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

Query processing of geometric objects with free form boundarie sin spatial databases

The increasing demand for the use of database systems as an integrating factor in CAD/CAM applications has necessitated the development of database systems with appropriate modelling and retrieval capabilities. One essential problem is the treatment of geometric data which has led to the development of spatial databases. Unfortunately, most proposals only deal with simple geometric objects like multidimensional points and rectangles. On the other hand, there has been a rapid development in the field of representing geometric objects with free form curves or surfaces, initiated by engineering applications such as mechanical engineering, aviation or astronautics. Therefore, we propose a concept for the realization of spatial retrieval operations on geometric objects with free form boundaries, such as B-spline or Bezier curves, which can easily be integrated in a database management system. The key concept is the encapsulation of geometric operations in a so-called query processor. First, this enables the definition of an interface allowing the integration into the data model and the definition of the query language of a database system for complex objects. Second, the approach allows the use of an arbitrary representation of the geometric objects. After a short description of the query processor, we propose some representations for free form objects determined by B-spline or Bezier curves. The goal of efficient query processing in a database environment is achieved using a combination of decomposition techniques and spatial access methods. Finally, we present some experimental results indicating that the performance of decomposition techniques is clearly superior to traditional query processing strategies for geometric objects with free form boundaries

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

Global Pattern Search at Scale

In recent years, data collection has far outpaced the tools for data analysis in the area of non-traditional GEOINT analysis. Traditional tools are designed to analyze small-scale numerical data, but there are few good interactive tools for processing large amounts of unstructured data such as raw text. In addition to the complexities of data processing, presenting the data in a way that is meaningful to the end user poses another challenge. In our work, we focused on analyzing a corpus of 35,000 news articles and creating an interactive geovisualization tool to reveal patterns to human analysts. Our comprehensive tool, Global Pattern Search at Scale (GPSS), addresses three major problems in data analysis: free text analysis, high volumes of data, and interactive visualization. GPSS uses an Accumulo database for high-volume data storage, and a matrix of word counts and event detection algorithms to process the free text. For visualization, the tool displays an interactive web application to the user, featuring a map overlaid with document clusters and events, search and filtering options, a timeline, and a word cloud. In addition, the GPSS tool can be easily adapted to process and understand other large free-text datasets

Clustering Spatial Data for Join Operations Using Match-based Partition

The spatial join is an operation that combines two sets of spatial data by their spatial relationships. The cost of spatial join could be very high due to the large sizes of spatial objects and the computation-intensive spatial operations. In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters and then schedule the processing of the clusters such that the number of times the same objects to be fetched into memory can be minimized. In this paper, we propose a match-based approach to partition a large spatial data set into clusters, which is computed based on the maximal match on the spatial join graph. Simulations have been conducted and the results have shown that, when comparing to existing approaches, our new method can significantly reduce the number of clusters produced in spatial join processin