Complex Patten Processing in Spatio-temporal Databases

Yang Zheng, Annies Ductan, Devin Thomas, Mohamed Y. Eltabakh

2014

Abstract

The increasing complexity of spatio-temporal applications has caused the underlying queries to be more sophisticated and usually carry complex semantics. As a result, the traditional spatio-temporal query types, e.g., range, kNN, and aggregation queries, have become just building blocks in more complex query plans. In this paper, we present the STEPQ system, which is an extensible spatio-temporal query engine for complex pattern processing over spatio-temporal data. STEPQ enables full-fledged and optimized integration between spatiotemporal queries and complex event processing (CEP). This integration enables expressing complex queries that execute the desired application semantics without the need for indifferent middle-aware or application level support. The system is implemented using TerraLib module on top of PostgreSQL DBMSs. The experimental evaluation demonstrates the feasibility and practicality of the STEPQ system, and the efficiency of the proposed optimizations.

References

  1. Adaikkalavan, R. and Chakravarthy, S. (2003). SnoopIB: Interval-based event specification and detection for active databases. In Proceedings of ADBIS, pages 190-204.
  2. Aguilera, M., Strom, R., Sturman, D., Astley, M., and Chandra, T. (1999). Matching events in a contentbased subscription system. In Proceedings of Principles of Distributed Computing.
  3. Ali, M. H., Mokbel, M. F., and Aref, W. G. (2007). Phenomenon-aware Stream Query Processing. In Proceedings of the International Conference on Mobile Data Management, MDM.
  4. Arasu, A., Babu, S., and Widom, J. (2003). CQL: A language for continuous queries over streams and relations. In DBPL, pages 1-19.
  5. Behr, T. and Guting, R. H. (2005). Fuzzy Spatial Objects: An Algebra Implementation in SECONDO. In In Proceedings of the International Conference on Data Engineering, ICDE, page 1137 1139.
  6. Benetis, R., Jensen, C. S., Karciauskas, G., and Saltenis., S. (2002). Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects. In Proceedings of the International Database Engineering and Applications Symposium, IDEAS, pages 44-53.
  7. Brinkhoff, T. and Str, O. (2002). A framework for generating network-based moving objects. Geoinformatica, 6:2002.
  8. Cai, Y., Hua, K. A., and Cao., G. (2004). Processing Range-Monitoring Queries on Heterogeneous Mobile Objects. In Proceedings of the International Conference on Mobile Data Management, MDM.
  9. Carey, M., Livny, M., and Jauhari, R. (1988). The HiPAC project: Combining active databases and timing constraints. SIGMOD Record, 17(1).
  10. Chakravarthy, S., Krishnaprasad, V., Anwar, E., and Kim, S. (1994). Composite Events for Active Databases: Semantics, Contexts and Detection. In VLDB, pages 606-617.
  11. Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M., Hellerstein, J., Hong, W., and et al. (2003). TelegraphCQ: Continuous dataflow processing for an uncertain world. In CIDR.
  12. Chen, Y. and Patel, J. M. (2007). Efficient Evaluation of All-Nearest-Neighbor Queries. In In Proceedings of the International Conference on Data Engineering, ICDE, page 10561065.
  13. Cheng, R., Zhang, Y., Bertino, E., and Prabhakar., S. (2006). Preserving User Location Privacy in Mobile Data Management Infrastructures. In In Proceedings of Privacy Enhancing Technology Workshop.
  14. Choi, Y.-J. and Chung., C.-W. (2002). Selectivity Estimation for Spatio-temporal Queries to Moving Objects. In Proceedings of the ACM International Conference on Management of Data, SIGMOD, page 440451.
  15. Cugola, G. and Margara, A. (2012). Processing flows of information: From data stream to complex event processing. ACM Comput. Surv., 44(3):15:1-15:62.
  16. Demers, A., Gehrke, J., Hong, M., Riedewald, M., and et al. (2006). Towards expressive publish/subscribe systems. In EDBT, pages 627-644.
  17. Dieker, S. and Guting., R. H. (2000). Plug and Play with Query Algebras: SECONDO- A Generic DBMS Development Environment. In Proceedings of the International Database Engineering and Applications Symposium, IDEAS, page 380392.
  18. Elmongui, H. G., Mokbel, M. F., and Aref., W. G. (2005). Spatio-temporal Histograms. In Proceedings of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, page 1936.
  19. Fabret, F., Jacobsen, H., Llirbat, J., Ross, K., and Shasha, D. (2001). Filtering algorithms and implementation for very fast publish/subscribe systems. In SIGMOD, pages 115-126.
