Scalable Query Processing on Spatial Networks

Spatial networks (e.g., road networks) are general graphs with spatial information (e.g., latitude/longitude) information associated with the vertices and/or the edges of the graph. Techniques are presented for query processing on spatial networks that are based on the observed coherence between the spatial positions of the vertices and the shortest paths between them. This facilitates aggregation of the vertices into coherent regions that share vertices on the shortest paths between them. Using this observation, a framework, termed SILC, is introduced that precomputes and compactly encodes the N^2 shortest path and network distances between every pair of vertices on a spatial network containing N vertices. The compactness of the shortest paths from source vertex V is achieved by partitioning the destination vertices into subsets based on the identity of the first edge to them from V. The spatial coherence of these subsets is captured by using a quadtree representation whose dimension-reducing property enables the storage requirements of each subset to be reduced to be proportional to the perimeter of the spatially coherent regions, instead of to the number of vertices in the spatial network. In particular, experiments on a number of large road networks as well as a theoretical analysis have shown that the total storage for the shortest paths has been reduced from O(N^3) to O(N^1.5). In addition to SILC, another framework, termed PCP, is proposed that also takes advantage of the spatial coherence of the source vertices and makes use of the Well Separated Pair decomposition to further reduce the storage, under suitably defined conditions, to O(N). Using these frameworks, scalable algorithms are presented to implement a wide variety of operations such as nearest neighbor finding and distance joins on large datasets of locations residing on a spatial network. These frameworks essentially decouple the process of computing shortest paths from that of spatial query processing as well as also decouple the domain of the participating objects from the domain of the vertices of the spatial network. This means that as long as the spatial network is unchanged, the algorithm and underlying representation of the shortest paths in the spatial network can be used with different sets of objects

Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings

In the past few years, the growth of urban area has been increasing and has resulted immense number of urban datasets. This situation contributes to the difficulties in handling and managing issues related to urban area. Huge and massive datasets can degrade the performance of data retrieval and information analysis. In addition, urban environments are very difficult to manage because they involved with various types of data, such as multiple types of zoning themes in urban mixeduse development. Thus, a special technique for efficient data handling and management is necessary. In this study, a new three-dimensional (3D) spatial access method, the Voronoi Classified and Clustered Data Constellation (VOR-CCDC) is introduced. The VOR-CCDC data structure operates on the basis of two filters, classification and clustering. To boost up the performance of data retrieval, VORCCDC offers a minimal percentage of overlap among nodes and a minimal coverage area in order to avoid repetitive data entry and multi-path queries. Besides that, VOR-CCDC data structure is supplemented with an extra element of nearest neighbour information. Encoded neighbouring information in the Voronoi diagram allows VOR-CCDC to optimally explore the data. There are three types of nearest neighbour queries that are presented in this study to verify the VOR-CCDC’s ability in finding the nearest neighbour information. The queries are Single Search Nearest Neighbour query, k Nearest Neighbour (kNN) query and Reverse k Nearest Neighbour (RkNN) query. Each query is tested with two types of 3D datasets; single layer and multi-layer. The test demonstrated that VOR-CCDC performs the least amount of input/output than their best competitor, the 3D R-Tree. Besides that, VOR-CCDC is also tested for performance evaluation. The results indicate that VOR-CCDC outperforms its competitor by responding 60 to 80 percent faster to the query operation. In the future, VOR-CCDC structure is expected to be expanded for temporal and dynamic objects. Besides that, VOR-CCDC structure can also be used in other applications such as brain cell database for analysing the spatial arrangement of neurons or analysing the protein chain reaction in bioinformatics applications

Depth-First K-Nearest Neighbor Finding Using the MaxNearestDist Estimator £

A description is given of how to use an estimate of the maximum possible distance at which a nearest neighbor can be found to prune the search process in a depth-first branch and bound k-nearest neighbor finding algorithm.