research-article

Service Ratio-Optimal, Content Coherence-Aware Data Push Systems

Authors:

Christos Liaskos,

Ageliki TsioliaridouAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 6, Issue 4

Article No.: 15, Pages 1 - 23

https://doi.org/10.1145/2850423

Published: 13 January 2016 Publication History

Abstract

Advertising new information to users via push is the trigger of operation for many contemporary information systems. Furthermore, passive optical networks are expected to extend the reachability of high-quality push services to thousands of clients. The efficiency of a push service is the ratio of successfully informed users. However, pushing only data of high popularity can degrade the thematic coherency of the content. The present work offers a novel, analysis-derived, tunable way for selecting data for push services. The proposed scheme can maximize the service ratio of a push system with regard to data coherence constraints. Extensive simulations demonstrate the efficiency of the scheme compared to alternative solutions. The proposed scheme is the first to tackle the problem of data coherence-aware, service ratio optimization of push services.

References

[1]

Swarup Acharya, Rafael Alonso, Michael Franklin, and Stanley Zdonik. 1995. Broadcast disks. ACM SIGMOD Record 24, 2 (1995), 199--210.

Digital Library

[2]

Gediminas Adomavicius and Jingjing Zhang. 2012. Impact of data characteristics on recommender systems performance. ACM Transactions on Management Information Systems 3, 1 (2012), 3:1--3:17. http://doi.acm.org/10.1145/2151163.2151166.

Digital Library

[3]

Mohammad Yahya H. Al-Shamri and Kamal K. Bharadwaj. 2007. A compact user model for hybrid movie recommender system. In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'07). 519--524.

Digital Library

[4]

David Arthur and Sergei Vassilvitskii. 2007. K-means++: The advantages of careful seeding. In Proceedings of the 18th annual ACM-SIAM Symposium on Discrete Algorithms (SODA'07). Society for Industrial and Applied Mathematics, 1027--1035.

Digital Library

[5]

Umut Balli, Haisang Wu, Binoy Ravindran, Jonathan Anderson, and Daglas Jensen. 2007. Utility accrual real-time scheduling under variable cost functions. IEEE Transactions on Computers 56, 3 (2007), 385--401.

Digital Library

[6]

Linas Baltrunas, Tadas Makcinskas, and Francesco Ricci. 2010. Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys'10). ACM, New York, NY, 119--126.

Digital Library

[7]

Shlomo Berkovsky and Jill Freyne. 2010. Group-based recipe recommendations: Analysis of data aggregation strategies. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys'10). ACM, New York, NY, 111--118.

Digital Library

[8]

James C. Bezdek and Richard J. Hathaway. 2002. Some notes on alternating optimization. In Advances in Soft Computing — AFSS 2002, Nikhil R. Pal and Michio Sugeno (Eds.). Lecture Notes in Computer Science, Vol. 2275. Springer, Berlin, 288--300.

Digital Library

[9]

James C. Bezdek and Richard J. Hathaway. 2003. Convergence of alternating optimization. Neural, Parallel Scientific Computing 11, 4 (2003), 351--368. http://dl.acm.org/citation.cfm?id=964885.964886.

Digital Library

[10]

Derya Birant and Alp Kut. 2007. ST-DBSCAN: An algorithm for clustering spatial--temporal data. Data & Knowledge Engineering 60, 1 (2007), 208--221.

Digital Library

[11]

Chih-Lin Hu and Ming-Syan Chen. 2009. Online scheduling sequential objects with periodicity for dynamic information dissemination. IEEE Transactions on Knowledge and Data Engineering 21, 2 (2009), 273--286.

Digital Library

[12]

Sergio Cleger-Tamayo, Juan M. Fernández-Luna, and Juan F. Huete. 2012. Top-N news recommendations in digital newspapers. Knowledge-Based Systems 27 (2012), 180--189. j.knosys.2011.11.017

Digital Library

[13]

R. M. Corless, G. H. Gonnet, Hare, D. E. G., D. J. Jeffrey, and D. E. Knuth. 1996. On the lambertw function. Advances in Computational Mathematics 5, 1 (1996), 329--359.

[14]

Andrew Crossen, Jay Budzik, and Kristian J. Hammond. 2002. Flytrap: Intelligent group music recommendation. In Proceedings of the 7th International Conference on Intelligent User Interfaces (IUI'02). ACM, New York, NY, 184--185.

Digital Library

[15]

Abhinandan S. Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web (WWW'07). ACM, New York, NY, 271--280.

Digital Library

[16]

M. Daszykowski, B. Walczak, and D. L. Massart. 2001. Looking for natural patterns in data. Chemometrics and Intelligent Laboratory Systems 56, 2 (2001), 83--92.

[17]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96). Portland, Oregon, USA, 226--231.

Digital Library

[18]

Oren Etzioni, Jorg P. Muller, Jeffrey M. Bradshaw, and Nicholas Kushmerick. 1999. Learning to remove internet advertisements. In Proceedings of the 3rd Annual Conference on Autonomous Agents (AGENTS'99). ACM Press, 175--181.

Digital Library

[19]

Kerim Fouli, Martin Maier, and Muriel Medard. 2011. Network coding in next-generation passive optical networks. IEEE Communications Magazine 49, 9 (2011), 38--46.

[20]

Inma Garcia, Laura Sebastia, and Eva Onaindia. 2011. On the design of individual and group recommender systems for tourism. Expert Systems with Applications 38, 6 (2011), 7683--7692. 10.1016/j.eswa.2010.12.143.

Digital Library

[21]

Jane Grossman, Michael Grossman, and Robert Katz. 1980. The First Systems of Weighted Differential and Integral Calculus. Archimedes Foundation.

