Abstract
This paper proposed a new clustering technique for an object-oriented database mangement system. The technique is dynamic and employs a reduced set of statistics to minimize statistics collection overhead. Clustering is done automatically by using several evaluation criteria and requires less knowledge from the user by using fewer user's hints than other techniques. The technique also allows the user to tune the clustering process through a reduced set of parameters. Simulation experiments based on the HyperModel benchmark showed that the proposed clustering technique outperformed the two existing techniques, ORION and CACTIS.
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References
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© 1997 Springer-Verlag Berlin Heidelberg
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Gay, JY., Gruenwald, L. (1997). A clustering technique for object-oriented databases. In: Hameurlain, A., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1997. Lecture Notes in Computer Science, vol 1308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022020
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DOI: https://doi.org/10.1007/BFb0022020
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