Title: CCAM: A connectivity-clustered access method for networks and network computations
Authors: Shekhar, S
Liu, DR
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
Keywords: access methods;geographic information systems;network computations;spatial databases;spatial networks
Issue Date: 1-Jan-1997
Abstract: Current Spatial Database Management Systems (SDBMS) provide efficient access methods and operators for point and range queries over collections of spatial points, line segments, and polygons. However, it is not clear if existing spatial access methods can efficiently support network computations which traverse line-segments in a spatial network based on connectivity rather than geographic proximity. The expected I/O cost for many network operations can be reduced by maximizing the Weighted Connectivity Residue Ratio (WCRR), i.e., the chance that a pair of connected nodes that are more likely to be accessed together are allocated to a common page of the file. CCAM is an access method for general networks that uses connectivity clustering, CCAM supports the operations of insert, delete, create, and iind as well as the new operations, get-A-successor and get-successors, which retrieve one or all successors of a node to facilitate aggregate computations on networks. The nodes of the network are assigned to disk pages via a graph partitioning approach to maximize the WCRR. CCAM includes methods for static clustering, as well as dynamic incremental reclustering, to maintain high WCRR in the face of updates, without incurring high overheads. We also describe possible modifications to improve the WCRR that can be achieved by existing spatial access methods. Experiments with network computations on the Minneapolis road map show that CCAM outperforms existing access methods, even though the proposed modifications also substantially improve the performance of existing spatial access methods.
URI: http://dx.doi.org/10.1109/69.567054
http://hdl.handle.net/11536/788
ISSN: 1041-4347
DOI: 10.1109/69.567054
Journal: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume: 9
Issue: 1
Begin Page: 102
End Page: 119
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  1. A1997WQ61200008.pdf