Databases such as Oracle, IBM, SQLServer are increasing their support for temporal and spatio- temporal data with every release. In this paper, we describe how Oracle users can extend the existing functionality in Oracle and ground their research by using Oracle's extensibility. To illustrate its applicability to temporal data, we present how a Map21 (M.A. Nascimento and M.H. Dunham, 1999) based index can be implemented using this framework.. Oracle uses the same extensibility framework to implement R-trees and rules manager. We describe enhancements to Oracle' R-trees for spatio-temporal data, and real-time event processing using Oracle's rules manager.

Much research has been devoted to scalable storage and retrieval techniques for domain databases such as spatial, text, XML and gene sequence data. Many efficient indexing techniques have been developed in this context. Given the improvement in the underlying technology, database applications are increasingly using domain data in transactional semantics. We examine the issue of when during the lifetime of a transaction is it better to incorporate updates in domain indexes. We present our experiences with R-tree indexes in Oracle. We examine two approaches for incorporating updates in spatial R-tree indexes: the first at update time, and the second at commit time. The first approach immediately incorporates changes in the index right away using system transactions and at commit time makes them visible to other transactions. The second approach, referred to as the deferred-incorporate approach, defers the updates in a secondary table and incorporates the changes in the index only at commit time. In experiments on real data sets, we compare the performance of the two approaches. For most transactions with reasonable number of update operations, we observe that the deferred approach outperforms the immediate-incorporate approach significantly for update operations and with appropriate optimizations achieves comparable query performance.

Spatial joins and spatial index creation are two of the most expensive operations in Oracle Spatial. Since spatial indexing is implemented in extensible indexing framework where queries only return rows from a single table, spatial joins could not be effectively and efficiently implemented in Oracle8i and prior releases. On the other hand, spatial indexcreationinvolvesmuchcomputationorI/Othatcouldbe easily parallelized. In this paper, we describe how Oracle Spatial applies parallel and pipelined table function technology to perform fast spatial joins and parallel index creation. This technologyhas been introduced in Oracle9i and allows users to iteratively return subsets of result rows to be used in the “from” clause of a SQL query. We present our experiences with these implementations and examine the performance on real datasets.

Spatial indexing has been one of the active focus areas in recent database research. Several variants of Quadtree and R-tree indexes have been proposed in database literature. In this paper, we first describe briefly our implementation of Quadtree and R-tree index structures and related optimizations in Oracle Spatial. We then examine the relative merits of two structures as implemented in Oracle Spatial and compare their performance for different types of queries and other operations. Finally, we summarize experiences with these different structures in indexing large GIS datasets in Oracle Spatial.

No abstract available.

Databases are increasingly being used to store multi-media objects such as maps, images, audio and video. Storage and retrieval of these objects is accomplished using multi-dimensional index structures such as R*-trees and SS-trees. As dimensionality increases, query performance in these index structures degrades. This phenomenon, generally referred to as the dimensionality curse, can be circumvented by reducing the dimensionality of the data. Such a reduction is however accompanied by a loss of precision of query results. Current techniques such as QBIC use SVD transform-based dimensionality reduction to ensure high query precision. The drawback of this approach is that SVD is expensive to compute, and therefore not readily applicable to dynamic databases. In this paper, we propose novel techniques for performing SVD-based dimensionality reduction in dynamic databases. When the data distribution changes considerably so as to degrade query precision, we recompute the SVD transform and incorporate it in the existing index structure. For recomputing the SVD-transform, we propose a novel technique that uses aggregate data from the existing index rather than the entire data. This technique reduces the SVD-computation time without compromising query precision. We then explore efficient ways to incorporate the recomputed SVD-transform in the existing index structure without degrading subsequent query response times. These techniques reduce the computation time by a factor of 20 in experiments on color and texture image vectors. The error due to approximate computation of SVD is less than 10%.

Non-uniformity in data extents is a general characteristic of spatial data. Indexing such non-uniform data using conventional spatial index structures such as R/sup */-trees is inefficient for two reasons: (1) the non-uniformity increases the likelihood of overlapping index entries, and (2) clustering of non-uniform data is likely to index more dead space than clustering of uniform data. To reduce the impact of these anomalies, we propose a new scheme that promotes data objects to higher levels in tree-based index structures. We examine two criteria for promotion of data objects and evaluate their relative merits using an R*-tree. In experiments on cartographic data, we observe that our promotion criteria yield up to 45% improvement in query performance for an R*-tree.