Abstract/Details

Mining of business data


2009 2009

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Abstract (summary)

Applying statistical tools to help understand business processes and make informed business decisions has attracted enormous amount of research interests in recent years. In this dissertation, we develop and apply data mining techniques to two sources of data, online bidding data for eBay and offline sales transaction data from a grocery product distributor. We mine online auction data to develop forecasting models and bidding strategies and mine offline sales transaction data to investigate sales people's price formation process.

We start with discussing bidders' bidding strategies in online auctions. Conventional bidding strategies do not help bidders select an auction to bid on. We propose an automated and data-driven strategy which consists of a dynamic forecasting model for auction closing price and a bidding framework built around this model to determine the best auction to bid on and the best bid amount.

One important component of our bidding strategy is a good forecasting model. We investigate forecasting alternatives in three ways. Firstly, we develop model selection strategies for online auctions (Chapter 3). Secondly, we propose a novel functional K-nearest neighbor (KNN) forecaster for real time forecasting of online auctions (Chapter 4). The forecaster uses information from other auctions and weighs their contribution by their relevance in terms of auction features. It improves the predictive performance compared to several competing models across various levels of data heterogeneity. Thirdly, we develop a Beta model (Chapter 5) for capturing auction price paths and find this model has advantageous forecasting capability.

Apart from online transactions, we also employ data mining techniques to understand offline transactions where sales representatives (salesreps) serve as media to interact with customers and quote prices. We investigate the mental models for salesreps' decision making, and find that price recommendation makes salesreps concentrate on cost related information.

In summary, the dissertation develops various data mining techniques for business data. Our study is of great importance for understanding auction price formation processes, forecasting auction outcomes, optimizing bidding strategies, and identifying key factors in sales people's decision making. Those techniques not only advance our understanding of business processes, but also help design business infrastructure.

Indexing (details)


Subject
Mathematics;
Statistics
Classification
0405: Mathematics
0463: Statistics
Identifier / keyword
Pure sciences; Data mining; Online bidding
Title
Mining of business data
Author
Zhang, Shu
Number of pages
211
Publication year
2009
Degree date
2009
School code
0117
Source
DAI-B 70/09, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781109382600
Advisor
Jank, Wolfgang
Committee member
Elmaghraby, Wedad J.; Kagan, Abram; Karaesmen Aydin, Itir Z.; Kumar, Mahesh; Smith, Paul J.
University/institution
University of Maryland, College Park
Department
Applied Mathematics and Scientific Computation
University location
United States -- Maryland
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3372937
ProQuest document ID
304920761
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304920761
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