Parallel Distributed Processing of Constrained Skyline Queries by Filtering

Bin Cui , Hua Lu , Quanqing Xu , Lijiang Chen , Yafei Dai , Yongluan ZhouICDE 08Parallel Distributed Processing of Constrained Skyline Queries by Filtering1OutlineIntroductionProblem DefinitionParallel Distributed Skyline ProcessingExperimental StudyConclusion2IntroductionDistributed computing environments is consisting of different computers. Sorg directly communicates with any other site(Computer). Each site(Computer) can compute at the same time (Parallel).For instance, multiple stock information databases available at different places like New York Stock Exchange, London Stock Exchange, Tokyo Stock Exchange, etc. For each single stock, the agent needs to take into consideration multiple attributes. Therefore, a skyline query against those distributed databases will help the agent get those interesting stocks.3

Problem Definition

Sorg directly communicates with any other site Si .

D : {p(2,6),q(2,4),r(3,3)} , q and r are not dominated. Skyline of D:{q , r }

4Parallel Distributed Skyline ProcessingComputing local skyline and rMBRs in parallel.Parallel Distributed Query ExecutionMerge.5(Cont.)Computing local skyline and rMBRs in paralle Green Block: MBRSkyline: {(1,4),(3,3),(5,2)})

6(Cont.)Blue Block: skyline and rMBB (reduce MBB). rMBB only includes local skyline.{(1,4), (3,3),(5,2)}.

7

(Cont.)Parallel Distributed Query ExecutionEach site has a rMBB and local skyline set, and rMBB is represented by two points, the lower left corner rMBB.min and its uper right corner rMBB.max

8(Cont.)

rMBB 1rMBB 2rMBB 2.minrMBB 2.min.DRrMBB 1.minrMBB 1.min.DR9(Cont.)Execution plan: partitioning : Incomparable

Partitioned into:{{A},{B,C,D,E}{F,G}}

10(Cont.)Though B and D are incomparable, they are assigned to the same group with C and E, because either of them are not incomparable with C (and E).

11(Cont.)Pick filtering point: 1.Distance of each filtering point is max(MaxDist): dominating region of each filtering point has small overlap. 2. filtering points Dominating Region is max(MaxSum): dominating region of each filtering point is larege. 3.Random12(Cont.)Assume 2 filtering point. Max distance: choose (1,5),(6,2)

13(Cont.)Assume 2 filtering point. Max Dominating Region: Choose (2,4) and (4,3)

(1,5) (2,4):4(1,5) (4,3):4(1,5) (6,2):0(2,4) (4,3):6(2,4) (6,2):4(4,3) (6,2):4

Max14(Cont.)

15(Cont.)Computing local skylines and rMBBs in parallel.

16(Cont.)

17(Cont.)Partitioned into {{A,B},{C,D}}

rMBBIncomparablecomparableAC,DBBC,DACA,BDDA,BC18(Cont.)Assume 1 filtering point:A:pick(2,4)(Dominating Region: (1,5):0,(2,4):2,(4,3):0

(2,4) compares with Bs (2,4) dominates (2,7),(5,4)Skyline of Partition {A,B}:{(1,5),(2,4),(4,3)}

19(Cont.)Assume 1 filtering point:C:pick(6,2)

(6,2) compares with Ds (6,2) dominates (8,2)(10,1) compares with Ds(10,0)(10,1) is dominated by (10,0)

Skyline of Partition{C,D}:{(6,2),(10,0)}

20(Cont.)Merge Skyline of{A,B},{C,D}: {(1,5),(2,4),(4,3) ,(6,2),(10,0)}

21Experimental StudyIndependent Datasets

22(Cont.)AntiCorrelated Datasets

23(Cont.)NBA Dataset

24(Cont.)Performance with Different Numbers of Filtering Points

25ConclusionThe Percentage of FIlter Points:10% is better.MaxSum is better than MaxDist and Random2626