  20. Gedik, B. and Liu., L. (2004). MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System. In Proceedings of the International Conference on Extending Database Technology, EDBT.
  21. Gehani, N., Jagadish, H., and Shmueli, O. (1992). Composite Event Specification in Active Databases: Model and Implementation. In VLDB, pages 327-338.
  22. Hu, H., Xu, J., and Lee., D. L. (2005). A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects. . In Proceedings of the ACM International Conference on Management of Data, SIGMOD, page 479490.
  23. Kanoulas, E., Du, Y., Xia, T., and Zhang., D. (2006). Finding Fastest Paths on A Road Network with Speed Patterns. . In Proceedings of the International Conference on Data Engineering, ICDE.
  24. Lerner, A. and Shasha, D. (2003). AQuery: Query Language for Ordered Data, Optimization Techniques, and Experiments. In VLDB, pages 345-356.
  25. Marios Hadjieleftheriou ad George Kollios, D. G. and Tsotras., V. J. (2003). On-Line Discovery of Dense Areas in Spatio-temporal Databases. . In Proceedings of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, page 306324.
  26. Mokbel, M. F., Xiong, X., and Aref., W. G. (2004a). SINA: Scalable Incremental Processing of Continuous Queries in Spatio-temporal Databases. . In Proceedings of the ACM International Conference on Management of Data, SIGMOD, page 443454.
  27. Mokbel, M. F., Xiong, X., Hammad, M. A., and Aref, W. G. (2004b). Continuous query processing of spatiotemporal data streams in place. In STDBM, pages 57- 64.
  28. Mouratidis, K., Papadias, D., and Papadimitriou., S. (2005). Medoid Queries in Large Spatial Databases. . In Proceedings of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, page 5572.
  29. Mukherji, A., Rundensteiner, E. A., Brown, D. C., and Raghavan, V. (2008). SNIF TOOL: sniffing for patterns in continuous streams. In CIKM, pages 369-378.
  30. Nehme, R. and Rundensteiner., E. (2006). SCUBA: Scalable Cluster-Based Algorithm for Evaluating Continuous Spatio-Temporal Queries on Moving Objects. In Proceedings of the International Conference on Extending Database Technology, EDBT, page 10011019.
  31. Shahabi, C., Kolahdouzan, M. R., and Sharifzadeh., M. (2003). A Road Network Embedding Technique for K-Nearest Neighbor Search in Moving Object Databases. . GeoInformatica, 7(3):255273.
  32. Wolfson, O., Sistla, A. P., Xu, B., Zhou, J., and Chamberlain., S. (1999). DOMINO: Databases for Moving Objects tracking (Demo). In Proceedings of the ACM International Conference on Management of Data, SIGMOD, page 547549.
  33. Wu, E., Diao, Y., and Rizvi, S. (2006). High-performance complex event processing over streams. In Proceedings of the ACM SIGMOD international conference on Management of data, pages 407-418.
  34. Xiao, D. and Eltabakh, M. (2013). STEPQ: Spatio-temporal Engine for Complex Pattern Queries. In International Conference on Advances in Spatial and Temporal Databases (SSTD), pages 386-390.
  35. Xiong, X., Mokbel, M. F., and Aref., W. G. (2005). SEACNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases. . In Proceedings of the IEEE International Conference on Data Engineering, ICDE, page 643654.
  36. Xiong, X., Mokbel, M. F., and Aref., W. G. (2006). LUGrid: Update-tolerant Grid-based Index- ing for Moving Objects. . In Proceedings of the International Conference on Mobile Data Management, MDM, pages 13-21.
  37. Yiu, M. L., Ghinita, G., Jensen, C. S., and Kalnis, P. (2009). Outsourcing Search Services on Private Spatial Data. In ICDE, pages 1140-1143.
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Paper Citation


in Harvard Style

Zheng Y., Ductan A., Thomas D. and Y. Eltabakh M. (2014). Complex Patten Processing in Spatio-temporal Databases . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 157-169. DOI: 10.5220/0004992401570169


in Bibtex Style

@conference{data14,
author={Yang Zheng and Annies Ductan and Devin Thomas and Mohamed Y. Eltabakh},
title={Complex Patten Processing in Spatio-temporal Databases},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={157-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004992401570169},
isbn={978-989-758-035-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Complex Patten Processing in Spatio-temporal Databases
SN - 978-989-758-035-2
AU - Zheng Y.
AU - Ductan A.
AU - Thomas D.
AU - Y. Eltabakh M.
PY - 2014
SP - 157
EP - 169
DO - 10.5220/0004992401570169