An Algorithm for Data Reorganization in a Multi-dimensional Index

In spatial databases, data are associated with spatial coordinates and are retrieved based on spatial proximity. A spatial database uses spatial indexes to optimize spatial queries. An essential ingredient for efficient spatial query processing is spatial clustering of data and reorganization of spatial data. Traditional clustering algorithms and reorganization utilities lack in performance and execution. To solve this problem we have developed an algorithm to convert a two dimensional spatial index into a single dimensional value and then a reorganization is done on the spatial data. This report describes this algorithm as well as various experiments to validate its effectiveness

W42 - a scalable spatial database system for holding Digital Elevation Models

The design of a scalable system for holding spatial data in general and digital elevation models (DEMs) in specific has to account for the characteristics of data from various application fields. The data can be heterogeneous in coverage, as well as in resolution, information content and quality. A database aiming at the representation of world-wide DEMs has to consider these differences in the design of the system with respect to the structure and the algorithms. The database system W42, which is presented in the work at hand, is a scalable spatial database system capable of holding, extracting, mosaicking, and fusing spatial data represented in raster- as well as in vector-format. Design aspects for this task can be specified as holding spatial data in unique data structures and providing unique access functions to the data. These are subject of this work as well as first experiences gained from the implementation of part of the extensions made for the TanDEM-X mission

The Impact of Global Clustering on Spatial Database Systems

Global clustering has rarely been investigated in the area of spatial database systems although dramatic performance improvements can be achieved by using suitable techniques. In this paper, we propose a simple approach to global clustering called cluster organization. We will demonstrate that this cluster organization leads to considerable performance improvements without any algorithmic overhead. Based on real geographic data, we perform a detailed empirical performance evaluation and compare the cluster organization to other organization models not using global clustering. We will show that global clustering speeds up the processing of window queries as well as spatial joins without decreasing the performance of the insertion of new objects and of selective queries such as point queries. The spatial join is sped up by a factor of about 4, whereas non-selective window queries are accelerated by even higher speed up factors

Optimization of Spatial Joins Using Filters

When viewing present-day technical applications that rely on the use of database systems, one notices that new techniques must be integrated in database management systems to be able to support these applications efficiently. This paper discusses one of these techniques in the context of supporting a Geographic Information System. It is known that the use of filters on geometric objects has a significant impact on the processing of 2-way spatial join queries. For this purpose, filters require approximations of objects. Queries can be optimized by filtering data not with just one but with several filters. Existing join methods are based on a combination of filters and a spatial index. The index is used to reduce the cost of the filter step and to minimize the cost of retrieving geometric objects from disk. In this paper we examine n-way spatial joins. Complex n-way spatial join queries require solving several 2-way joins of intermediate results. In this case, not only the profit gained from using both filters and spatial indices but also the additional cost due to using these techniques are examined. For 2-way joins of base relations these costs are considered part of physical database design. We focus on the criteria for mutually comparing filters and not on those for spatial indices. Important aspects of a multi-step filter-based n-way spatial join method are described together with performance experiments. The winning join method uses several filters with approximations that are constructed by rotating two parallel lines around the object

Efficient Processing of Spatial Joins Using R-Trees

Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so-called shared virtual memory which is well-suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execu-tion. In order to reduce CPU- and I/O-cost, the three phases are processed in a fashion that pre-serves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance compar-ison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed-up under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012