A Statistical And Signal Processing Based System
For Data Quality Management
Author(s)
A. C. H. Dantas, J. M. de Seixas, F. B. Diniz & T. N. Ferreira
Abstract
Research in data quality is getting more important as databases in research centers
and companies get larger. Therefore, developing new mechanisms to discover
knowledge in large data bases is as urgent as finding ways to measure and assure
the quality of the data. This work describes the development of a Quality Control
System, intended to operate on dynamic and large data bases. Techniques used
vary from standard statistical tests to signal processing methodology, such as filtering,
wavelet transformation and neural processing. Data used for this work consist
of a social database and of time series for five years of stock data for companies
present in the SP500 index. Generalization tests show that feed forward neural networks
represent a suitable tool for tracking pre-processed (filtered) financial series,
and can be used to define a corridor inside which one may consider new data as
acceptable. For this data, we were also able to develop a model for the distribution
of the differences between consecutive days, which can be combined to neural
processing for data acceptation. Tests performed on the social data allowed us to
identify probabilistic density functions for a set of variables, making it possible to
create a objective test of data quality assessment.
Keywords: data quality, quality control system, statistical tests, signal processing
techniques, multidimensional data, financial time series.
1 Introduction
Specialists say that this century is surely the century of data [1]. Poor quality customer
data costs U.S. business US$611 billion a year [2]. It is then easy to see
that data is a critical asset in the information economy, and that the quality of a
company’s data is a good predictor of its future success. This means that we are
entering a data driven era, and that our databases must now be treated as real com-
Keywords
data quality, quality control system, statistical tests, signal processing techniques, multidimensional data, financial time series.
Related Book
Other papers in this volume
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