The Community for Technology Leaders
Green Image
<p><b>Abstract</b>—New application domains cause today's database sizes to grow rapidly, posing great demands on technology. Data fragmentation facilitates techniques (like distribution, parallelization, and main-memory computing) meeting these demands. Also, fragmentation might help to improve efficient processing of query types such as top <tmath>{\rm{N}}</tmath>. Database design and query optimization require a good notion of the costs resulting from a certain fragmentation. Our mathematically derived selectivity model facilitates this. Once its two parameters have been computed based on the fragmentation, after each (though usually infrequent) update, our model can forget the data distribution, resulting in fast and quite good selectivity estimation. We show experimental verification for Zipfian distributed IR databases.</p>
Selectivity, fragmentation, Zipf, information retrieval, databases.
Sunil Choenni, Peter M.G. Apers, Henk Ernst Blok, Henk M. Blanken, "A Selectivity Model for Fragmented Relations: Applied in Information Retrieval", IEEE Transactions on Knowledge & Data Engineering, vol. 16, no. , pp. 635-639, May 2004, doi:10.1109/TKDE.2004.1277824
86 ms
(Ver 3.3 (11022016))