A Comparative Study of Spatial-Temporal Database Trends

Summary A comparative study is presented on the most known k-nearest neighbor search methods used by spatial-temporal database systems in order to provide the advantages and limitations of each algorithm used in system simulations. The scope is limited to the development of the grid indexing searching technique in terms of three different algorithms, including the well-known CPM, SEA-CNN, and CkNN algorithm. These algorithms don’t make any assumptions about the movement of queries or objects. There are a number of functions proposed, which is used in: 1) partitioning the space around the query point in case of CPM and CkNN algorithms and 2) computing minimum and maximum distances between query and cell/level. All studied algorithms are compared together according to the required number of nearest neighbors, grid granularity, location update rate, speed, and population. An accuracy comparison is done between these algorithms to estimate the performance and determine the searching region error during query processing.

[1]  Walid G. Aref,et al.  PLACE: A Query Processor for Handling Real-time Spatio-temporal Data Streams , 2004, VLDB.

[2]  Raghu Ramakrishnan,et al.  Theory of nearest neighbors indexability , 2006, TODS.

[3]  Divyakant Agrawal,et al.  Range and kNN Query Processing for Moving Objects in Grid Model , 2003, Mob. Networks Appl..

[4]  Walid G. Aref,et al.  Scalable continuous query processing in location-aware database servers , 2005 .

[5]  Meng-Han Yang,et al.  Approximate Continuous K Nearest Neighbor Queries for Continuous Moving Objects with Pre-defined Paths , 2005, ER.

[6]  Rimma V. Nehme,et al.  Continuous Query Processing on Spatio-Temporal Data Streams , 2005 .

[7]  Walid G. Aref,et al.  Scalable spatio-temporal continuous query processing for location-aware services , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[8]  Christian S. Jensen,et al.  Multiple k Nearest Neighbor Query Processing in Spatial Network Databases , 2006, ADBIS.

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

[10]  Wei Zhang,et al.  Processing Continuous k -Nearest Neighbor Queries in Location- Dependent Application , 2006 .

[11]  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).

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

[13]  Walid G. Aref,et al.  Efficient Evaluation of Continuous Range Queries on Moving Objects , 2002, DEXA.

[14]  Lei Bing,et al.  MODELING SPATIAL-TEMPORAL DATA IN VERSION-DIFFERENCE MODEL , 2005 .

[15]  Nick Roussopoulos,et al.  K-Nearest Neighbor Search for Moving Query Point , 2001, SSTD.

[16]  Beng Chin Ooi,et al.  Approximate NN queries on Streams with Guaranteed Error/performance Bounds , 2004, VLDB.

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