DATABASE ORIENTED MODEL FOR PRICE DISCRIMINATION By Prof. Fazal Rehman Shamil, Pages 37-45

DATABASE ORIENTED MODEL FOR PRICE
DISCRIMINATION


Prof. Fazal Rehman Shamil,
University Of Shamil, Mianwali, Pakistan
fazalrehmanshamil@gmail.com


ABSTRACT
There have been a lot of interest to sell the product at high prices. Price discrimination is mostly used by ECommerce websites to offer the different prices of the same product to the different customers. Different techniques of price discrimination are available for web-based the application. Log file and cookies based price discrimination is more common among all. This paper introduces a new database oriented technique for price discrimination. Database oriented model is a model for price discrimination. In the proposed model, an intelligent database is maintained to offers the different prices of the same product to the different customers. This model supports 100% availability for tracking the end user or customer. Price discrimination is done every time a customer makes a transaction and buy the product. This feature makes this model more attractive as compared to other techniques.

  1. CONCLUSIONS

 

Price discrimination is very common nowadays. Different techniques are helpful for E-Commerce websites to offer different prices of the same product to the different customers. Weblog data and cookies are used by Amazon and other E-Commerce websites like eBay. DBOPD is a model that auto manage the website and all customers are auto deals with different prices. A person who want to buy the higher class and expansive products are offered at more prices.

A person who wants to buy the economy class and cheapest products are offered at fewer prices. A person who want to buy the neutral class and normal products are offered at normal prices. This proposed technique is easy to manage and easy to implement as compared to other techniques, because only we need to design the database according to this model, and nothing else needed for price discrimination. Further user can’t disable the tracking. It is 100% surety that user is tracked. This model contributes in a good way to promote the price discrimination.

 

  1. REFERENCES
  • Punit Ahluwalia. What is behind price dispersion in e-markets, Int. J. Services and Standards, Vol. 7, Nos. 3/4, 2011.
  • Jakub Mikians, László Gyarmati, Vijay Erramilli, Nikolaos Laoutaris. Detecting price and search discrimination on the Internet, Proceedings of the 11th ACM Workshop on Hot Topics in Networks, Pages 79-84
  • K.R.    Suneetha,    Dr.    R.    Karishnamoorthi,    identifying    user    behavior    by    analyzing    web    server access log file, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.4, April 2009
  • Tsuyoshi Murata and Kota Saito, Extracting Users’ Interests from Web Log Data, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 343-346
  • Jakub Mikians, Laszlo Gyarmati, Vijay Erramilli, Nikolaos Laoutaris, Crowd assisted the search for price discrimination in E-Commerce: First results, Proceedings of the ninth ACM conference on Emerging networking experiments and technologies, pages 1-6
  • David Liu, A model of optimal consumer search and price discrimination in the airline industry,(2012 15th June) Retrieved from http://economics.mit.edu/grad/davliu/research
  • Nick Craswell, David Hawking, Stephen Robertson, Effective site finding using link anchor information, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 250-257
  • Uichin Lee, Zhenyu Liu, Junghoo automatic identification of user goals in web search, 05 proceeding of the 14th international conference on world wide web, pages 391-400
  • Keaton Mowery, Dillon Bogenreif, Scott Yilek, and Hovav Shachamy, fingerprinting information in JavaScript Implementations, Proceedings of W2SP, 2011
  • Anitha, Dr.P.Isakki, A Survey on Predicting User Behavior Based onWeb Server Log Files in a Web Usage Mining s, Computing Technologies and Intelligent Data Engineering (ICCTIDE), 7-9 Jan. 2016
  • Zhen Liao, Yang Song, Yalou Huang, Li-Wei He, Qi He, An Effective Segmentation of UserSearch Behavior, IEEE Transactions on Knowledge and Data Engineering, Pages: 3090 – 3102,
  • Aniko Hannak, Gary Soeller, Measuring Price Discrimination and Steering E-commerce Web Sites, Proceedings of the 2014 Conference on Internet Measurement Conference, November 05 – 07, 2014
  • Jakub Mikians, László Gyarmati, Vijay Erramilli, Nikolaos Laoutaris, Crowd-assisted Search for Price Discrimination in E-Commerce: First results, Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
  • Thierry Warin, Homogenous goods markets: an empirical study of price dispersion on the internet, Int. J. Economics and Business Research, Vol. 4, No. 5, 2012
  • Shuqing Wang, Li She, Algorithm Research on User Interests Extracting via Web Log Data, International Conference on Web Information Systems and Mining, 2009
  • Max I. Fomitchev, How google analytics and conventional cookie tracking techniques overestimate unique visitors, Proceedings of the 19th international conference on World wide web
  • Hajime Hotta, Takashi Nozawa, Masafumi Hagiwara, A Design of Client Side Information Management Method for Web Services Collaboration, WI-IATW ’07 Proceedings of 2007, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology – Workshops.
  • Steven Englehardt, Dillon Reisman, Christian Eubank, Peter Zimmerman, Cookies That Give You Away: The Surveillance Implications of Web Tracking, Proceedings of the 24th International Conference on World Wide
  • Aaron Cahn, Scott Alfeld, Paul Barford, Muthukrishnan, An Empirical Study of Web Cookies, Proceedings of the 25th International Conference on World Wide Web.
  • Hongyan Liu, Jun he, Yingqin Gu, Hui Xiong, Detecting and Tracking Topics and Events from Web Search Logs, Proceedings of the 25th International Conference on World Wide Web, ACM Transactions on Information Systems, Volume 30 Issue 4, November 2012.