Spatial Keyword Range Search on Trajectories

Transcription

1 Spatial Keyword Range Search on Trajectories Yuxing Han 1, Liping Wang 1, Ying Zhang 2, Wenjie Zhang 3, Xuemin Lin 1,3 1 East China Normal University, China 2 University of Technology, Sydney 3 The University of New South Wales, Sydney

2 Big Trajectory Data Wireless Sensors with Global Position System Driving Route Track Record Social Network with Location-based Service check-in in social network

3 Motivation It is meaningful to search trajectories based on three aspects, i.e., spatio, temporal, textual. query keywords query range {coffee} {pizza} X {} {flower,streak} {mall} {steak} {mall, pizza} {flower} {flower} {flower,steak} {mall, pizza} R4 {steak} {steak} {} {pizza} R2 R3 {steak} R1 {flower,coffee} Y T last month query timespan

4 Related Work Activity Trajectory Similarity Query (ATSQ) [23] Zheng, K., Shang, S., Yuan, N. J., and Yang, Y. Towards efficient search for activity trajectories. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on (2013), IEEE, pp Top-k Spatial Keyword Query (TkSK) on trajectory [6] Cong, G., Lu, H., Ooi, B. C., Zhang, D., and Zhang, M. Efficient spatial keyword search in trajectory databases. arxiv preprint arxiv: (2012).

5 Problem Statement Spatial Keyword Range search on Trajectories (SKRT): Given a query region, a timespan and a set of keywords, we aim to retrieve trajectories that go through this region during query timespan, and contain all the query keywords.

6 Example Q.R : the space within dotted circle Q.T = [ts,te] Q. = {a,c} Trajectories Retrieved: R1, R2 p 34 : {d} p 33 : {c} p 43 p 32 : {a, e} X p 24 : {b} p 23 : {b, c} p 42 : {a} p 14 : {d} p 22 : {a, e} p 13 : {a} p 12 : {b, c} R4 p 41 : {e} p 11 p 31 : {e} p 30 : {c} R3 R2 p 20 : {e} R1 t e t s Y p 10 : {a, d} T t 4 t 3 t 2 t 1 t 0

7 Inverted Octree (IOC-Tree) inverted index octree morton code signature technique

8 Inverted Index WHY follow keyword-first-pruning?

9 Octree & Morton Code octree: 3D analog of quadtree morton code: two nodes with high spatio-temporal proximity are assigned to the same page in the secondary storage T X Y

10 Signature for each node in octree, a signature is maintained to summarize the identifications of the trajectories that go through the corresponding spatio-temporal region. SIG SIG SIG SIG SIG SIG SIG SIG SIG SIG SIG SIG

11 IOC-Tree of Example 111 OC a OC c

12 Algorithm Main Idea: Prune as many trajectories as possible based on spatio, temporal, textual info by using IOC-Tree Prune & Verification Strategy Three types of octree nodes: a) ones locate outside the range, b) fully-covered nodes, c) partially-covered nodes.

13 Pruning Procedure Prune prunes non-promising nodes based on IOC-Tree STRangeFilter: only explore black nodes(i.e., non-empty nodes) deals with nodes level by level among related octrees

14 Verification ProcedureVerification aims at validating candidate trajectories Further validation should load corresponding cell on the disk 111 OC a OC c

15 Extension Spatial Keyword Range search on Trajectories with Order-sensitive keywords (SKRTO)

16 Experiment Implemented in C++ Windows 7 Intel i5 CPU(3.10GHz) 8 GB main memory Three Real Trajectory Datasets: a) two Foursquare check-in datasets from LA and NY b) one geo-tagged tweets dataset Two Baselines: a) GAT (ICDE 13) b) B ck -tree (arxiv 12)

17 Dataset Statistics Experimental Settings

18 Varying number of query keywords

19 Varying query timespan

20 Varying query range

21 Conclusion Propose spatial keyword range search on trajectories Design IOC-Tree structure and query processing algorithm Extensive experiments on real datasets confirm the efficiency of our techniques

22 Thank you!