Paper Info
Title
Trend analysis and prediction in multimedia-on-demand systems
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
Year
DOI
Venue
2001
10.1109/ICC.2001.936905
Communications, 2001. ICC 2001. IEEE International Conference
Keywords
Field
DocType
Internet,broadband networks,interactive systems,multimedia communication,statistical analysis,CRHU,CRU,EES regression,EPS regression,FRS,HU,MOD,broadband Internet,continuous regression updating,continuous regression with historical updating,exponential regression,exponential-exponential-sum regression,exponential-power-sum regression,fixed regression selecting,forecasting,historical trends,historical updating,incoming viewing request data,linear regression,multimedia content,multimedia files,multimedia-on-demand systems,power regression,prediction algorithm,regression models,resource-demanding services,trend analysis,viewing behaviour,viewing trend
Data mining,Trend analysis,Algorithm design,Regression,Computer science,Regression analysis,Statistics,Internet access,Goodness of fit,Multimedia,The Internet,Linear regression
Conference
Volume
ISBN
Citations 
4
0-7803-7097-1
0
PageRank 
References 
Authors
0.34
2
4
Name
Order
Citations
PageRank
Danny M. P. Ng100.34
Eric W. M. Wong21059.70
King-Tim Ko333328.73
K. S. Tang41288127.58