Computing ranked selection indexes for linear preference queries
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The significant increase in data storage and its consumption in today's world brings the focus on efficient query processing. It is general practice to query several data sources for k data entities based on a ranking of certain attributes associated with these entities. In our work, we consider the top-k selection query of relational databases: SELECT * FROM S ORDER BY f(t) LIMIT k We consider the special case of linear monotone preference functions f that are based only on two rank attributes in S. This is an important special case of the top-k join query, and has received much attention. We study the problem of constructing efficient indexes on S, so as to find the top-k tuples, for any given linear monotone preference function f. There are efficient algorithms in the literature to construct such an index for k=2. We present another efficient algorithm for k=2, and also an efficient algorithm for k=3. As in some previous works, our approach is based on convex layers, which are more appropriate for linear monotone preference functions.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science