6533b7d4fe1ef96bd1261f7b

RESEARCH PRODUCT

Investigating Long-Range Dependence in E-Commerce Web Traffic

Grażyna SuchackaAdam Domański

subject

h indexWeb serverweb serverSelf-similarityComputer science02 engineering and technologyE-commercecomputer.software_genre01 natural sciencesSet (abstract data type)010104 statistics & probabilityWeb traffichurst indexlong-range dependence0202 electrical engineering electronic engineering information engineeringRange (statistics)0101 mathematicsTRACE (psycholinguistics)Hurst exponenthurst parameterself-similaritybusiness.industryweb trafficHTTP traffic020201 artificial intelligence & image processingData miningbusinesscomputer

description

This paper addresses the problem of investigating long-range dependence (LRD) and self-similarity in Web traffic. Popular techniques for estimating the intensity of LRD via the Hurst parameter are presented. Using a set of traces of a popular e-commerce site, the presence and the nature of LRD in Web traffic is examined. Our results confirm the self-similar nature of traffic at a Web server input, however the resulting estimates of the Hurst parameter vary depending on the trace and the technique used.

yearjournalcountryeditionlanguage
2016-01-01
https://doi.org/10.1007/978-3-319-39207-3_4