Exerting spatial join and KNN queries on spatial database

Spatial database system as a database system that offers spatial data types in its data model and query language and supports spatial data types in its implementation, providing at least spatial indexing and spatial join methods. Spatial database applications, such as Geographical Information Systems (GIS), typically use R-tree variants to index geographical data. Spatial Joins are important operations in applications such as GIS, Cartography and CAD/CAM. Spatial Join is very useful technique for wide spread implementation of R-trees as Spatial index structures. Proposed an algorithm based on R-tree to perform the operation of spatial join for spatial objects in multi-user environment. K-Nearest Neighbor (k-NN) queries are used in GIS and CAD/CAM applications to find the k spatial objects closest to some given query point. Quickly executing k-Nearest-Neighbor (kNN) in spatial database applications requires an informative and efficient index structure that can effectively reduce the search space. Proposed method implements extension to R-trees that uses object classifications to reduce the search space of kNN queries in multi-user environment.

[1]  P. Burrough.  Principles of Geographical Information Systems for Land Resources Assessment , 1986 .

[2]  Derek Thompson,et al.  Fundamentals of spatial information systems , 1992, A.P.I.C. series.

[3]  Chang-Tien Lu,et al.  A Concurrency Control Protocol for Continuously Monitoring Moving Objects , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[4]  P. Pizor.  Principles of Geographical Information Systems for Land Resources Assessment. , 1987 .

[5]  Chang-Tien Lu,et al.  GLIP: A Concurrency Control Protocol for Clipping Indexing , 2009, IEEE Transactions on Knowledge and Data Engineering.

[6]  A OrensteinJack.  Spatial query processing in an object-oriented database system , 1986 .

[7]  Ming-Ling Lo,et al.  The Design and Implementation of Seeded Trees: An Efficient Method for Spatial Joins , 1998, IEEE Trans. Knowl. Data Eng..

[8]  Yannis Manolopoulos,et al.  Algorithms for processing K-closest-pair queries in spatial databases , 2004, Data Knowl. Eng..

[9]  Elke A. Rundensteiner,et al.  Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations , 1997, VLDB.

[10]  Gustavo Rossi,et al.  Designing adaptable geographic objects for mobile applications , 2003, Fourth International Conference on Web Information Systems Engineering Workshops, 2003. Proceedings..

[11]  Ming-Ling Lo,et al.  Spatial joins using seeded trees , 1994, SIGMOD '94.

[12]  David J. DeWitt,et al.  Partition based spatial-merge join , 1996, SIGMOD '96.

[13]  V RavishankarChinya,et al.  Spatial joins using seeded trees , 1994 .

[14]  Yannis Manolopoulos,et al.  Closest pair queries in spatial databases , 2000, SIGMOD '00.

[15]  E. Rundensteiner,et al.  BFRJ: Global Optimization of Spatial Joins Using R-trees , 1997 .

[16]  J DeWittDavid,et al.  Partition based spatial-merge join , 1996 .

[17]  Feifei Li,et al.  K nearest neighbor queries and kNN-Joins in large relational databases (almost) for free , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).