NDLI: Trend analysis and prediction in multimedia-on-demand systems

Content Provider	IEEE Xplore Digital Library
Author	Ng, D.M.P. Wong, E.W.M. Ko, K.T. Tang, K.S.
Copyright Year	2001
Description	Author affiliation: Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China (Ng, D.M.P.)
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 are used in trend prediction. Four prediction approaches, 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 trends and new incoming viewing request data, is in general the best method in forecasting MOD trend.
Starting Page	1292
Ending Page	1298
File Size	671679
Page Count	7
File Format	PDF
ISBN	0780370971
DOI	10.1109/ICC.2001.936905
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-06-11
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multimedia systems Streaming media Web server Predictive models Web and internet services Algorithm design and analysis Prediction algorithms Linear regression High-speed networks Ice
Content Type	Text
Resource Type	Article

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