On finding safe regions for moving range queries

Abstract The cost of monitoring and keeping the location of a Moving Query updated is very high, as the calculation of the range query needs to be re-evaluated whenever the query moves. Many methods have been proposed to minimize the computation and communication costs for the continuous monitoring of Moving Range Queries. However, because this problem has been only partly solved, more radical efforts are needed. In response, we propose an efficient technique by adopting the concept of a safe region. The safe region is an area where the set of objects of interest does not change. If a moving query is roaming within the safe region then there is no need to update the query. This paper presents efficient techniques to create a competent safe region to reduce the communication costs. We use Monte-Carlo simulation to calculate the area of the safe region due to the irregularity of its shape. As long as the query remains inside its specified safe region, expensive re-computation is not required, which reduces the computational and communication costs in client–server architectures.

[1]  Man Lung Yiu,et al.  A safe-exit approach for efficient network-based moving range queries , 2012, Data Knowl. Eng..

[2]  A. Prasad Sistla,et al.  Modeling and querying moving objects , 1997, Proceedings 13th International Conference on Data Engineering.

[3]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[4]  David Taniar,et al.  Voronoi-based range and continuous range query processing in mobile databases , 2011, J. Comput. Syst. Sci..

[5]  Brian D. Ripley,et al.  Stochastic Simulation , 2005 .

[6]  Eduardo Mena,et al.  A friendly location-aware system to facilitate the work of technical directors when broadcasting sport events , 2012, Mob. Inf. Syst..

[7]  Marios Hadjieleftheriou,et al.  R-Trees - A Dynamic Index Structure for Spatial Searching , 2008, ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems.

[8]  Iraj Mahdavi,et al.  A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths , 2013, Math. Comput. Model..

[9]  David Taniar,et al.  Research in mobile database query optimization and processing , 2005, Mob. Inf. Syst..

[10]  Muhammad Aamir Cheema,et al.  Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks , 2011, The VLDB Journal.

[11]  Maytham Safar,et al.  Approximate range query processing in spatial network databases , 2012, Multimedia Systems.

[12]  David Taniar,et al.  Spatial Network RNN Queries in GIS , 2011, Comput. J..

[13]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, Second Edition , 2000, Wiley Series in Probability and Mathematical Statistics.

[14]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .

[15]  Marek R. Ogiela,et al.  Cognitive Techniques in Visual Data Interpretation , 2009, Studies in Computational Intelligence.

[16]  Christian S. Jensen,et al.  Path prediction and predictive range querying in road network databases , 2010, The VLDB Journal.

[17]  David Taniar,et al.  Voronoi-based multi-level range search in mobile navigation , 2011, Multimedia Tools and Applications.

[18]  Maytham Safar,et al.  Approximate static and continuous range search in mobile navigation , 2011, ICUIMC '11.

[19]  TaniarDavid,et al.  Research in mobile database query optimization and processing , 2005 .

[20]  Muhammad Aamir Cheema,et al.  Continuous Monitoring of Distance-Based Range Queries , 2011, IEEE Transactions on Knowledge and Data Engineering.

[21]  David Taniar,et al.  Approximate algorithms for static and continuous range queries in mobile navigation , 2012, Computing.

[22]  Tae-Sun Chung,et al.  A distributed approach to continuous monitoring of constrained k-nearest neighbor queries in road networks , 2012, Mob. Inf. Syst..

[23]  Nasser Ghadiri,et al.  Optimizing the performance and robustness of type-2 fuzzy group nearest-neighbor queries , 2011 .

[24]  Guilherme Dias da Fonseca,et al.  Approximate Range Searching: The Absolute Model , 2007, WADS.

[25]  Alexandros Nanopoulos,et al.  Continuous range monitoring of mobile objects in road networks , 2008, Data Knowl. Eng..

[26]  David Taniar,et al.  Voronoi-Based Continuous $k$ Nearest Neighbor Search in Mobile Navigation , 2011, IEEE Transactions on Industrial Electronics.

[27]  Yufei Tao,et al.  Location-based spatial queries , 2003, SIGMOD '03.

[28]  Nasser Ghadiri,et al.  Optimizing the performance and robustness of type-2 fuzzy group nearest-neighbor queries , 2011, Mob. Inf. Syst..