Rolfe R. Schmidt

Data Integration and Access

This chapter describes the progress of the Digital Government Research Center in tackling the challenges of integrating and accessing the massive amount of statistical and text data available from government agencies. In particular, we address the issues of database heterogeneity, size, distribution, and control of terminology. In this chapter we provide an overview of our results in addressing problems such as (1) ontological mappings for terminology standardization, (2) data integration across data bases with high speed query processing, and (3) interfaces for query input and presentation of results. The DGRC is a collaboration between researchers from Columbia University and the Information Sciences Institute of the University of Southern California employing technology developed at both locations, in particular the SENSUS ontology, the SIMS multi-database access planner, the LEXING automated dictionary and terminology analysis system, the main-memory query processing component and others. The pilot application targets gasoline data from the Bureau of Labor Statistics, the Energy Information Administration of the Department of Energy, the Census Bureau, and other government agencies.

ProPolyne: A Fast Wavelet-Based Algorithm for Progressive Evaluation of Polynomial Range-Sum Queries

Many range aggregate queries can be efficiently derived from a class of fundamental queries: the polynomial range-sums. After demonstrating how any range-sum can be evaluated exactly in the wavelet domain, we introduce a novel pre-aggregation method called ProPolyne to evaluate arbitrary polynomial range-sums progressively. At each step of the computation, ProPolyne makes the best possible wavelet approximation of the submitted query. The result is a data-independent approximate query answering technique which uses data structures that can be maintained efficiently. ProPolynes performance as an exact algorithm is comparable to the best known MOLAP techniques. Our experimental results show that this approach of approximating queries rather than compressing data produces consistent and superior approximate results when compared to typical wavelet-based data compression techniques.

How to evaluate multiple range-sum queries progressively

Users of decision support system typically submit batches of range-sum queries simultaneously rather than issuing individual, unrelated queries. We propose a wavelet based technique that exploits T/O sharing across a query batch to evaluate the set of queries progressively and efficiently. The challenge is that now controlling the structure of errors across query results becomes more critical than minimizing error per individual query. Consequently, we define a class of structural error penalty functions and show how they are controlled by our technique Experiments demonstrate that our technique is efficient as an exact algorithm, and the progressive estimates are accurate, even after less than one I/O per query.