Modeling and Predicting Temporal Patterns of Web Content Changes

Daniele Tessera, M. Calzarossa*

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

17 Citazioni (Scopus)

Abstract

The technologies aimed at Web content discovery, retrieval and management face the compelling need of coping with its highly dynamic nature coupled with complex user interactions. This paper analyzes the temporal patterns of the content changes of three major news websites with the objective of\r\nmodeling and predicting their dynamics. It has been observed that changes are characterized by a time\r\ndependent behavior with large\r\nfluctuations and significant differences across hours and days. To explain this behavior, we represent the change patterns as time series. The trend and seasonal components of\r\nthe observed time series capture the weekly and daily periodicity, whereas the irregular components\r\ntake into account the remaining\r\nfluctuations. Models based on trigonometric polynomials and ARMA\r\ncomponents accurately reproduce the dynamics of the empirical change patterns and provide extra-\r\npolations into the future to be used for forecasting.
Lingua originaleEnglish
pagine (da-a)115-123
Numero di pagine9
RivistaJournal of Network and Computer Applications
Volume2015
Numero di pubblicazione56
DOI
Stato di pubblicazionePubblicato - 2015
Pubblicato esternamente

All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.1700.1706???
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Keywords

  • Time series analysis
  • Web dynamics

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