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

A storage and access architecture for efficient query processing in spatial database systems

Due to the high complexity of objects and queries and also due to extremely large data volumes, geographic database systems impose stringent requirements on their storage and access architecture with respect to efficient query processing. Performance improving concepts such as spatial storage and access structures, approximations, object decompositions and multi-phase query processing have been suggested and analyzed as single building blocks. In this paper, we describe a storage and access architecture which is composed from the above building blocks in a modular fashion. Additionally, we incorporate into our architecture a new ingredient, the scene organization, for efficiently supporting set-oriented access of large-area region queries. An experimental performance comparison demonstrates that the concept of scene organization leads to considerable performance improvements for large-area region queries by a factor of up to 150

Data Management and Mining in Astrophysical Databases

We analyse the issues involved in the management and mining of astrophysical data. The traditional approach to data management in the astrophysical field is not able to keep up with the increasing size of the data gathered by modern detectors. An essential role in the astrophysical research will be assumed by automatic tools for information extraction from large datasets, i.e. data mining techniques, such as clustering and classification algorithms. This asks for an approach to data management based on data warehousing, emphasizing the efficiency and simplicity of data access; efficiency is obtained using multidimensional access methods and simplicity is achieved by properly handling metadata. Clustering and classification techniques, on large datasets, pose additional requirements: computational and memory scalability with respect to the data size, interpretability and objectivity of clustering or classification results. In this study we address some possible solutions.Comment: 10 pages, Late

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

QUASII: QUery-Aware Spatial Incremental Index.

With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can be executed efficiently. Such an indexing step requires substantial computing resources and introduces a considerable and growing data-to-insight gap where scientists need to wait before they can perform any analysis. Moreover, scientists often only use a small fraction of the data - the parts containing interesting phenomena - and indexing it fully does not always pay off. In this paper we develop a novel incremental index for the exploration of spatial data. Our approach, QUASII, builds a data-oriented index as a side-effect of query execution. QUASII distributes the cost of indexing across all queries, while building the index structure only for the subset of data queried. It reduces data-to-insight time and curbs the cost of incremental indexing by gradually and partially sorting the data, while producing a data-oriented hierarchical structure at the same time. As our experiments show, QUASII reduces the data-to-insight time by up to a factor of 11.4x, while its performance converges to that of the state-of-the-art static indexes

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio