Abstract
<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US"><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Decision making is an activity that addresses the problem of extracting knowledge and information from data stored in data warehouses, in order to improve the business processes of information systems. Usually, decision making is based on On-Line Analytical Processing, data mining, or approximate query processing. In the last case, answers to analytical queries are provided in a fast manner, although affected with a small percentage of error. In the paper, we present the architecture of an approximate query answering system. Then, we illustrate our ADAP (Analytical Data Profile) system, which is based on an engine able to provide fast responses to the main statistical functions by using orthogonal polynomials series to approximate the data distribution of multidimensional relations. Moreover, several experimental results to measure the approximation error are shown and the response-time to analytical queries is reported.</span></span></p>
Highlights
The increasing importance of data warehouses (DWs) in information systems is due to their capacity of allowing decision makers to perform the extraction of knowledge and information by means of On-Line Analytical Processing (OLAP) and data mining techniques (Chaudhuri, Dayal, & Ganti, 2001)
There are three logical models for the development of a DW: (a) Relational OLAP model (ROLAP), that resides on the relational technology, (b) Multidimensional OLAP model (MOLAP), that uses multidimensional arrays for storing data; and (c) Hybrid OLAP model (HOLAP), which adopts both ROLAP and MOLAP technologies (Golfarelli & Rizzi, 1998a; Golfarelli, Maio, & Rizzi, 1998b)
ADAP is an OLAP tool, whose features are to collect, to organize, and to process large data volumes stored in a DW, in order to obtain statistical data profiles to use for approximate query processing
Summary
The increasing importance of data warehouses (DWs) in information systems is due to their capacity of allowing decision makers to perform the extraction of knowledge and information by means of On-Line Analytical Processing (OLAP) and data mining techniques (Chaudhuri, Dayal, & Ganti, 2001). In DWs built over the relational technology, analytical processing involves typically the computation of summary data and the execution of aggregate queries. This kind of queries needs to access all the data stored in the database and, on large volumes of data, the computation of aggregate queries leads to a long answering time. The alternative to the scan of huge amounts of data in data warehouses provides approximate query answering when applications can tolerate small errors in query answers (Sassi, Tlili, & Ounelli, 2012) The goal of this approach is to achieve interactive response times to aggregate queries.
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