Trend analysis and prediction in multimedia-on-demand systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

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Detail(s)

Original languageEnglish
Pages (from-to)1292-1298
Journal / PublicationIEEE International Conference on Communications
Volume4
Publication statusPublished - 2001

Conference

TitleInternational Conference on Communications (ICC2001)
PlaceFinland
CityHelsinki
Period11 - 14 June 2001

Abstract

Resource-demanding services such as Multimedia-on-Demand (MOD) become possible as broadband Internet is getting more popular. However, as the size of multimedia files grows rapidly, storage of such large files becomes a problem. Since multimedia contents will generally become less popular with time, it is desirable to design a prediction algorithm so that the multimedia content can be unloaded from the server when it becomes unpopular. In this paper, we have two objectives: 1) analyse the MOD viewing trend in order to understand the viewing behaviour of users, 2) predict the viewing trend based on the knowledge obtained from the trend analysis. For trend analysis, we study three traditional regression models, including linear regression, exponential regression, and power regression, and propose two additive regression models, exponential-exponential-sum (EES) and exponential-power-sum (EPS), to improve the goodness of fit. Then, the most fitted models will be used in trend prediction. Four prediction app roaches, Fixed Regression Selecting (FRS), Continuous Regression Updating (CRU), Historical Updating (HU), and Continuous Regression with Historical Updating (CRHU) are proposed. From the numerical results, we find that CRHU, which is constructed by considering historical trend and new incoming viewing request data, is in general the best method in forecasting MOD trend.

Research Area(s)

  • Additive regression, Continuous regression updating, Continuous regression with historical updating, Fixed regression selecting, Historical updating, Multimedia-on-demand, Regression models, Trend analysis, Trend prediction