Showing papers by "Nikos Mamoulis published in 1999"

Processing and optimization of multiway spatial joins using R-trees

Abstract: One of the most important types of query processing in spatial databases and geographic information systems is the spatial join, an operation that selects, from two relations, all object pairs satisfying some spatial predicate. A multiway join combines data originated from more than two relations. Although several techniques have been proposed for pairwise spatial joins, only limited work has focused on multiway spatial join processing. This paper solves multiway spatial joins by applying systematic search algorithms that exploit R-trees to efficiently guide search, without building temporary indexes or materializing intermediate results. In addition to general methodologies, we propose cost models and an optimization algorithm, and evaluate them through extensive experimentation.

Integration of spatial join algorithms for processing multiple inputs

Abstract: Several techniques that compute the join between two spatial datasets have been proposed during the last decade. Among these methods, some consider existing indices for the joined inputs, while others treat datasets with no index, providing solutions for the case where at least one input comes as an intermediate result of another database operator. In this paper we analyze previous work on spatial joins and propose a novel algorithm, called slot index spatial join (SISJ), that efficiently computes the spatial join between two inputs, only one of which is indexed by an R-tree. Going one step further, we show how SISJ and other spatial join algorithms can be implemented as operators in a database environment that joins more than two spatial datasets. We study the differences between relational and spatial multiway joins, and propose a dynamic programming algorithm that optimizes the execution of complex spatial queries.

Content-based retrieval using heuristic search

Abstract: The fast growth of multimedia information in image and video databases has triggered research on efficient retrieval methods. This paper deals with structural queries, a type of content-based retrieval where similarity is not defined on visual properties such as color and texture, but on object relations in space. We propose the application of heuristic algorithms which provide good, but not necessarily optimal, solutions in a pre-determined time period, and compare our approach with systematic search methods which are guaranteed to find optimal solutions but require exponential time in the worst case. The quality of the output is calculated using a relation framework which is an extension of Allen’s relations. With this framework our methods can be applied in multiple resolutions and dimensions, thus covering a wide range of applications in spatial, multimedia and video systems.

Hierarchical constraint satisfaction in spatial databases

Abstract: Several content-based queries in spatial databases and geographic infonnation systems (GISs) can be modelled and processed as constraint satisfaction problems (CSPs). Regular CSP algorithms, however, work for main memory retrieval without utilizing indices to prune the search space. This paper shows how systematic and local search techniques can take advantage of the hierarchical decomposition of space, preserved by spatial data structures, to efficiently guide search. We study the conditions under which hierarchical constraint satisfaction outperfonns traditional methods with extensive experimentation.

Integration of Spatial Join Algorithms for Joining Multiple Inputs

Abstract: Several techniques that compute the join between two spatial datasets have been proposed during the last decade. Among these methods, some consider existing indices for the joined inputs, while others treat datasets with no index, thus providing solutions for the case where at least one input comes as an intermediate result of another database operator. In this paper we analyze previous work on spatial joins and propose a novel algorithm, called slot index spatial join (SISJ), that eff iciently computes the spatial join between two inputs, only one of which is indexed by an R-tree. Going one step further, we show how SISJ and other spatial join algorithms can be implemented as operators in a database environment that joins more than two spatial inputs. We study the differences between relational and spatial multi -way joins, and propose a dynamic programming algorithm that optimizes the execution of complex spatial queries. Contact Author: Dimitris Papadias Tel: ++852-23586971 http://www.cs.ust.hk/~dimitris/ Fax: ++852-23581477 E-mail: dimitris@cs.ust.hk The Hong Kong University of Science & Technology Technical Report Series Department of Computer Science Integration of Spatial Join Algorithms for Joining Multiple Inputs Nikos Mamoulis and Dimitris Papadias Technical Report HKUST-CS98-15 November, 1998

Improving search using indexing: a study with temporal CSPs

Abstract: Most studies concerning constraint satisfaction problems (CSPs) involve variables that take values from small domains. This paper deals with an alternative form of temporal CSPs; the number of variables is relatively small and the domains are large collections of intervals. Such situations may arise in temporal databases where several types of queries can be modeled and processed as CSPs. For these problems, systematic CSP algorithms can take advantage of temporal indexing to accelerate search. Directed search versions of chronological backtracking and forward checking are presented and tested. Our results show that indexing can drastically improve search performance.