The increasing use of temporal and spatial data in presentday relational systems necessitates an efficient support of joins on interval-valued attributes. Standard join algorithms do not support those data types adequately, whereas special approaches for interval joins usually require an augmentation of the internal access methods which is not supported by existing relational systems. To overcome these problems we introduce new join algorithms for interval data. Based on the Relational Interval Tree, these algorithms can easily be implemented on top of any relational database system while providing excellent performance on joining intervals. As experimental results on an Oracle9i server show, the new techniques outperform existing relational methods for joining intervals significantly. 1.

A wide-range of applications, including Publish/Subscribe, Workflow, and Web-site Personalization, require maintaining user's interest in expected data as conditional expressions. This paper proposes to manage such expressions as data in Relational Database Systems (RDBMS). This is accomplished 1) by allowing expressions to be stored in a column of a database table and 2) by introducing a SQL EVALUATE operator to evaluate expressions for given data. Expressions when combined with predicates on other forms of data in a database, are just a flexible and powerful way of expressing interest in a data item. The ability to evaluate expressions (via EVALUATE operator) in SQL, enables applications to take advantage of the expressive power of SQL to support complex subscription models. The paper describes the key concepts, presents our approach of managing expressions in Oracle RDBMS, discusses a novel indexing scheme that allows efficient filtering of a large set of expressions, and outlines future directions.

We propose the notion of tight-coupling [8] to add new data types into the DBMS engine. In this paper, we introduce the Odysseus ORDBMS and present its tightly-coupled IR features (U.S. patented). We demonstrate a web search engine capable of managing 20 million web pages in a nonparallel configuration using Odysseus. 1

The W3C’s Semantic Web Activity is illustrating the use of semantics for information integration, search, and analysis. However, the majority of the work in this community has focused more on the thematic aspects of information and has paid less attention to its spatial and temporal dimensions. In this paper, we present an integrative ontology-based framework incorporating the thematic, spatial, and temporal dimensions of information. This framework is built around the RDF metadata model. Our ultimate goal is to provide an information system which allows searching and analysis of relationships in any or all of the three dimensions of space, time, and theme. Toward this end, we present an upper-level ontology combining concepts and relationships from both the thematic and spatial dimensions and show how to incorporate temporal semantics into this ontology. We also introduce the notion of a thematic context linking entities of differing dimensions and define a set of query operators built upon these contexts.

In order to achieve efficient execution plans for queries comprising userdefined data types and predicates, the database system has to be provided with appropriate index structures, query processing methods, and optimization rules. Although available extensible indexing frameworks provide a gateway to seamlessly integrate user-defined access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the access method itself. An internal enhancement of the database kernel is usually not an option for database developers.

Primary B +-tree, a variant of B +-tree structure with row data in leaf blocks, is an ideal storage organization for queries involving exact match and/or range search on primary keys. Commercially, primary B +-tree like structures have been supported in DBMSs like Compaq Non-Stop SQL, Sybase Adaptive Server, and Microsoft SQL Server. Oracle’s index-organized table is like a primary B +-tree; however, it differs from its commercial counterparts in the following respects: 1) The storage organization does not require the entire row to be stored in the primary key index. Infrequently accessed columns can be selectively pushed into an overflow storage area to speed up access to columns that are frequently accessed. 2) Secondary indexes on index-organized tables support logical primary key-based row identifiers, and still provide performance comparable to secondary indexes with physical row identifiers by storing and making use of guess-DBA (Database Block Address). 3) Support for primary key compression leads to reduced storage requirements. This paper Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by

The efficient management of interval data represents a core requirement for many temporal and spatial database applications. With the Relational Interval Tree (RI-tree ), an efficient access method has been proposed to process interval intersection queries on top of existing objectrelational database systems. This paper complements that approach by effective and efficient models to estimate the selectivity and the I/O cost of interval intersection queries in order to guide the cost-based optimizer whether and how to include the RI-tree into the execution plan. By design, the models immediately fit to common extensible indexing/ optimization frameworks, and their implementations exploit the built-in statistics facilities of the database server. According to our experimental evaluation on an Oracle database, the average relative error of the estimated cost to the actual cost of index scans ranges from 0% to 23%, depending on the resolution of the persistent statistics and the size of the query objects.

Many evolving database applications warrant the use of non-traditional indexing mechanisms beyond B+-trees and hash tables. SP-GiST is an extensible indexing framework that broadens the class of supported indexes to include disk-based versions of a wide variety of space-partitioning trees, e.g., disk-based trie variants, quadtree variants, and kd-trees. This paper presents a serious attempt at implementing and realizing SP-GiST-based indexes inside PostgreSQL. Several index types are realized inside PostgreSQL facilitated by rapid SP-GiST instantiations. Challenges, experiences, and performance issues are addressed in the paper. Performance comparisons are conducted from within PostgreSQL to compare update and search performances of SP-GiST-based indexes against the B+-tree and the R-tree for string, point, and line segment data sets. Interesting results that highlight the potential performance gains of SP-GiST-based indexes are presented in the paper.

Modern database applications including computeraided design (CAD), medical imaging, or molecular biology impose new requirements on spatial query processing. Particular problems arise from the need of high resolutions for very large spatial objects, including cars, space stations, planes and industrial plants, and from the design goal to use general purpose database management systems in order to guarantee industrial-strength. In the past two decades, various stand-alone spatial index structures have been proposed but their integration into fully-fledged database systems is problematic. Most of these approaches are based on decomposition of spatial objects leading to replicating index structures. In contrast to common black-and-white decompositions which suffer from the lack of intermediate solutions, we introduce grey approximations as a new and general concept. We demonstrate the benefits of grey approximations in the context of encoding spatial objects by space filling curves resulting in grey interval sequences. Spatial intersection queries are then processed by a filter and refine architecture which, as an important design goal, can purely be expressed by means of the SQL:1999 standard. Our new High Resolution Indexing (HRI) method can easily be integrated into general purpose DBMSs. The experimental evaluation on real-world test data from car and plane design projects points out that our new concept outperforms competitive techniques that are implementable on top of a standard object -relational DBMS by an order of magnitude with respect to secondary storage space and overall query response time. 1.

. Intervals represent a fundamental data type for temporal, scientific, and spatial databases where time stamps and point data are extended to time spans and range data, respectively. For OLTP and OLAP applications on large amounts of data, not only intersection queries have to be processed efficiently but also general interval relationships including before, meets, overlaps, starts, finishes, contains, equals, during, startedBy, finishedBy, overlappedBy, metBy, and after. Our new algorithms use the Relational Interval Tree, a purely SQL-based and objectrelationally wrapped index structure. The technique therefore preserves the industrial strength of the underlying RDBMS including stability, transactions, and performance. The efficiency of our approach is demonstrated by an experimental evaluation on a real weblog data set containing one million sessions. 1