Empirical observations on the session timeout threshold

The World Wide Web is a popular ''tool'' for companies. It can be used as a method of communication between companies and their customers; it also allows organizations to setup virtual storefronts that can be accessed by customers from all over the world. The ability to understand customers' behavior is extremely important as companies strive to increase the usability and profitability of their web service. The concept of a session is a popular unit of measurement used to analyze recorded information. However, this concept is currently rather abstract and lacks definition. How we measure a session is a fundamental question for web services utilizing this concept. Currently, this question has no real answer. This paper presents a session timeout threshold model based on empirical observations as an initial answer to this question. The model seeks to provide accurate session data with respect to individual web services.

[1]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[2]  David M. Kristol,et al.  HTTP State Management Mechanism , 1997, RFC.

[3]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[4]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[5]  Myra Spiliopoulou,et al.  A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis , 2003, INFORMS J. Comput..

[6]  M. HamidR.Jamali,et al.  Website usage metrics: A re-assessment of session data , 2008, Inf. Process. Manag..

[7]  Daniel A. Menascé,et al.  Fractal Characterization of Web Workloads , 2002 .

[8]  Sally Jo Cunningham,et al.  A Comparative Transaction Log Analysis of Two Computing Collections , 2000, ECDL.

[9]  James E. Pitkow,et al.  Summary of WWW characterizations , 1998, World Wide Web.

[10]  Katerina Goseva-Popstojanova,et al.  Empirical study of session-based workload and reliability for Web servers , 2004, 15th International Symposium on Software Reliability Engineering.

[11]  Mark Levene,et al.  Associating search and navigation behavior through log analysis , 2005, J. Assoc. Inf. Sci. Technol..

[12]  Martin Arlitt,et al.  A workload characterization study of the 1998 World Cup Web site , 2000, IEEE Netw..

[13]  Zhixiang Chen,et al.  Linear time algorithms for finding maximal forward references , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[14]  Jesse James Garrett Ajax: A New Approach to Web Applications , 2007 .

[15]  David M. Kristol,et al.  HTTP State Management Mechanism , 2000, RFC.

[16]  Mark S. Squillante,et al.  Web traffic modeling and Web server performance analysis , 1999, PERV.

[17]  Ralph B. D'Agostino,et al.  Goodness-of-Fit-Techniques , 2020 .

[18]  Zhao Li,et al.  Evaluating Web software reliability based on workload and failure data extracted from server logs , 2004, IEEE Transactions on Software Engineering.

[19]  M. HamidR.Jamali,et al.  The information seeking behaviour of the users of digital scholarly journals , 2006, Inf. Process. Manag..

[20]  Jerome A. Rolia,et al.  Characterizing the scalability of a large web-based shopping system , 2001, ACM Trans. Internet Techn..

[21]  Myra Spiliopoulou,et al.  Web usage mining for Web site evaluation , 2000, CACM.

[22]  Carey L. Williamson,et al.  Internet Web servers: workload characterization and performance implications , 1997, TNET.

[23]  David Nicholas,et al.  Evaluating consumer website logs: a case study of The Times/The Sunday Times website , 2000, J. Inf. Sci..

[24]  Mark Levene,et al.  Associating search and navigation behavior through log analysis: Research Articles , 2005 .

[25]  Carol Tenopir,et al.  What deep log analysis tells us about the impact of big deals: case study OhioLINK , 2006, J. Documentation.

[26]  James E. Pitkow Summary of WWW characterizations , 2004, World Wide Web.

[27]  Xuan Wang,et al.  A Contribution Towards Solving the Web Workload Puzzle , 2006, International Conference on Dependable Systems and Networks (DSN'06).

[28]  Christos Faloutsos,et al.  Identifying Web Browsing Trends and Patterns , 2001, Computer.

[29]  M. B. Wilk,et al.  An Analysis of Variance Test for the Exponential Distribution (Complete Samples) , 1972 .

[30]  H. Rittel,et al.  Dilemmas in a general theory of planning , 1973 .

[31]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[32]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[33]  Daqing He,et al.  Detecting session boundaries from Web user logs , 2000 .

[34]  Myra Spiliopoulou,et al.  Measuring the Accuracy of Sessionizers for Web Usage Analysis , 2001 .

[35]  Dale Schuurmans,et al.  Dynamic Web log session identification with statistical language models , 2004, J. Assoc. Inf. Sci. Technol..

[36]  James E. Pitkow,et al.  Characterizing Browsing Strategies in the World-Wide Web , 1995, Comput. Networks ISDN Syst..

[37]  Mike Shema Cross-Site Scripting , 2010 .

[38]  Virgílio A. F. Almeida,et al.  A methodology for workload characterization of E-commerce sites , 1999, EC '99.

[39]  MAGDALINI EIRINAKI,et al.  Web mining for web personalization , 2003, TOIT.

[40]  Katerina Goseva-Popstojanova,et al.  Empirical Characterization of Session–Based Workload and Reliability for Web Servers , 2006, Empirical Software Engineering.

[41]  Lefteris Angelis,et al.  Model-Based Cluster Analysis for Web Users Sessions , 2005, ISMIS.