Efficient computation of spatial joins

Günther, Oliver

doi:10.1109/icde.1993.344078

Cited by 87 publications

(56 citation statements)

References 9 publications

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“…The fact that there is no total ordering of objects in space that preserves spatial proximity [Günther 1993] renders conventional indexes, such as B + -trees, inapplicable to spatial databases. As a result, a number of spatial access methods have been proposed [Gaede and Günther 1998].…”

Section: Introductionmentioning

confidence: 99%

Multiway spatial joins

Mamoulis

Papadias

2001

ACM Trans. Database Syst.

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Due to the evolution of Geographical Information Systems, large collections of spatial data having various thematic contents are currently available. As a result, the interest of users is not limited to simple spatial selections and joins, but complex query types that implicate numerous spatial inputs become more common. Although several algorithms have been proposed for computing the result of pairwise spatial joins, limited work exists on processing and optimization of multiway spatial joins. In this paper we review pairwise spatial join algorithms and show how they can be combined for multiple inputs. In addition, we explore the application of synchronous traversal (ST), a methodology that processes synchronously all inputs without producing intermediate results. Then, we integrate the two approaches in an engine that includes ST and pairwise algorithms, using dynamic programming to determine the optimal execution plan. The results show that in most cases multiway spatial joins are best processed by combining ST with pairwise methods. Finally, we study the optimization of very large queries by employing randomized search algorithms.

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Section: Introductionmentioning

confidence: 99%

Multiway spatial joins

Mamoulis

Papadias

2001

ACM Trans. Database Syst.

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“…[GG97] and [Sam90] provide excellent surveys of almost all of these methods. Multidimensional indexes are used to utilize spatial restrictions (e.g., range restrictions, intersection, overlap) and to efficiently compute spatial joins [Rot91,Gün93,BKS93]. Multidimensional data structures are usually classified as point access methods storing points in multidimensional space and spatial access methods storing multidimensional extended objects of arbitrary volume, e.g., boxes, spheres, etc.…”

Section: Multidimensional Access Methodsmentioning

confidence: 99%

MISTRAL: Processing Relational Queries using a Multidimensional Access Technique

Markl

2000

Ausgezeichnete Informatikdissertationen 1999

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PrefaceClassical one dimensional B-trees have been the standard access method of all commercial database systems for many years. This dissertation is a very promising effort to introduce universal B-trees (UB-trees), the multidimensional variant of Btrees, as a basic access method into the core of fundamental DB-technology. The universal relevance of UB-trees is a consequence of the fact, that every relation can be considered as a set of points in multidimensional space. UB-trees organize this space for efficient processing of the data, resulting for many types of queries in orders of magnitude improvement over classical methods.The thesis lays the theoretical foundations of UB-tress, predicts their performance by analytical models and validates these models by experiments using large real world databases and real life applications and queries from the field of datawarehousing. The need to sort data and intermediate results frequently is an annoying drawback of today' s query processing methods. In combination with the Tetris algorithm UBtrees allow to avoid sorting in most cases, leading to dramatic improvements of response time, storage requirement and overall query processing time.Adding a new access method requires to consider all aspects of database systems:-architecture of subsystems -query optimization -query processing -multiuser operation and synchronization -bulk loading -storage requirement -parallelism, etc.Since UB-trees rely on classical B-trees for their implementation, all of these issues can be solved in a satisfactory way and can be dealt with elegantly.The performance experiments reported in this thesis were carried out with an implementation of UB-trees as a middleware layer on top of SQL. Additional performance improvements can be gained by integrating the UB-tree technology in the kernel of database systems.This thesis is a cornerstone of the MISTRAL project. MISTRAL has the goal to introduce UB-trees as a new access method into database systems with the fascinating vision, to extend fundamental database technology in an essential way. MISTRAL is financially supported by SAP, Teijin, NEC, Hitachi, the European Commission, Project MDA, TAS, Gfk and Microsoft. Abstract:A multidimensional access method offering significant performance increases by intelligently partitioning the query space is applied to relational database management systems (RDBMS). We introduce a formal model for multidimensional partitioned relations and discuss several typical query patterns. The model identifies the significance of multidimensional range queries and sort operations. The discussion of current access methods gives rise to the need for a multidimensional partitioning of relations. A detailed analysis of space partitioning focussing especially on Z-ordering illustrates the principle benefits of multidimensional indexes. After describing the UB-Tree and its standard algorithms for insertion, deletion, point queries, and range queries, we introduce the spiral algorithm for nearest neighbor queries with UB-Tree...

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“…However, conventional spatial join algorithms [13], [14], [21], [22] cannot be straightforwardly applied to distributed spatial join processing on sensor networks. Most of the conventional algorithms are based on centralized spatial indexes such as the R-tree and its variants (R + -tree, R*-tree).…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Distributed Spatial Joint Processing in Wireless Sensor Networks

Kim¹,

Son

Kim³

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naïve and existing approaches through extensive experiments and clarify our approach's distinguished features. key words: distributed spatial index, distributed spatial join, in-network query processing, wireless sensor network

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Efficient computation of spatial joins

Cited by 87 publications

References 9 publications

Multiway spatial joins

Multiway spatial joins

MISTRAL: Processing Relational Queries using a Multidimensional Access Technique

Energy-Efficient Distributed Spatial Joint Processing in Wireless Sensor Networks

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