Trend Analysis and Prediction with Historical Request Data for Multimedia-on-Demand Systems
Summary :
Resource-demanding services such as Multi-media-on-Demand (MOD) become possible as Internet and broadband connections are getting more popular. However, as the sizes of multimedia files grow 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 if it is no longer popular. This can relieve the storage problem in an MOD system, and hence spare more space for new multimedia files. In this paper, we analyse the MOD viewing trend in order to understand the viewing behaviour of users and predict the viewing trend of a particular category of multimedia based on the knowledge obtained from its trend analysis. In trend analysis, two additive regression models, exponential-exponential-sum (EES) and exponential-power-sum (EPS), are proposed to improve the fitness of the trend. The most suitable model will then be used for trend prediction based on four proposed approaches, namely Fixed Regression Selection (FRS), Continuous Regression Updating (CRU), Historical Updating (HU) and Continuous Regression with Historical Updating (CRHU). From the numerical results, it is found that CRHU, which is constructed by considering historical trend and new incoming data of viewing requests, is in general the best method in forecasting the request trend of a particular category of multimedia clips.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E86-B No.6 pp.2001-2011
- Publication Date
- 2003/06/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Multimedia Systems
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Danny M. P. NG, Eric W. M. WONG, King-Tim KO, Kit-Song TANG, "Trend Analysis and Prediction with Historical Request Data for Multimedia-on-Demand Systems" in IEICE TRANSACTIONS on Communications,
vol. E86-B, no. 6, pp. 2001-2011, June 2003, doi: .
Abstract: Resource-demanding services such as Multi-media-on-Demand (MOD) become possible as Internet and broadband connections are getting more popular. However, as the sizes of multimedia files grow 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 if it is no longer popular. This can relieve the storage problem in an MOD system, and hence spare more space for new multimedia files. In this paper, we analyse the MOD viewing trend in order to understand the viewing behaviour of users and predict the viewing trend of a particular category of multimedia based on the knowledge obtained from its trend analysis. In trend analysis, two additive regression models, exponential-exponential-sum (EES) and exponential-power-sum (EPS), are proposed to improve the fitness of the trend. The most suitable model will then be used for trend prediction based on four proposed approaches, namely Fixed Regression Selection (FRS), Continuous Regression Updating (CRU), Historical Updating (HU) and Continuous Regression with Historical Updating (CRHU). From the numerical results, it is found that CRHU, which is constructed by considering historical trend and new incoming data of viewing requests, is in general the best method in forecasting the request trend of a particular category of multimedia clips.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e86-b_6_2001/_p
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@ARTICLE{e86-b_6_2001,
author={Danny M. P. NG, Eric W. M. WONG, King-Tim KO, Kit-Song TANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Trend Analysis and Prediction with Historical Request Data for Multimedia-on-Demand Systems},
year={2003},
volume={E86-B},
number={6},
pages={2001-2011},
abstract={Resource-demanding services such as Multi-media-on-Demand (MOD) become possible as Internet and broadband connections are getting more popular. However, as the sizes of multimedia files grow 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 if it is no longer popular. This can relieve the storage problem in an MOD system, and hence spare more space for new multimedia files. In this paper, we analyse the MOD viewing trend in order to understand the viewing behaviour of users and predict the viewing trend of a particular category of multimedia based on the knowledge obtained from its trend analysis. In trend analysis, two additive regression models, exponential-exponential-sum (EES) and exponential-power-sum (EPS), are proposed to improve the fitness of the trend. The most suitable model will then be used for trend prediction based on four proposed approaches, namely Fixed Regression Selection (FRS), Continuous Regression Updating (CRU), Historical Updating (HU) and Continuous Regression with Historical Updating (CRHU). From the numerical results, it is found that CRHU, which is constructed by considering historical trend and new incoming data of viewing requests, is in general the best method in forecasting the request trend of a particular category of multimedia clips.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - Trend Analysis and Prediction with Historical Request Data for Multimedia-on-Demand Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 2001
EP - 2011
AU - Danny M. P. NG
AU - Eric W. M. WONG
AU - King-Tim KO
AU - Kit-Song TANG
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E86-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2003
AB - Resource-demanding services such as Multi-media-on-Demand (MOD) become possible as Internet and broadband connections are getting more popular. However, as the sizes of multimedia files grow 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 if it is no longer popular. This can relieve the storage problem in an MOD system, and hence spare more space for new multimedia files. In this paper, we analyse the MOD viewing trend in order to understand the viewing behaviour of users and predict the viewing trend of a particular category of multimedia based on the knowledge obtained from its trend analysis. In trend analysis, two additive regression models, exponential-exponential-sum (EES) and exponential-power-sum (EPS), are proposed to improve the fitness of the trend. The most suitable model will then be used for trend prediction based on four proposed approaches, namely Fixed Regression Selection (FRS), Continuous Regression Updating (CRU), Historical Updating (HU) and Continuous Regression with Historical Updating (CRHU). From the numerical results, it is found that CRHU, which is constructed by considering historical trend and new incoming data of viewing requests, is in general the best method in forecasting the request trend of a particular category of multimedia clips.
ER -
English
