Object-Extended OLAP Querying

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17 Citations (Scopus)

Abstract

On-line analytical processing (OLAP) systems based on a dimensional view of data have found widespread use in business applications and are being used increasingly in non-standard applications. These systems provide good performance and ease-of-use. However, the complex structures and relationships inherent in data in non-standard applications are not accommodated well by OLAP systems. In contrast, object database systems are built to handle such complexity, but do not support OLAP-type querying well.

This paper presents the concepts and techniques underlying a flexible, "multi-model" federated system that enables OLAP users to exploit simultaneously the features of OLAP and object systems. The system allows data to be handled using the most appropriate data model and technology: OLAP systems for dimensional data and object database systems for more complex, general data. This allows data analysis on the OLAP data to be significantly enriched by the use of additional object data. Additionally, physical integration of the OLAP and the object data can be avoided. As a vehicle for demonstrating the capabilities of the system, a prototypical OLAP language is defined and extended to naturally support queries that involve data in object databases. The language permits selection criteria that reference object data, queries that return combinations of OLAP and object data, and queries that group dimensional data according to object data. The system is designed to be aggregation-safe, in the sense that it exploits the aggregation semantics of the data to prevent incorrect or meaningless query results. These capabilities may also be integrated into existing languages. It is shown how to integrate relational and XML data using the technology. A prototype implementation of the system is reported, along with performance measurements that show that the approach is a viable alternative to a physically integrated data warehouse.

Original languageEnglish
JournalData & Knowledge Engineering
Volume68
Issue number5
Pages (from-to)453-480
Number of pages28
ISSN0169-023X
DOIs
Publication statusPublished - 2009

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