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A user-friendly self-similarity analysis tool
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Volume 33 ,  Issue 3  (July 2003) table of contents
COLUMN: Tools table of contents
Pages: 81 - 93  
Year of Publication: 2003
ISSN:0146-4833
Authors
Thomas Karagiannis  University of California, Riverside
Michalis Faloutsos  University of California, Riverside
Mart Molle  University of California, Riverside
Publisher
ACM  New York, NY, USA
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ABSTRACT

The concepts of self-similarity, fractals, and long-range dependence (LRD) have revolutionized network modeling during the last decade. However, despite all the attention these concepts have received, they remain difficult to use by non-experts. This difficulty can be attributed to a relative complexity of the mathematical basis, the absence of a systematic approach to their application and the absence of publicly available software. In this paper, we introduce SELFIS, a comprehensive tool, to facilitate the evaluation of LRD by practitioners. Our goal is to create a stand-alone public tool that can become a reference point for the community. Our tool integrates most of the required functionality for an in-depth LRD analysis, including several LRD estimators. In addition, SELFIS includes a powerful approach to stress-test the existence of LRD, Using our tool, evidence are presented that the widely-used LRD estimators can provide misleading results. It is worth mentioning that 25 researchers have acquired SELFIS within a month of its release, which clearly demonstrates the need for such a tool.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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The SELFIS Tool. http://www.cs.ucr.edu/~tkarag.
 
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T. Karagiannis and M. Faloutsos. SELFIS: A Tool For Self-Similarity and Long-Range Dependence Analysis. In 1st Workshop on Fractals and Self-Similarity in Data Mining: Issues and Approaches (in KDD), Edmonton, Canada, July 23, 2002.
 
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T. Karagiannis, M. Faloutsos, and R. Riedi. Long-Range dependence: Now you see it, now you don't! In IEEE GLOBECOM, Global Internet Symposium, 2002.
 
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Collaborative Colleagues:
Thomas Karagiannis: colleagues
Michalis Faloutsos: colleagues
Mart Molle: colleagues