Real-time processing of k-NN queries over moving objects
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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 tree-based 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 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 not predictable. To address this problem, we present a dynamic Strip Rectangle Index (SRI), which can reach a good balance between 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 with a filter-and-refine strategy to enhance the efficiency of process k-NN queries, rather than iteratively enlarging the search space like the grid-index-based approaches. Finally, we conduct experiments to fully evaluate the performance of our proposal.
Keywordsk-NN queries Spatial–temporal data Search algorithm
This work was supported in part by the Shandong Provincial Natural Science Foundation (ZR2016FB14), the Shandong Provincial Natural Science Foundation (ZR2014FQ029), the Shandong Provincial Key R&D Program (2015GGX106007), and the Project of Shandong Province Higher Educational Science and Technology Program (J16LN13)
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Chaudhuri S, Gravano L (1999) Evaluating top-k selection queries. In: VLDB, pp 399–410Google Scholar
- Cui C, Shen J, Ma J, Lian T (2016) Social tag relevance learning via ranking-oriented neighbor voting. Multimedia Tools Appl 1–27Google Scholar
- Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: SIGMOD, pp 47–57Google Scholar
- Mokbel MF, Xiong X, Aref WG (2004) SINA: scalable incremental processing of continuous queries in spatio-temporal databases. In: SIGMOD, pp 623–634Google Scholar
- Nehme RV, Rundensteiner EA (2006) SCUBA: scalable cluster-based algorithm for evaluating continuous spatio-temporal queries on moving objects. In: EDBT, pp 1001–1019Google Scholar
- Seidl T, Kriegel H (1998) Optimal multi-step k-nearest neighbor search. In: SIGMOD, pp 154–165Google Scholar
- Šidlauskas D, Šaltenis S, Jensen CS (2012) Parallel main-memory indexing for moving-object query and update workloads. In: SIGMOD, pp 37–48Google Scholar
- Tao Y, Papadias D, Shen Q (2002) Continuous nearest neighbor search. In: VLDB, pp 287–298Google Scholar
- Wang H, Zimmermann R (2008) Snapshot location-based query processing on moving objects in road networks. In: SIGSPATIAL GIS, pp 50:1–50:4Google Scholar
- Xiong X, Mokbel MF, Aref WG (2005) SEA-CNN: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE, pp 643–654Google Scholar
- Yang M, Liu Y, Yu Z (2015) Distributed grid-based k nearest neighbour query processing over moving objects. In: International Conference on Web-Age Information Management, pp 350–361Google Scholar
- Yu C, Ooi B, Tan K, Jagadish H (2001) Indexing the distance: an efficient method to knn processing. In: VLDB, pp 421–430Google Scholar
- Yu X, Pu K, Koudas N (2005) Monitoring k-nearest neighbor queries over moving objects. In: ICDE, pp 631–642Google Scholar
- Zheng B, Zheng K, Xiao X, Su H, Yin H, Zhou X, Li G (2016) Keyword-aware continuous KNN query on road networks. In: ICDE, pp 871–882Google Scholar