By Martin Kersten (auth.), Yannis Ioannidis, Marc H. Scholl, Joachim W. Schmidt, Florian Matthes, Mike Hatzopoulos, Klemens Boehm, Alfons Kemper, Torsten Grust, Christian Boehm (eds.)
The sequence of overseas meetings on Extending Database expertise (EDBT) is a longtime and prestigious discussion board for the alternate of the most recent learn ends up in facts administration. It offers detailed possibilities for database researchers, practitioners, builders, and clients to discover new rules, strategies, and instruments, and to replace reports. This quantity includes the complaints of the tenth EDBT convention, held in Munich, Germany, March 27-29, 2006. The convention integrated three keynote talks, fifty six considerable and four half-size study papers in 20 periods, eight commercial shows in three periods, 1 panel consultation, five tutorials in 7 classes, and 20 demonstrations in four periods. all the study papers in addition to papers and abstracts from many of the different periods are incorporated the following. individual contributors of the database and information-retrieval groups introduced the 3 keynotes, which have been all within the spirit of the banner subject matter selected for EDBT 2006: “From Database platforms to common info administration. ” Martin Kersten, a pioneer within the region of database help for ambient program environments and the investigator of a number of kernel database architectures, mentioned quite a few challenging concerns that come up in natural database platforms, i. e. , structures that may be embedded in different functions and feature autonomic habit. Alan Smeaton, a pace-setter in content-based retrieval of knowledge in a large choice of media, brought us to the area of electronic video libraries and challenged us with numerous open difficulties linked to their potent management.
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One of the interesting problems in data stream mining is that of predictive query processing. This is useful for a variety of data mining applications which require us to estimate the future behavior of the data stream. In this paper, we will discuss the problem from the point of view of predictive summarization. In predictive summarization, we would like to store statistical characteristics of the data stream which are useful for estimation of queries representing the behavior of the stream in the future.
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