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dc.contributor.advisorRamanan, Prakash
dc.contributor.authorFrancis, Preeti
dc.date.accessioned2016-06-15T16:56:25Z
dc.date.available2016-06-15T16:56:25Z
dc.date.issued2015-12
dc.identifier.othert15069
dc.identifier.urihttp://hdl.handle.net/10057/12097
dc.descriptionThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
dc.description.abstractThe 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.
dc.format.extentx, 36 p
dc.language.isoen_US
dc.publisherWichita State University
dc.rightsPreeti Francis
dc.rightsCopyright 2015 Preeti Francis
dc.subject.lcshElectronic thesis
dc.titleComputing ranked selection indexes for linear preference queries
dc.typeThesis


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  • CE Theses and Dissertations
    Doctoral and Master's theses authored by the College of Engineering graduate students
  • EECS Theses and Dissertations
    Collection of Master's theses and Ph.D. dissertations completed at the Dept. of Electrical Engineering and Computer Science
  • Master's Theses
    This collection includes Master's theses completed at the Wichita State University Graduate School (Fall 2005 --)

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