Object-Relational Spatial Indexing (original) (raw)

An extensible index for spatial databases

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.

Jackpine: A Benchmark to Evaluate Spatial Database Performance

— The volume of spatial data generated and consumed is rising exponentially and new applications are emerging as the costs of storage, processing power and network bandwidth continue to decline. Database support for spatial operations is fast becoming a necessity rather than a niche feature provided by a few products. However, the spatial functionality offered by current commercial and open-source relational databases differs significantly in terms of available features, true geodetic support, spatial functions and indexing. Benchmarks play a crucial role in evaluating the functionality and performance of a particular database, both for application users and developers, and for the database developers themselves. In contrast to transaction processing, however, there is no standard, widely used benchmark for spatial database operations. In this paper, we present a spatial database benchmark called Jackpine. Our benchmark is portable (it can support any database with a JDBC driver implementation) and includes both micro benchmarks and macro workload scenarios. The micro benchmark component tests basic spatial operations in isolation; it consists of queries based on the Dimensionally Extended 9-intersection model of topological relations and queries based on spatial analysis functions. Each macro workload includes a series of queries that are based on a common spatial data application. These macro scenarios include map search and browsing, geocod-ing, reverse geocoding, flood risk analysis, land information management and toxic spill analysis. We use Jackpine to evaluate the spatial features in 2 open source databases and 1 commercial offering.

Chapter 6: Access Methods and Query Processing Techniques

Lecture Notes in Computer Science, 2003

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B + -tree, hashing with chaining, and, in some cases, linear hashing . Object-oriented and object-relational systems have also adopted these structures to a great extend.

AN INDEXING METHOD FOR SPATIAL DATABASES

In this paper, we introduce an indexing method for accessing spatial databases. The index structure described here is multi-dimensional and is an extension of Multilevel Grid File (MLGF) combined with the z-ordering technique to efficiently handle indexing on the spatial components of the objects. The other important property of the proposed index structure is to be able to index on fuzzy information and process fuzzy querying in spatial databases. Handling spatial, aspatial data and fuzzy information in the physical database is necessary to satisfy some of the requirements of the spatial database applications, i.e., the geographic information systems (GIS) applications. With our proposed multi-dimensional index structure (we call it ExMLGF in this paper), one can create an index structure on aspatial (and fuzzy) data along with spatial data on the same index structure and process aspatial, spatial queries and fuzzy/crisp queries efficiently for the spatial database applications. The ExMLGF access structure is designed and implemented in a way that database users can have fuzzy queries on both homogenous and heterogeneous domains. In this paper we include a number of algorithms for processing different kinds of queries in spatial databases.

Extending a DBMS with spatial operations

Lecture Notes in Computer Science, 1991

A central problem in modern database design is how to resolve spatial operations with normal database operations in an extended relational database environment. A data architecture that matches the requirements for efficient processing of spatial queries in the extended database environment is proposed. It provides an equal opportunity for both the spatial components and the non-spatial components of the data to participate in query processing and optimization. The notion of extended operators to integrate homogeneously both spatial and non-spatial operations is introduced. Although intended primarily for spatial data, extended operators also provide a proper interface for integrating multi-media data into a database environment. The implications of this data architecture are presented. They include their effects on standard database operations, how spatial operations are integrated into the database management system (DBMS) for efficient processing, and how query processing and optimization are performed in this architecture. The operations of insertion and deletion, relational-based selection and join, and spatial-based selection and join are redefined in terms of extended operators. Spatial query processing is also described using extended operators. This data architecture can be built on top of an extensible database management system. Since it is dedicated towards efficient spatial query processing, this architecture can be used for testing and validating the extensibility of such systems and their effectiveness for supporting spatial data.

Oracle8i Spatial: Experiences with Extensible Databases

Lecture Notes in Computer Science, 1999

Conventional relational databases often do not have the technology required to handle complex data like spatial data. Unlike the traditional applications of databases, spatial applications require that databases understand complex data types like points, lines, and polygons. Typically, operations on these types are complex when compared to the operations on simple types. Hence relational database systems need to be extended in several areas to facilitate the storage and retrieval of spatial data. Several research reports have described the requirements from a database system and prioritized the research needs in this area.

Artikel An Introduction to Spatial Database Systems

We propose a definition of a spatial database system as a database system that offers spatial data types in its data model and query language and supports spatial data types in its implementation, providing at least spatial indexing and spatial join methods. Spatial database systems offer the underlying database technology for geographic information systems and other applications. We survey data modeling, querying, data structures and algorithms, and system architecture for such systems. The emphasis is on describing known technology in a coherent manner rather than on listing open problems.

Spatial indexing in microsoft SQL server 2008

Proceedings of the 2008 ACM SIGMOD international conference on Management of data - SIGMOD '08, 2008

Microsoft SQL Server 2008 adds built-in support for 2-dimensional spatial data types for both planar and geodetic geometries to address the increasing demands for managing location-aware data. SQL Server 2008 also adds indexing capabilities that, together with the necessary plan selections done by the query optimizer, provide efficient processing of spatial queries. This paper will present an overview of the spatial indexing implementation in SQL Server 2008 and outline how the indexing is implemented and how the cost-based query optimizer chooses among the different plans.

Data models and query languages for spatial databases

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].

JCAM: The joined clustered access method

1st International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2008, 2008

Spatial data management has been an active area of intensive research for more than two decades. In order to support objects in a database system, several issues should be taken into consideration including indexing and efficient query processing. Several indexing techniques have been proposed in the literature. Most of the existing indexing mechanisms are designed for spatial selection but may not be efficient for join operations. In this paper, a new structure is proposed to handle join operations between spatial data sets, called the Joined Clustered Access Method (JCAM). JCAM is pre-joined index dedicated for answering spatial join queries designed to enhance the response time of spatial join queries by decreasing the number of disk accesses. JCAM is a secondary index used to represent relationships between data sets as a colored-graph. Experiments show that JCAM outperforms the RTJ algorithm which is based on R-tree but requires more construction time.