Efficient Computation of Group Skyline Queries on MapReduce

Skyline query is one of the important issues indatabase research and has been applied in diverse applicationsincluding multi-criteria decision support systems and so on. Theresponse of a skyline query eliminates unnecessary tuples andreturns only the user-interested result. Traditional skyline querypicks out the outstanding tuples, based on one-to-one recordcomparisons. Some modern applications request, beyond thesingular ones, for superior combinations of records. For example,fantasy basketball is composed of 5 players, fantasy baseball of 9players, and a hackathon of several programmers. Group skylineaims at considering all the groups comprising several records,and finding out the non-dominated ones. Because of the highcomplexity, few studies have been conducted and none has beenpresented in either distributed or parallel computing. This paperis the first study that solves the group skyline in the distributedMapReduce framework. We propose the MRGS algorithm togenerate all the combinations, compute the winners at each localnode, and find out the answer globally. We further propose theMRIGS algorithm to release the bottleneck of MRGS onunbalanced computing load of nodes. Finally, we propose theMRIGS-P algorithm to prune the impossible combinations andproduce indexed and balanced MapReduce computation.Extensive experiments with NBA datasets show that MRIGS-P is6 times faster than the MRGS algorithm

I/O-Efficient Dynamic Planar Range Skyline Queries

We present the first fully dynamic worst case I/O-efficient data structures that support planar orthogonal \textit{3-sided range skyline reporting queries} in \bigO (\log_{2B^\epsilon} n + \frac{t}{B^{1-\epsilon}}) I/Os and updates in \bigO (\log_{2B^\epsilon} n) I/Os, using \bigO (\frac{n}{B^{1-\epsilon}}) blocks of space, for $n$ input planar points, $t$ reported points, and parameter $0 \leq \epsilon \leq 1$. We obtain the result by extending Sundar's priority queues with attrition to support the operations \textsc{DeleteMin} and \textsc{CatenateAndAttrite} in \bigO (1) worst case I/Os, and in \bigO(1/B) amortized I/Os given that a constant number of blocks is already loaded in main memory. Finally, we show that any pointer-based static data structure that supports \textit{dominated maxima reporting queries}, namely the difficult special case of 4-sided skyline queries, in \bigO(\log^{\bigO(1)}n +t) worst case time must occupy $\Omega(n \frac{\log n}{\log \log n})$ space, by adapting a similar lower bounding argument for planar 4-sided range reporting queries.Comment: Submitted to SODA 201