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Summary: Fast Approximate Evaluation of OLAP Queries
for Integrated Statistical Data£
Jose Luis Ambite½, Cyrus Shahabi¾, Rolfe R. Schmidt¾, and Andrew Philpot½
Digital Government Research Center
Information Sciences Institute½ and Computer Science Department¾
University of Southern California
4676 Admiralty Way, Marina del Rey, CA 90292, USA
Abstract
We have developed a mediator architecture that integrates statistical information about energy prod-
ucts from several government agencies, such as the Bureau of Labor Statistics, the Energy Information
Administration, and the California Energy Commission. Our architecture has a dual mode of operation.
First, our system can retrieve live data from databases and web sources from these agencies. This allows
the users to obtain completely up-to-date data. However, for complex analytical queries that typically
require large amounts of data and processing, live access does not offer the level of interactivity that
some users require. Second, our system can warehouse the information from the data sources to allow
for complex analytical queries to be executed much more efficiently. However, the data would be only
as recent as the last update to the data warehouse. In this paper we describe the architecture and focus on
how to perform analytical queries against the data warehouse very efficiently. We present results using
a fast wavelet-based technique for progressive evaluation of range-sum queries. This technique allows
for returning an approximate result to the query very efficiently and for fast convergence to the exact
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