Skip to main content

Abstract

Data mining is based on a simple analogy. The growth of data warehousing has created mountains of data. The mountains represent a valuable resource to the enterprise. But to extract value from these mountains, we must “mine” for the gold in data warehouses and data marts. Everywhere that there are data warehouses, data mines are also constructed. Data visualization has the ability to present a great deal of information in a user friendly format. It is well known that humans comprehend visual information much quicker and more efficiently than verbal information. “A picture is worth a thousand words.” Successful visualizations can reduce the time it takes to get the information, make sense out of it, and enhance creative thinking. Great strides have been made in the area of computer generated data visualizations in recent years. This paper discusses visual data mining techniques for analyzing real forensic data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C.Brunk, R. Kohavi. Silicon Graphics, Inc., “Data Mining and Visualization.” 25 Sept. 1998. http://ai.stanford.edu/∼ ronnyk/minesetHB.pdf#search=%22History%20of%20Mineset%22.>

    Google Scholar 

  2. G.Conti, M. Ahamad, and J. Stasko, “Attacking Information Visualization System Usability Overloading and Deceiving the Human”, SOUPS 2005, Pittsburgh, PA, USA, July 6-8 2005.p.89-100.

    Google Scholar 

  3. N. Gershon, S. Card, and S. Eick, “Information Visualization Tutorial”. ACM-CHI99, May 15-20 1999, p.149-150.

    Google Scholar 

  4. G. Giuseppini, and M. Burnett, Microsoft Log Parser Toolkit, Syngress, Rockland, 2004.

    Google Scholar 

  5. J. Heer, S. Card, and J. Landay, “Prefuse: A Toolkit for Interactive Information Visualization” ACM-CHI 2005, April 2-7, 2005, Portland Oregon, USA, p. 421-430.

    Google Scholar 

  6. M. Heo, and S. Hirtle, “An Empirical Comparison of Visualization Tools to Assist Information Retrieval on the Web”, Journal of the American Society for Information Science and Technology, 52(8), p.666-675, 2001.

    Google Scholar 

  7. H. Hochheiser and B. Shneiderman, “Using Interactive Visualizations of WWW Log Data to Characterize Access Patterns and Inform Site Design”, Journal of the American Society for Information Science and Technology, 52(4), p. 331-343, 2001.

    Article  Google Scholar 

  8. M. Kantardzic, J. Zurada, Next Generation of Data-Mining Applications, John Wiley & Sons, Inc., Hoboken, New Jersey, 2005.

    MATH  Google Scholar 

  9. K. Paviou, and R. Snodgrass, “Forensic Analysis of Database Tampering”, ACM-SIGMOD’06, June 27-29, 2006, p.109-120, 2006.

    Google Scholar 

  10. R. Pilat, E. Valiati, and C. Freitas, “Experimental Study on Evaluation of Multidimensional Information Visualization Techniques”, CLIHC’05, October 23-26, 2005, p. 20-30.

    Google Scholar 

  11. Purple Insight. Oct.1 2006. http://www.purpleinsight.com/products/index/shtml>.

    Google Scholar 

  12. T. Saracevic, “Information Science, Journal of the American Society for Information Science,50(12), p. 1051-1063, 1999.

    Article  Google Scholar 

  13. J.Schauer, “Silicon Graphics, Powerful Solutions”. May 1998. http://www.dmreview.com/article_sub.cfm?articleId=603>.

    Google Scholar 

  14. A. Shen-Hsieh, and M. Schindler, “Data Visualization for Strategic Decision Making”, American Institute of Graphic Arts Experience Case Study Archive, p.1-17, 2002.

    Google Scholar 

  15. “SGI Announces Agreement with Purple Insight for MineSet Data Mining and Real-Time 3D Visualization Software.” 2003. http://www.sgi.com/company_info/newsroom/press_releases/2003/october/purple.html>.

    Google Scholar 

  16. M. Souppaya, and K. Kent, Guide to Computer Security Log Management (Draft), National Institute of Standards and Technology. Aithersburg, MD, 2006.

    Google Scholar 

  17. N. Ye The Handbook of Data Mining, Lawrence Erlbaum Assoiates, New Jersey, 2003.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer

About this paper

Cite this paper

Francia, G.I., Trifas, M., Brown, D., Francia, R., Scott, C. (2007). Visual Data Mining of Log Files. In: Sobh, T. (eds) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6268-1_93

Download citation

  • DOI: https://doi.org/10.1007/978-1-4020-6268-1_93

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-6267-4

  • Online ISBN: 978-1-4020-6268-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics