A Knowledge Acquisition System for Price Change Rules
Date
2016-01
Authors
Jahan, Moslema
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Graduate Studies and Research, University of Regina
Abstract
Knowledge acquisition is the process of extracting and organizing knowledge from one
source and storing it in some other location such as a knowledge base. Our research
developed a new approach to knowledge acquisition concerning motor fuel pricing and
implemented it in the Knowledge Acquisition System for Price ChangE Rules (KASPER)
software system. Store managers want to understand the pricing strategies at competing
stores or brands. The main goal of our research is to provide decision rules with high
predictive accuracy on unseen data that may explain why a store or brand made a price
change in a speci c category. These decision rules should relate prices at one store to
those at other stores or brands in the same city.
Our approach is able to generate directional and categorical price change rules. The
approach can use brand-based or distance-based store-to-store relations or use brand-
to-brand relations. KASPER was applied to data from four cities to generate decision
rules from these relations. We tested the decision rules on unseen data and found that
most decision rules had high predictive accuracy in cases where the price changes tend to
uctuate more. Our approach was more e ective in the two cities where price changes of
varied sizes occur than in the two cities where price changes are of consistent, small sizes.
We found that high variability of price changes allows the system to match corresponding
behaviours more e ectively.
Description
A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science, University of Regina. xvi , 126 p.