Top-k Dominating Queries on Incomplete Data
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INTRODUCTION
GIVEN a set Sof d-dimensional objects, top-k dominating (TKD) query ranks the objects oin Sbased on the number of the objects in Sdominated by o, and returns the objects from Sthat dominate the maximum number of objects. Here, an object odominates another object o0,if is no worse than o0in all dimensions, and is better than o0in at least one dimension.
Since the TKD query identifies the most significant objects in an intuitive way, it is a powerful decision making tool to rank objects in many real life applications. Take the typical MovieLens dataset from a movie recommender system (http://www.imdb.com/) as an example.
MovieLens includes a group of movies with the ratings from audiences, where every movie is represented as a multi-dimensional object with each dimension corresponding to a rating in the range of [1, 5] from an audience. Typically, a higher rating indicates a better recognition.
As anexample, given two movies o1= (5, 3, 4) and o2= (3, 3, 2), we understand that there are three audiences scoring o1and o2, where the first audience (w.r.t. the first dimension) scores o1 and o2as 5 and 3 respectively, the second audience (w.r.t.the second dimension) scores both o1and o2as 3, and the third audience (w.r.t. the third dimension) scores o1and o2 as 4 and 2 respectively.
Hence, among three audiences, both the first and the third audiences think o1is better than o2, and the second audience thinks they are equally good.According to the dominance definition, it can derive that, o1 dominates o2, meaning that no audience rates o2higherTop-k Dominating Queries on Incomplete
Top-k Dominating Queries on Incomplete Data
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