The Flexible Socio Spatial Group Queries Bishwamittra Ghosh 1 , - PowerPoint PPT Presentation

The Flexible Socio Spatial Group Queries Bishwamittra Ghosh 1 , Mohammed Eunus Ali 2 , Farhana M. Choudhury 3 , Sajid Hasan Apon 2 , Timos Sellis 4 , Jianxin Li 5 VLDB 2019 1 National University of Singapore 2 Bangladesh University of Engineering and Technology 3 RMIT University and University of Melbourne, Australia 4 Swinburne University of Technology, Australia 5 The University of Western Australia Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 1

Socio-spatial Graph b a social layer c d e o 2 o 1 spatial layer Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 2

Problem Formulation Given ◮ Set of meeting points Q ◮ Socio-spatial graph G = ( V , E ) Find top k groups such that score( G i , q i ) ≥ score( G i +1 , q i +1 ) where G i is a subgraph of G , q i ∈ Q and 1 ≤ i ≤ k − 1 Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 3

Constraints for a feasible group G i = ( V , E ) ◮ minimum social connectivity constraint c ◮ degree( v ) ≥ c , ∀ v ∈ V ◮ maximum distance d max ◮ dist( v , q ) ≤ d max , ∀ v ∈ V ◮ minimum group size n min , maximum group size n max ◮ n min ≤ | V | ≤ n max Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 4

Score of group G i = ( V , E ) w.r.t. meeting point q 2 | E | score social = | V | ( | V | − 1) � v ∈ V dist( v , q ) score spatial = 1 − d max | V | score size = | V | n max score = α · score social + β · score spatial + γ · score size Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 5

Literature review There are existing works that address socio spatial group queries. The major gaps are ◮ specific group size 6 vs variable group size ◮ finding only the best group 6 vs top k groups ◮ fixed meeting point vs multiple meeting points 7 ◮ average social connectivity constraint 8 vs minimum social connectivity constraint 9 ◮ ranking function combining social and spatial factors 10 vs ranking function combining social, spatial and group size factors 6 [Fang17], [Shen16], [Zhu14],[Yang12] 7 [Shen16] 8 [Shen16], [Yang12] 9 [Fang17],[Zhu14] 10 [Armenatzoglou15] Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 6

Contribution ◮ Exact algorithm ◮ member ordering based on spatial distance ◮ optimistic assumption (maximum) on social connectivity of including members ◮ early termination based on upper bound on spatial distance n max n min Intermediate group Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 7

Continued . . . ◮ Heuristic approximate approach ◮ member ordering based on spatial distance ◮ lower bound on social connectivity while including a member in the intermediate group Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 8

Continued . . . ◮ A fast approximate approach ◮ a tighter lower bound on social connectivity while including a member in the intermediate group ◮ upper bound on spatial distance and lower bound on social connectivity that improves the rank of current exploring group ◮ prune when including a member can not increase the score of intermediate group ◮ Greedy approach ◮ avoid backtracking Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 9

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . select meeting point that has minimum spatial distance to first unexplored member Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . { a , b , c } is a result group Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Advance termination based on upper bound on spatial distance Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . degree( c , { a } ) < lower bound on social connectivity Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . degree( c , { b } ) ≥ lower bound on social connectivity Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Simulation ◮ meeting point q 1 ◮ meeting point q 2 ◮ distance ordered members ◮ distance ordered members { a , b , c , d . . . } { b , a , c , . . . } ∅ ∅ . . . a b b X a , b a , d b , c b , a b , c . . . . . . a , b , c . . . . . . Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 10

Approximation ratio of fast approximate algorithm lowest scoring retrieved group approximation ratio = best scoring group that may not be retrived Emphasis Weights Approximation ratio c Social score α = 1 , β = γ = 0 n max − 1 Spatial score β = 1 , α = γ = 0 1 n min Size score γ = 1 , α = γ = 0 n max Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 11

Experimental Results B = Baseline 11 , E = Exact, A = Approximate, FA = Fast approximate, GA = Greedy approximate E B A E 300 FA A GA FA 10 6 GA 250 Time (ms) Time (ms) 10 5 200 10 4 150 10 3 10 2 100 50 100 200 400 50 100 200 400 # of meeting points # of meeting points (a) Brightkite (b) Gowalla Figure: Computation time of different algorithm 11 [YANG12] Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 12

Experimental Results A = Approximate, FA = Fast approximate, GA = Greedy approximate k(A) k (FA) k (GA) k(A) k (FA) k (GA) 1.5k(A) 1.5k (FA) 1.5k (GA) 1.5k(A) 1.5k (FA) 1.5k (GA) 2k(A) 2k (FA) 2k (GA) 2k(A) 2k (FA) 2k (GA) 100 100 Accuracy (%) Accuracy (%) 80 80 60 60 40 40 20 20 0 0 50 100 200 400 50 100 200 400 # of meeting points # of meeting points (a) Brightkite (b) Gowalla Figure: Percentage of groups in top k of approximate algorithm that also appear in top k , top 1 . 5 k , and top 2 k of the exact algorithm Bishwamittra Ghosh The Flexible Socio Spatial Group Queries VLDB 2019 13