Analysis of competing data structures: Does ontological clarity produce better end user query performance

Bowen, Paul L., O'Farrell, Robert A. and Rohde, Fiona H. (2006) Analysis of competing data structures: Does ontological clarity produce better end user query performance. Journal of the Association for Information Systems, 7 8: 514-544.

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Name Description MIMEType Size Downloads
Author Bowen, Paul L.
O'Farrell, Robert A.
Rohde, Fiona H.
Title Analysis of competing data structures: Does ontological clarity produce better end user query performance
Journal name Journal of the Association for Information Systems
ISSN 1558-3457
1536-9323
Publication date 2006-08
Sub-type Article (original research)
Volume 7
Issue 8
Start page 514
End page 544
Total pages 31
Editor K. Lyytinen
Place of publication Atlanta, GA, United States
Publisher Association for Information Systems
Collection year 2006
Language eng
Subject C1
350202 Business Information Systems (incl. Data Processing)
700299 Information services not elsewhere classified
Abstract End users respond to stakeholders' information requests by using query tools to retrieve information from their organizations' data stores. The structure of these data stores impacts end users' performance, e. g., the accuracy of their responses. Ontologically clearer conceptual models have been shown to facilitate better problem solving within real-world application domains. If, however, ontologically clearer conceptual models are directly transformed into implementation ( logical) data models, the differences in the number of entities and relationships may cause cognitive issues for end users that are likely to affect their query performance. This paper reports the results of an experiment that investigated the effect on query performance of more traditional logical models compared to ontologically clearer logical models. Results indicate that end users of the ontologically clearer implementation made fewer semantic errors overall. Thus, the benefits of ontological clarity at the conceptual level may translate into similar benefits when querying ontologically clearer logical models. Unfortunately, an examination of the specific types of errors that were made indicated that the benefits are not clear cut. While the removal of optional attributes and relationships led to an overall reduction in the number of errors, closer analyses show that some types of errors ( involving projection and restriction) decreased as expected, while other types of errors ( involving joins) increased.
Keyword Ontology
Information retrieval
End user performance
Logical data model
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Wed, 17 Oct 2007, 15:31:12 EST