We present in this paper three dynamicclustering techniques for object-Oriented Databases (OODBs). The first two, dynamic, Statistical & Tunable clustering (DSTC) and StatClust, exploit both comprehensive usage s...
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(纸本)3540416641
We present in this paper three dynamicclustering techniques for object-Oriented Databases (OODBs). The first two, dynamic, Statistical & Tunable clustering (DSTC) and StatClust, exploit both comprehensive usage statistics and the inter-object reference graph. They are quite elaborate. However, they are also complex to implement and induce a high overhead. The third clustering technique, called Detection & Reclustering of objects (DRO), is based on the same principles, but is much simpler to implement. These three clustering algorithm have been implemented in the Texas persistent object store and compared in terms of clustering efficiency (i.e., overall performance increase) and overhead using the object Cluslering Benchmark (OCB). The results obtained showed that DRO induced a lighter overhead while still achieving better overall performance.
Video data management is fast becoming one of the most important topics in multi media databases. In this paper, we describe the development of an experimental video information management system, called ''VIM...
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Video data management is fast becoming one of the most important topics in multi media databases. In this paper, we describe the development of an experimental video information management system, called ''VIM'', bring implemented at the Hong Kong University of Science & Technology, which employs two fundamental components-i) a Video Classification Component (VCC) for the generation of effective indices necessary for structuring the video data, and ii) a Conceptual clustering Mechanism (CCM) having extended object-oriented features and techniques. By incorporating CCM concepts and techniques together with the classified features and indices generated from the VCC, the information management system enables users to form dynamically, among other things, video programs (or segments) from existing objects based on semantic features/index terms. A prototype of this system has been constructed, using a persistent object storage manager (viz., EOS), on Sun4 workstations.
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