Abstract
In modern database applications the similarity of complex objects is examined by performing distance-based queries (e.g. nearest neighbour search) on data of high dimensionality. Most multidimensional indexing methods have failed to efficiently support these queries in arbitrary high-dimensional datasets (due to the dimensionality curse). Similarity join queries and K closest pairs queries are the most representative distance join queries, where two high-dimensional datasets are combined. These queries are very expensive in terms of response time and I/O activity in case of high-dimensional spaces. On the other hand, the filtering-based approach, as applied by the VA-file, has turned out to be a very promising alternative for nearest neighbour search. In general, the filtering-based approach represents vectors as compact approximations, whereas by first scanning these approximations, only a small fraction of the real vectors is visited. Here, we elaborate on VA-files and develop VA-file based algorithms for answering similarity join and K closest pairs queries on high-dimensional data. Also, performance-wise we compare the use of VA-files and R*-trees (a structure that has been proven to be of robust nature) for answering these queries. The results of the comparison do not lead to a clear winner.
Supported by the ARCHIMEDES project 2.2.14, «Management of Moving Objects and the WWW», of the Technological Educational Institute of Thessaloniki (EPEAEK II), co-funded by the Greek Ministry of Education and Religious Affairs and the European Union, INDALOG TIC2002-03968 project «A Database Language Based on Functional Logic Programming» of the Spanish Ministry of Science and Technology under FEDER funds, and the framework of the Greek-Serbian bilateral protocol.
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Corral, A., D’Ermiliis, A., Manolopoulos, Y., Vassilakopoulos, M. (2005). VA-Files vs. R*-Trees in Distance Join Queries. In: Eder, J., Haav, HM., Kalja, A., Penjam, J. (eds) Advances in Databases and Information Systems. ADBIS 2005. Lecture Notes in Computer Science, vol 3631. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11547686_12
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DOI: https://doi.org/10.1007/11547686_12
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