New application domains cause todays 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 improving effcient processing of query types such as top N. 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.
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