Evaluation Of Shortest Path Query Algorithm In Spatial Databases Electronic Resource
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Evaluation of Shortest Path Query Algorithm in Spatial Databases [electronic Resource]
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Many variations of algorithms for finding the shortest path in a large graph have been introduced recently due to the needs of applications like the Geographic Information System (GIS) or Intelligent Transportation System (ITS). The primary subjects of those algorithms are materialization and hierarchical path views. Some studies focus on the materialization and sacrifice the pre-computational costs and storage costs for faster computation of a query. Other studies focus on the shortest-path algorithm, which has less pre-computation and storage but takes more time to compute the shortest path. The main objective of this thesis is to accelerate the computation time for the shortest-path queries while keeping the degree of materialization as low as possible. This thesis explores two different categories: 1) the reduction of the I/O-costs for multiple queries, and 2) the reduction of search spaces in a graph. The thesis proposes two simple algorithms to reduce the I/O-costs, especially for multiple queries. To tackle the problem of reducing search spaces, we give two different levels of materializations, namely, the boundary set distance matrix and x-Hop sketch graph, both of which materialize the shortest-path view of the boundary nodes in a partitioned graph. Our experiments show that a combination of the suggested solutions for 1) and 2) performs better than the original Disk-based SP algorithm [7], on which our work is based, and requires much less storage than HEPV [3].
Shortest Path Queries in Very Large Spatial Databases [electronic Resource]
Finding the shortest paths in a graph has been studied for a long time, and there are many main memory based algorithms dealing with this problem. Among these, Dijkstra's shortest path algorithm is one of the most commonly used efficient algorithms to the non-negative graphs. Even more efficient algorithms have been developed recently for graphs with particular properties such as the weights of edges fall into a range of integer. All of the mentioned algorithms require the graph totally reside in the main memory. Howevery, for very large graphs, such as the digital maps managed by Geographic Information Systems (GIS), the requirement cannot be satisfied in most cases, so the algorithms mentioned above are not appropriate. My objective in this thesis is to design and evaluate the performance of external memory (disk-based) shortest path algorithms and data structures to solve the shortest path problem in very large digital maps. In particular the following questions are studied: What have other researchers done on the shortest path queries in very large digital maps? What could be improved on the previous works? How efficient are our new shortest paths algorithms on the digital maps, and what factors affect the efficiency? What can be done based on the algorithm? In this thesis, we give a disk-based Dijkstra's-like algorithm to answer shortest path queries based on pre-processing information. Experiments based on our Java implementation are given to show what factors affect the running time of our algorithms.
Spatial and Temporal Databases
This book constitutes the refereed proceedings of the 13th International Symposium on Spatial and Temporal Databases, SSTD 2013, held in Munich, Germany, in August 2013. The 24 revised full papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on joins and algorithms; mining and discovery; indexing; trajectories and road network data; nearest neighbours queries; uncertainty; and demonstrations.