Abstract
Central to many location-based service applications is the task of processing k-nearest neighbor (k-NN) queries over moving objects. Many existing approaches adapt different index structures and design various search algorithms to deal with this problem. In these works, tree-based indexes and grid index are mainly utilized to maintain a large volume of moving objects and improve the performance of search algorithms. In fact, tree-based indexes and grid index have their own flaws for supporting processing k-NN queries over an ocean of moving objects. A treebased index (such as R-tree) needs to constantly maintain the relationship between nodes with objects continuously moving, which usually causes a high maintenance cost. Grid index is although widely used to support k-NN queries over moving objects, but the approaches based on grid index almost require an uncertain number of iterative calculations, which makes the performance of these approaches be not predictable. To address this problem, we present a dynamic Strip-Rectangle Index (SRI), which can reach a good balance of the maintenance cost and the performance of supporting k-NN queries over moving objects. SRI supplies two different index granularities that makes it better adapt to handle different data distributions than existing index structures. Based on SRI, we propose a search algorithm called SR-KNN that can rapidly calculate a final region space with a filter-and-refine strategy to enhance the efficiency of process k-NN queries, rather than iteratively enlarging the search space like the approaches based on grid index. Finally, we conduct experiments to fully evaluate the performance of our proposal.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
K. L. Cheung and A. W.-C. Fu, “Enhanced nearest neighbour search on the r-tree,” ACM SIGMOD Record, vol. 27, no. 3, pp. 16–21, 1998.
A. Guttman, “R-trees: a dynamic index structure for spatial searching,” in SIGMOD, 1984, pp. 47–57.
X. Yu, K. Pu, and N. Koudas, “Monitoring k-nearest neighbor queries over moving objects,” in ICDE, 2005, pp. 631–642.
K. Mouratidis, D. Papadias, and M. Hadjieleftheriou, “Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring,” in SIGMOD, 2005, pp. 634–645.
M. Cheema, “CircularTrip and arctrip: Effective grid access methods for continuous spatial queries,” in DASFAA, 2007, pp. 863–869.
Y. Tao, D. Papadias, and Q. Shen, “Continuous nearest neighbor search,” in VLDB, 2002, pp. 287–298.
K. Mouratidis and D. Papadias, “Continuous nearest neighbor queries over sliding windows,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 6, pp. 789–803, 2007.
M. S. H. A.-K. Sultan Alamri, David Taniar, “Tracking moving objects using topographical indexing,” Concurrency and Computation: Practice and Experience, 27(8): 1951-1965, 2015.
K. Raptopoulou, A. Papadopoulos, and Y. Manolopoulos, “Fast nearest-neighbor query processing in moving-object databases,” GeoInformatica, vol. 7, no. 2, pp. 113–137, 2003.
T. Seidl and H. Kriegel, “Optimal multi-step k-nearest neighbor search,” in SIGMOD, 1998, pp. 154–165.
S. Chaudhuri and L. Gravano, “Evaluating top-k selection queries,” in VLDB, 1999, pp. 399–410.
B. Gedik, K. Wu, P. Yu, and L. Liu, “Processing moving queries over moving objects using motion-adaptive indexes,” Knowledge and Data Engineering, vol. 18, no. 5, pp. 651–668, 2006.
C. Yu, B. Ooi, K. Tan, and H. Jagadish, “Indexing the distance: An efficient method to knn processing,” in VLDB, 2001, pp. 421–430.
B. Zheng, J. Xu, W.-C. Lee, and L. Lee, “Grid-partition index: a hybrid method for nearest-neighbor queries in wireless location-based services,” The VLDB Journal, vol. 15, no. 1, pp. 21–39, 2006.
K. Mouratidis, S. Bakiras, and D. Papadias, “Continuous monitoring of spatial queries in wireless broadcast environments,” IEEE Transactions on Mobile Computing, vol. 8, no. 10, pp. 1297–1311, 2009.
M. F. Mokbel, X. Xiong, and W. G. Aref, “SINA: scalable incremental processing of continuous queries in spatio-temporal databases,” in SIGMOD, 2004, pp. 623–634.
X. Xiong, M. F. Mokbel, and W. G. Aref, “SEA-CNN: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases,” in ICDE, 2005, pp. 643–654.
D. Šidlauskas, S. Šaltenis, and C. S. Jensen, “Parallel main-memory indexing for moving-object query and update workloads,” in SIGMOD, 2012, pp. 37–48.
H. Wang and R. Zimmermann, “Snapshot location-based query processing on moving objects in road networks,” in SIGSPATIAL GIS, 2008, pp. 50:1–50:4.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yu, Z., Chen, Y., Ma, K. (2017). SR-KNN: An Real-time Approach of Processing k-NN Queries over Moving Objects. In: Xhafa, F., Barolli, L., Amato, F. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-49109-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49108-0
Online ISBN: 978-3-319-49109-7
eBook Packages: EngineeringEngineering (R0)