Spatial databases-accomplishments and research needs

International Journal of Data Mining & Knowledge Management Process, 2013

With the rapid development in Geographic Information Systems (GISs) and their applications, more and more geographical databases have been developed by different vendors. However, data integration and accessing is still a big problem for the development of GIS applications as no interoperability exists among different spatial databases. In this paper we propose a unified approach for spatial data query. The paper describes a framework for integrating information from repositories containing different vector data sets formats and repositories containing raster datasets. The presented approach converts different vector data formats into a single unified format (File Geo-Database "GDB"). In addition, we employ "metadata" to support a wide range of users' queries to retrieve relevant geographic information from heterogeneous and distributed repositories. Such an employment enhances both query processing and performance.

2012

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial Geoinformatica

Spatial data has become more important everyday in decision-making and planning processes. As such, it needs to be stored and retrieved in information systems that often require high performance due to the voluminous nature of spatial data. Typically this is not much of a problem unless one considers the effect of spatial extent as a function of time in information retrieval. Taxonomies of spatial objects can be useful in suggesting a storage model that addresses spatiotemporal queries. This research develops such a taxonomy and then proposes how the taxonomy might lend itself to a high performance binary tree model for query and storage of spatial data that considers the relationship of time on the shape of objects in storage. The approach has the potential to retrieve data for certain types of queries much more quickly than a linear search of the same types of spatial objects. Comparative evaluation will be the subject of future work.

Transactions in …, 2004

A growing number of services are now being offered over mobile devices. They typically combine positioning technology, wireless technology and spatial analysis methods applied to detailed geographical, time based data to offer services in support of time critical, spatial, mobile decision making. A collection of research issues need to be addressed in the successful delivery of such services that extend beyond issues of sophisticated network algorithms. Specifically, careful attention needs to be given to: (1) people and user environments; (2) access to networks; (3) policy, privacy and liability; (4) standards and interoperability; and (5) data quality. Spatial Data Infrastructure (SDI) is the collective term for these interconnected issues and has been a traditional area of research associated with geographic information science. In this paper the particular SDI requirements for the successful delivery of Location Based Services (LBS) are explored through the development of a prototype LBS for journey planning. The initial implementation and testing of this prototype has revealed that the SDI context is well suited as a framework within which to examine the related LBS issues. From a more technical perspective, the testing has revealed that data structure and the means by which large data sets are mined (in order to gather information to present to users) is critical to the success of timely information delivery over limited bandwidth media.

2005

Spatial database systems has been an active area of research over the past 20 years. A large number of research efforts have appeared in literature aimed at effective modelling of spatial data and efficient processing of spatial queries. This book investigates several aspects of a spatial database system, and includes recent research efforts in this field. More specifically, some of the topics covered are: spatial data modelling; indexing of spatial and spatio-temporal objects; data mining and knowledge discovery in spatial and spatio-temporal databases; management issues; and query processing for moving objects. Therefore, the reader will be able to get in touch with several important issues that the research community is dealing with. Moreover, each chapter is self-contained, and it is easy for the non-specialist to grasp the main issues. xiv A Closing Remark The authors have made significant efforts to provide high-quality chapters, despite space restrictions. These authors are well-known researchers in the area of spatial and spatio-temporal databases, and they have offered significant contributions to the literature. We hope that the reader will gain the most out of this effort.

Pressure on land development in urban areas causes progressive efforts in spatial planning and management. The physical growth expansion of urban areas to accommodate rural migration gives a massive impact to social, economic and politics of major cities. Most of the models used in managing urban areas are moving towards sustainable urban development which to fulfill current necessities while preserving the resources for future generation. However in order to manage large amounts of urban spatial data, an efficient spatial data constellation method is needed. With the ease of three dimensional (3D) spatial data usage in urban areas as a new source of data input, practical spatial data indexing is necessary to improve data retrieval and management. Current two dimensional (2D) spatial indexing approaches seem not applicable to the current and future spatial developments. Therefore, the objective of this paper is to review existing spatial data indexing approaches to managing large urban area datasets. Each approach will be reviewed and discussed according to the current spatial data scenarios. In addition, a 3D spatial data indexing method will be discussed as an alternative for organizing 3D spatial data.

2001

Emerging database applications require the use of new indexing structures beyond B-trees and R-trees. Examples are the k-D tree, the trie, the quadtree, and their variants. They are often proposed as supporting structures in data mining, GIS, and CAD/CAM applications. A common feature of all these indexes is that they recursively divide the space into partitions. A new extensible index structure, termed SP-GiST, is presented that supports this class of data structures, mainly the class of space partitioning unbalanced trees. Simple method implementations are provided that demonstrate how SP-GiST can behave as a k-D tree, a trie, a quadtree, or any of their variants. Issues related to clustering tree nodes into pages as well as concurrency control for SP-GiST are addressed. A dynamic minimum-height clustering technique is applied to minimize disk accesses and to make using such trees in database systems possible and efficient. A prototype implementation of SP-GiST is presented as well as performance studies of the various SP-GiST's tuning parameters.

Abstract: The increased availability of spatial data in recent years has lead to new challenges in the analysis of large multidimensional datasets. One solution is to integrate GIS with OLAP and relational databases. Another strategy has been to leverage existing spatial capabilities of databases to perform spatial OLAP. In this article, we review existing modelling strategies for spatial data warehousing at all three levels: conceptual, logical and implementation.

Data & Knowledge Engineering, 1998

The main purpose of this paper is to investigate the characteristics that distinguish spatial databases systems from traditional ones. Hereto, we give an overview of some well-known data models and query languages of spatial database systems. We also investigate the concept of genericity, as introduced by Chandra and Harel for classical databases [6], for spatial databases. Paredaens, Van den Bussche and Van Gucht [34] have shown that the concept of genericity breaks up in a hierarchy of genericity classes. In this respect, we classify data models and query languages according to the type of generic operations they are designed to support [33].