Grant: Collaborative Research and Development Grants
University of Regina
$54,510.00
Data mining techniques for gas price data
FY 2015-16
Summary
This research project will address the problem of mining knowledge from crowd-sourced, spatio-temporal pricing data, principally concerning retail motor fuel (gas) prices. The industrial partner for this proposal is GB Internet Solutions (GBIS), a growing Regina-based developer of retailer- and consumer-oriented mobile applications (apps) for convenience retailers and consumers. The GBIS consumer app for the gas market is called GasBuddy, and the corresponding website is GasBuddy.com.
The research team, which consists of U. of Regina and GBIS personnel, will develop novel techniques for collecting, storing, mining, and visualizing crowd-sourced, spatio-temporal data in the form of gas price reports. We will apply these techniques to the database accumulated by GBIS to gain more insight into the gas market in North America and to enhance the experience of their members by providing more, and better, information and analytics to those using the GasBuddy app.
In our research, we will focus on six main problems, as described below. We will design and evaluate algorithms for building predictive gas price models to improve the predictive accuracy of such models while maintaining acceptable computational efficiency; identifying competitors in setting gas prices as an additional component in creating accurate and reliable predictive gas price models; appropriately and accurately filling in missing data; and characterizing gas price setting strategies at various levels of granularity to discover patterns of behaviour and incorporate them into our prediction models. As well, we will focus on the design and evaluation of new visual formats for obtaining and presenting information about gas prices that can be utilized by members to assist them in making informed purchase decisions, and identifying the best methodology for evaluating predictive models derived from spatio-temporal data when initial data exists, as the best combination of building, training and testing is not clear.
Program
Collaborative Research and Development Grants
Recipient
Location
Saskatchewan, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
453287-2013
Name nom
Hamilton, Howard
Department département
Computer Science
Organization id
17
Institution établissement
University of Regina
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2015
Competition year année de concours
2013
Award amount
54510
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2805
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Learning and inference theories
Research subject fr
Théories de l'apprentissage et l'inférence
Partie
2
Nb partie
3
Application title
Data mining techniques for gas price data
Keyword
NSERC
Application summary
This research project will address the problem of mining knowledge from crowd-sourced, spatio-temporal pricing data, principally concerning retail motor fuel (gas) prices. The industrial partner for this proposal is GB Internet Solutions (GBIS), a growing Regina-based developer of retailer- and consumer-oriented mobile applications (apps) for convenience retailers and consumers. The GBIS consumer app for the gas market is called GasBuddy, and the corresponding website is GasBuddy.com.
The research team, which consists of U. of Regina and GBIS personnel, will develop novel techniques for collecting, storing, mining, and visualizing crowd-sourced, spatio-temporal data in the form of gas price reports. We will apply these techniques to the database accumulated by GBIS to gain more insight into the gas market in North America and to enhance the experience of their members by providing more, and better, information and analytics to those using the GasBuddy app.
In our research, we will focus on six main problems, as described below. We will design and evaluate algorithms for building predictive gas price models to improve the predictive accuracy of such models while maintaining acceptable computational efficiency; identifying competitors in setting gas prices as an additional component in creating accurate and reliable predictive gas price models; appropriately and accurately filling in missing data; and characterizing gas price setting strategies at various levels of granularity to discover patterns of behaviour and incorporate them into our prediction models. As well, we will focus on the design and evaluation of new visual formats for obtaining and presenting information about gas prices that can be utilized by members to assist them in making informed purchase decisions, and identifying the best methodology for evaluating predictive models derived from spatio-temporal data when initial data exists, as the best combination of building, training and testing is not clear.