Abstract
The on-line auction is one of the most successful types of electronic marketplaces and has been the subject of many academic studies. In recent years, empirical research on on-line auction has been flourishing because of the availability of large amounts of high-quality bid data from on-line auction sites. Researcher can provide buyers and sellers with useful information, and help prevent fraud and reduce purchase cost. However, the increasingly large volumes of bid data have made data collection increasingly complex and time consuming, and there are no effective resources that can adequately support this work. So this study focuses on the parallel crawling and capturing of on-line auctions from the automatic agent perspective to help researchers collect auction data more effectively. The issues in this study include parallel crawling architecture, crawling strategies, content capturing strategies, and prototype system implementation. Finally we conduct an empirical experiment on eBay U.S. and Ruten Taiwan to evaluate the performance of our parallel crawling and capturing. The results of this study show that our parallel crawling and capturing methodology is able to work in the real world and generate good performance outcomes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bajari, P., Hortacsu, A.: The Winner’s Curse, Reserve Prices and Endogenous Entry: Empirical Insight from eBay Auctions. Rand Journal of Economics 3(2), 329–355 (2003)
Bapna, R., Goes, P., Gupta, A., Jin, Y.: User Heterogeneity and its Impact on Electronic Auction Market Design: An Empirical Exploration. MIS Quarterly 28(1), 21–43 (2004)
Bichler, M., Kalagnanam, J., Katircioglu, K., King, A.J., Lawrence, R.D., Lee, H.S., Lin, G.Y., Lu, Y.: Applications of Flexible Pricing in Business-to-Business Electronic Commerce. IBM Systems Journal 41(2), 287–302 (2002)
Borle, S., Boatwright, P., Kadane, J.B.: The Timing of Bid Placement and Extent of Multiple Bidding: An Empirical Investigation Using eBay Online Auctions, Working paper, Rice University (2005)
Dellarocas, C.N.: Analyzing the Economic Efficiency of eBay-like Online Reputation Reporting Mechanisms. In: Proceedings of the 3rd ACM Conference on Electronic Commerce, Tampa, FL, October 14-16 (2001)
Jank, W., Shmueli, G.: Profiling Price Dynamics in On-line Auctions Using Curve Clustering, Working Paper, Smith School of Business, University of Maryland (2005)
Kauffman, R.J., Wood, C.A.: Running up the Bid, Modeling Seller Opportunism in Internet Auctions. In: Proceedings of the AMCIS 2000 Conference, Long Beach CA (2000)
Lucking-Reiley, D.: Auctions on the Internet: What’s Being Auctioned and How. Journal of Industrial Economics 48(3), 227–252 (2000)
Roth, A.E., Ockenfels, A.: Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet. The American Economic Review 92(4), 1093–1103 (2002)
Shmueli, G., Jank, W., Aris, A., Plaisant, C., Shneiderman, B.: Exploring Auction Databases through Interactive Visualization. Decision Support Systems (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, CH., Lin, SJ. (2008). Parallel Crawling and Capturing for On-Line Auction. In: Yang, C.C., et al. Intelligence and Security Informatics. ISI 2008. Lecture Notes in Computer Science, vol 5075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69304-8_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-69304-8_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69136-5
Online ISBN: 978-3-540-69304-8
eBook Packages: Computer ScienceComputer Science (R0)