relatively little domain-specific knowledge was available. In other words, AI technology aimed at the processing of knowledge stored under comparatively general representation schemes. Nowadays, the focus has been redirected to the role played by specific and detailed knowledge, rather than to the reasoning methods themselves. Many new application systems are centered around knowledge bases, i. e. , they are based on large collections offacts, rules, and heuristics that cap­ ture knowledge about a specific domain of applications. Experience has shown that when used in combination with rich knowledge bases, even simple reasoning methods can be extremely effective in a wide variety of problem domains. Knowledge base construction and management will thus become the key factor in the development of viable knowledge-based ap­ plications. Knowledge Base Management Systems (KBMSs) are being proposed that provide user-friendly environments for the construction, retrieval, and manipUlation of large shared knowledge bases. In addition to deductive reasoning, KBMSs require operational characteristics such as concurrent access, integrity maintenance, error recovery, security, and perhaps distribution. For the development ofKBMSs, the need to integrate concepts and technologies from different areas, such as Artificial Intel­ ligence, Databases, and Logic, has been widely recognized. One of the central issues for KBMSs is the framework used for knowledge representation-semantic networks, frames, rules, and logics are proposed by the AI and logic communities.

In the past, applied artificial intelligence systems were built with particular emphasis on general reasoning methods intended to function efficiently, even when only relatively little domain-specific knowledge was available. In other words, AI technology aimed at the processing of knowledge stored under comparatively general representation schemes. Nowadays, the focus has been redirected to the role played by specific and detailed knowledge, rather than to the reasoning methods themselves. Many new application systems are centered around knowledge bases, i. e. , they are based on large collections offacts, rules, and heuristics that cap­ ture knowledge about a specific domain of applications. Experience has shown that when used in combination with rich knowledge bases, even simple reasoning methods can be extremely effective in a wide variety of problem domains. Knowledge base construction and management will thus become the key factor in the development of viable knowledge-based ap­ plications. Knowledge Base Management Systems (KBMSs) are being proposed that provide user-friendly environments for the construction, retrieval, and manipUlation of large shared knowledge bases. In addition to deductive reasoning, KBMSs require operational characteristics such as concurrent access, integrity maintenance, error recovery, security, and perhaps distribution. For the development ofKBMSs, the need to integrate concepts and technologies from different areas, such as Artificial Intel­ ligence, Databases, and Logic, has been widely recognized. One of the central issues for KBMSs is the framework used for knowledge representation-semantic networks, frames, rules, and logics are proposed by the AI and logic communities.