Indexing Large Moving Objects from Past to Future with PCFI+-Index

moving object databases should handle the past, current and future positions of moving objects efficiently. However, existing indexes such as SEB-Tree, SETI-Tree, 2+3R-Tree, 2- 3RT-Tree and etc. can only provide the capability for past and current query, and the others such as TPR-Tree, and TPR*-Tree can only provide the capability for current and future query. None of them can provide a strategy for indexing the past, current and also the future information of moving objects. In this paper, we present the Past-Current-Future + -Index (PCFI + -Index) which indexes the past, current & future information of the moving objects. The PCFI + -Index builds upon the PCFI-Index which was based on SETI-tree and TPR*-tree. The PCFI + -Index consists of two parts, in memory part with the name frontline, and disk based part. The whole region is partitioned into none-overlapping cells, and a spatial access method is used to index these cells. A set of main-memory TPR*-tree is used to index the moving objects that belong to the cells (one cell, one TPR*-tree). Considering the large update operation triggered by moving objects, the current data file which contains the moving objects' current information is organized as a hash index file. By keeping the restriction in SETI-Index, one page only contains the segments from one cell. Another sparse R*-tree is used to index the lifetimes of the pages. The performance analysis proves that the PCFI + -Index can handle most of the queries efficiently and provides a uniform solution for the trajectory, time-slice, internal and moving queries, and has a better performance than the SETI-Index, TPR*-Index, and PCFI- Index.

[1]  Sharad Mehrotra,et al.  Querying Mobile Objects in Spatio-Temporal Databases , 2001, SSTD.

[2]  Jimeng Sun,et al.  The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries , 2003, VLDB.

[3]  Mong-Li Lee,et al.  Supporting Frequent Updates in R-Trees: A Bottom-Up Approach , 2003, VLDB.

[4]  Yannis Theodoridis,et al.  On the Generation of Spatiotemporal Datasets , 1999 .

[5]  Yannis Theodoridis,et al.  Evaluation of Access Structures for Discretely Moving Points , 1999, Spatio-Temporal Database Management.

[6]  Walid G. Aref,et al.  Spatio-Temporal Access Methods , 2003, IEEE Data Eng. Bull..

[7]  Amit P. Sheth,et al.  Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis , 2003, IEEE Data Eng. Bull..

[8]  Markus Schneider,et al.  A foundation for representing and querying moving objects , 2000, TODS.

[9]  Shin'ichi Satoh,et al.  The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.

[10]  Sukho Lee,et al.  Indexing the current positions of moving objects using the lazy update R-tree , 2002, Proceedings Third International Conference on Mobile Data Management MDM 2002.

[11]  Roy Ladner,et al.  The 2-3TR-tree, a trajectory-oriented index structure for fully evolving valid-time spatio-temporal datasets , 2002, GIS '02.

[12]  Dimitrios Gunopulos,et al.  On indexing mobile objects , 1999, PODS '99.

[13]  BAEHae-young,et al.  Indexing large moving objects from past to future with PCFI , 2004 .

[14]  Nick Roussopoulos,et al.  SEB-tree: An Approach to Index Continuously Moving Objects , 2003, Mobile Data Management.

[15]  Yufei Tao,et al.  Efficient historical R-trees , 2001, Proceedings Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001.

[16]  Kihong Kim,et al.  Optimizing multidimensional index trees for main memory access , 2001, SIGMOD '01.

[17]  Mengchu Cai,et al.  Parametric R-Tree: An Index Structure for Moving Objects , 2000 .

[18]  Christian S. Jensen,et al.  Indexing the positions of continuously moving objects , 2000, SIGMOD '00.

[19]  Jignesh M. Patel,et al.  Indexing Large Trajectory Data Sets With SETI , 2003, CIDR.

[20]  Dieter Pfoser.  Indexing the Trajectories of Moving Objects , 2002 .

[21]  Hung-Yi Lin.  Indexing the Trajectories of Moving Objects , .

[22]  Dieter Pfoser,et al.  Novel Approaches in Query Processing for Moving Object Trajectories , 2000, VLDB 2000.

[23]  Yufei Tao,et al.  MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries , 2001, VLDB.