An adaptive updating protocol for reducing moving object database workload

In the last decade, spatio-temporal database research focuses on the design of effective and efficient indexing structures in support of location-based queries such as predictive range queries and nearest neighbor queries. While a variety of indexing techniques have been proposed to accelerate the processing of updates and queries, not much attention has been paid to the updating protocol, which is another important factor affecting the system performance. In this paper, we propose a generic and adaptive updating protocol for moving object databases with less number of updates between objects and the database server, thereby reducing the overall workload of the system. In contrast to the approach adopted by most conventional moving object database systems where the exact locations and velocities last disclosed are used to predict their motions, we propose the concept of Spatio-temporal safe region to approximate possible future locations. Spatio-temporal safe regions provide larger space of tolerance for moving objects, freeing them from location and velocity updates as long as the errors remain predictable in the database. To answer predictive queries accurately, the server is allowed to probe the latest status of objects when their safe regions are inadequate in returning the exact query results. Spatio-temporal safe regions are calculated and optimized by the database server with two contradictory objectives: reducing update workload while guaranteeing query accuracy and efficiency. To achieve this, we propose a cost model that estimates the composition of active and passive updates based on historical motion records and query distribution. More system performance improvements can be obtained by cutting more updates from the clients, when the users of system are comfortable with incomplete but accuracy bounded query results. We have conducted extensive experiments to evaluate our proposal on a variety of popular indexing structures. The results confirm the viability, robustness, accuracy and efficiency of our proposed protocol.

[1]  Christian S. Jensen,et al.  A benchmark for evaluating moving object indexes , 2008, Proc. VLDB Endow..

[2]  Xiaohui Yu,et al.  Monitoring k-nearest neighbor queries over moving objects , 2005, 21st International Conference on Data Engineering (ICDE'05).

[3]  Beng Chin Ooi,et al.  Effectively Indexing Uncertain Moving Objects for Predictive Queries , 2009, Proc. VLDB Endow..

[4]  Walid G. Aref,et al.  SINA: scalable incremental processing of continuous queries in spatio-temporal databases , 2004, SIGMOD '04.

[5]  Jianliang Xu,et al.  A generic framework for monitoring continuous spatial queries over moving objects , 2005, SIGMOD '05.

[6]  Yin Yang,et al.  Continuous k-Means Monitoring over Moving Objects , 2008, IEEE Transactions on Knowledge and Data Engineering.

[7]  Yufei Tao,et al.  Venn sampling: a novel prediction technique for moving objects , 2005, 21st International Conference on Data Engineering (ICDE'05).

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

[9]  Wei Wu,et al.  Distributed Processing of Moving K-Nearest-Neighbor Query on Moving Objects , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[10]  A. Prasad Sistla,et al.  Updating and Querying Databases that Track Mobile Units , 1999, Distributed and Parallel Databases.

[11]  Kyriakos Mouratidis,et al.  Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring , 2005, SIGMOD '05.

[12]  Beng Chin Ooi,et al.  Query and Update Efficient B+-Tree Based Indexing of Moving Objects , 2004, VLDB.

[13]  Anthony K. H. Tung,et al.  Minimizing the communication cost for continuous skyline maintenance , 2009, SIGMOD Conference.

[14]  Vijay V. Vazirani,et al.  Primal-Dual RNC Approximation Algorithms for Set Cover and Covering Integer Programs , 1999, SIAM J. Comput..

[15]  Thomas Brinkhoff,et al.  A Framework for Generating Network-Based Moving Objects , 2002, GeoInformatica.

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

[17]  Yufei Tao,et al.  The Bdual-Tree: indexing moving objects by space filling curves in the dual space , 2008, The VLDB Journal.

[18]  Beng Chin Ooi,et al.  Efficient indexing of the historical, present, and future positions of moving objects , 2005, MDM '05.

[19]  Walid G. Aref,et al.  SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases , 2005, 21st International Conference on Data Engineering (ICDE'05).

[20]  Christos Faloutsos,et al.  Prediction and indexing of moving objects with unknown motion patterns , 2004, SIGMOD '04.

[21]  Ling Liu,et al.  MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System , 2004, EDBT.

[22]  Beng Chin Ooi,et al.  ST2B-tree: a self-tunable spatio-temporal b+-tree index for moving objects , 2008, SIGMOD Conference.

[23]  Tiko Kameda,et al.  The R-Link Tree: A Recoverable Index Structure for Spatial Data , 1994, DEXA.

[24]  Walid G. Aref,et al.  LUGrid: Update-tolerant Grid-based Indexing for Moving Objects , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[25]  Beng Chin Ooi,et al.  An adaptive updating protocol for reducing moving object database workload , 2010, Proc. VLDB Endow..

[26]  Michael J. Carey,et al.  Performance of B+ tree concurrency control algorithms , 1993, The VLDB Journal.