NDLI: An OLAP data model driven approach to process statistical tables

Content Provider	IEEE Xplore Digital Library
Author	Luk, W.-S. Leung, P.
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada (Luk, W.-S.; Leung, P.)
Abstract	Statistical tables belong to an important subset of tables published in the Web, because they represent up-to-date, vital information sources for decision makers. These tables are often carefully designed for easy reading by analysts, and then mechanically produced by an OLAP database system. The general practice of extracting attribute-value pairs from statistical tables does not ensure high accuracy when they are used as a database for an information retrieval system. In this paper, we show how a human may visualize a statistical table as an multidimensional object, defined by a suitably modified OLAP model. In this way, the keywords are classified into semantically distinct groups, i.e., dimension hierarchies, without any ontological knowledge or resorting to machine learning. A prototype system which mimics the human reasoning for table processing has been implemented. Experiments on 150 randomly chosen tables from statistics Canada have confirmed the validity of this approach.
Starting Page	1054
Ending Page	1058
File Size	138517
Page Count	5
File Format	PDF
ISBN	0769524249
ISSN	15294188
DOI	10.1109/DEXA.2005.144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-22
Publisher Place	Denmark
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Humans Database systems Data mining Visual databases Information retrieval Visualization Multidimensional systems Ontologies Machine learning
Content Type	Text
Resource Type	Article

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