Http Log Analysis: An Approach to Studying the Use of Web-Based Information Systems

This article documents how log analysis can inform qualitative studies concerning the usage of web-based information systems (WIS). No prior research has used http log files as data to study collaboration between multiple users in organisational settings. We investigate how to perform http log analysis; what http log analysis says about the nature of collaborative WIS use; and how results from http log analysis may support other data collection methods such as surveys, interviews, and observation. The analysis of log files initially lends itself to research designs, which serve to test hypotheses using a quantitative methodology. We show that http log analysis can also be valuable in qualitative research such as case studies. The results from http log analysis can be triangulated with other data sources and for example serve as a means of supporting the interpretation of interview data. It can also be used to generate hypotheses, which were otherwise unthinkable. We suggest that log data be included as a main data source in the field of computer supported cooperative work, information systems, and computer-mediated communication, in order to help clarify the role of the technology related to concepts like coordination, task analysis, or communication.

[1]  Michael J. Gallivan,et al.  Organizational adoption and assimilation of complex technological innovations: development and application of a new framework , 2001, DATB.

[2]  Jesper Simonsen,et al.  Coordinating Work with Groupware: The Challenge of Integrating Protocol and Artefact , 2003, Information Systems Perspectives and Challenges in the Context of Globalization.

[3]  Bernardo A. Huberman,et al.  The laws of the web - patterns in the ecology of information , 2001 .

[4]  Maguelonne Teisseire,et al.  Using data mining techniques on Web access logs to dynamically improve hypertext structure , 1999, LINK.

[5]  David Lorge Parnas,et al.  A rational design process: How and why to fake it , 1986, IEEE Transactions on Software Engineering.

[6]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[7]  A. Parry.  Handbook of Qualitative Research , 2002 .

[8]  Wanda J. Orlikowski,et al.  Evolving the notes: organizational change around groupware technology , 1997 .

[9]  Myra Spiliopoulou,et al.  Improving the Effectiveness of a Web Site with Web Usage Mining , 1999, WEBKDD.

[10]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[11]  Jorgen P. Bansler,et al.  Information systems development with generic systems , 1994, ECIS.

[12]  Wanda J. Orlikowski,et al.  Learning from Notes: organizational issues in groupware implementation , 1992, CSCW '92.

[13]  JoAnne Yates,et al.  Collaborative genres for collaboration: genre systems in digital media , 1997, Proceedings of the Thirtieth Hawaii International Conference on System Sciences.

[14]  P. Tan,et al.  WebSIFT : The Web Site Information Filter , 1999 .

[15]  Nicholas Kushmerick,et al.  Mining web logs for personalized site maps , 2002, Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops), 2002..

[16]  Christian Hidber,et al.  Association Rule Mining , 2017 .

[17]  Shaoping Ma,et al.  Correlation-Based Web Document Clustering for Adaptive Web Interface Design , 2002, Knowledge and Information Systems.

[18]  Jonathan Grudin,et al.  Why CSCW Applications Fail: Problems in the Design and Evaluation of Organization of Organizational Interfaces. , 1988 .

[19]  Jonathan Grudin,et al.  Groupware and social dynamics: eight challenges for developers , 1994, CACM.

[20]  John L. Bennett,et al.  Learning from user experience with groupware , 1990, CSCW '90.

[21]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[22]  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.

[23]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[24]  Terry Winograd,et al.  A language/action perspective on the design of cooperative work , 1986, CSCW '86.

[25]  Torben Bach Pedersen,et al.  Analyzing clickstreams using subsessions , 2000, DOLAP '00.

[26]  Carla Simone,et al.  Coordination mechanisms: Towards a conceptual foundation of CSCW systems design , 1996, Computer Supported Cooperative Work (CSCW).

[27]  E. Rogers,et al.  Diffusion of Innovations, 5th Edition , 2003 .

[28]  W. Orlikowski,et al.  Genre Repertoire: The Structuring of Communicative Practices in Organizations , 1994 .

[29]  Maurice D. Mulvenna,et al.  Discovering Internet marketing intelligence through online analytical web usage mining , 1998, SGMD.

[30]  W. Orlikowski,et al.  Genres of Organizational Communication: A Structurational Approach to Studying Communication and Media , 1992 .

[31]  John Bowers,et al.  The work to make a network work: studying CSCW in action , 1994, CSCW '94.

[32]  Alex G. Büchner.  Discovering Internet Marketing Intelligence through Web Log Mining , 2003 .

[33]  Kjeld Schmidt,et al.  Taking CSCW Seriously: Supporting Articulation Work * , 1992 .

[34]  PatternsYongjian,et al.  Clustering of Web Users Based on Access , 1999 .

[35]  Tao Luo,et al.  Effective personalization based on association rule discovery from web usage data , 2001, WIDM '01.

[36]  Jorgen P. Bansler,et al.  Knowledge Sharing in Heterogeneous Groups: An Empiri- cal Study of IT-Support for Sharing Better Practices , 2002 .

[37]  Claudio U. Ciborra,et al.  Groupware and teamwork: invisible aid or technical hindrance? , 1997 .

[38]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.