[22]

Hiroki Ikeda, Jun Sugawa, Yoshihiro Ashi, and Kenichi Sakamoto. 2007. High-definition IPTV broadcasting architecture over gigabit-capable passive optical network. In Proceedings of the 2007 IEEE Global Telecommunications Conference (GLOBECOM'07). IEEE, 2242--2246.

[23]

ITU-T. 2010. 1 0-Gigabit-capable passive optical networks (XG-PON): Transmission convergence (TC) layer specification. {Online} https://www.itu.int/rec/T-REC-G.987.3/en.

[24]

J. Gecsei. 1983. The Architecture of Videotex Systems. Prentice-Hall, Englewood Cliffs, NJ.

Digital Library

[25]

Anthony Jameson and Barry Smyth. 2007. Recommendation to groups. In The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.). Lecture Notes in Computer Science, Vol. 4321. Springer, Berlin, 596--627.

Digital Library

[26]

Shu Jiang and Nitin H. Vaidya. 1999. Scheduling data broadcast to “impatient” users. In Proceedings of the 1st ACM International Workshop on Data Engineering for Wireless and Mobile Access (MoBiDe'99). ACM, 52--59.

Digital Library

[27]

Jianliang Xu, Xueyan Tang, and Wang-Chien Lee. 2006. Time-critical on-demand data broadcast: Algorithms, analysis, and performance evaluation. IEEE Transactions on Parallel and Distributed Systems 17, 1 (2006), 3--14.

Digital Library

[28]

V. Kakali, P. Sarigiannidis, Georgios Papadimitriou, and A. Pomportsis. 2011. A novel adaptive framework for wireless push systems based on distributed learning automata. Wireless Personal Communications 57, 4 (2011), 591--606.

Digital Library

[29]

Sang Hyuk Kang, Sujeong Choi, Seong Jong Choi, Gwangsoon Lee, Jaeug Lew, and Jun Lee. 2007. Scheduling data broadcast based on multi-frequency in mobile interactive broadcasting. IEEE Transactions on Broadcasting 53, 1 (2007), 405--411.

[30]

Sunho Kim and Sang H. Kang. 2010. Scheduling data broadcast: An efficient cut-off point between periodic and on-demand data. IEEE Communications Letters 14, 12 (2010), 1176--1178.

Digital Library

[31]

Hans-Peter Kriegel, Peer Kröger, and Arthur Zimek. 2009. Clustering high-dimensional data. ACM Transactions on Knowledge Discovery from Data 3, 1 (2009), 1--58.

Digital Library

[32]

H. J. Lee and Sung Joo Park. 2007. MONERS: A news recommender for the mobile web. Expert Systems with Applications 32, 1 (2007), 143--150.

[33]

Christos Liaskos and Georgios Papadimitriou. 2012. Entropy-based estimation of client preferences in wireless push systems. IEEE Transactions on Communications 60, 12 (2012), 3899--3908.

[34]

Christos Liaskos, Georgios Papadimitriou, Petros Nicopolitidis, and Andreas Pomportsis. 2012. Parallel data broadcasting for optimal client service ratio. IEEE Communications Letters 16, 11 (2012), 1741--1743.

[35]

Christos Liaskos, Sophia Petridou, and Georgios Papadimitriou. 2011. Towards realizable, low-cost broadcast systems for dynamic environments. IEEE/ACM Transactions on Networking 19, 2 (2011), 383--392.

Digital Library

[36]

Christos Liaskos, Angeliki Tsioliaridou, and Georgios Papadimitriou. 2012. More for less: Getting more clients by broadcasting less data. In Proceedings of the 10th International Conference on Wired/Wireless Internet Communications (WWIC'12). 64--75.

Digital Library

[37]

Christos Liaskos, Angeliki Tsioliaridou, and Georgios Papadimitriou. 2013. On data compatibility and broadcast stream formation. IEEE Transactions on Computers 63, 9 (2013), 2369--2375.

Digital Library

[38]

Christos Liaskos, Andreas Xeros, Georgios Papadimitriou, Marios Lestas, and Andreas Pitsillides. 2012. Broadcast scheduling with multiple concurrent costs. IEEE Transactions on Broadcasting 58, 2 (2012), 178--186.

[39]

Ee-Peng Lim, Hsinchun Chen, and Guoqing Chen. 2013. Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS) 3, 4 (2013), 17.

Digital Library

[40]

Han Man-Soo. 2012. Iterative dynamic bandwidth allocation for XGPON. In Proceedings of the 14th International Conference on Advanced Communication Technology. 1035--1040.

[41]

Judith Masthoff. 2004. Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction 14, 1 (2004), 37--85. B:USER.0000010138.79319.fd

Digital Library

[42]

MathWorks-Inc. 2010. MATLAB version (R2010a). Retrieved from http://www.mathworks.com/products/ matlab/.

[43]

Bradley N. Miller, Istvan Albert, Shyong K. Lam, Joseph A. Konstan, and John Riedl. 2003. MovieLens unplugged: Experiences with an occasionally connected recommender system. In Proceedings of the 8th ACM International Conference on Intelligent User Interfaces (ACM IUI'03). Miami, Florida, USA, 263--266.

Digital Library

[44]

Petros Nicopolitidis, Georgios Papadimitriou, and Andreas Pomportsis. 2002. Using learning automata for adaptive push-based data broadcasting in asymmetric wireless environments. IEEE Transactions on Vehicular Technology 51, 6 (2002), 1652--1660.

[45]

Mark O'Connor, Dan Cosley, Joseph A. Konstan, and John Riedl. 2001. PolyLens: A recommender system for groups of users. In Proceedings of the 7th European Conference on Computer Supported Cooperative Work. 199--218.

Digital Library

[46]

Georgios Papadimitriou, Mohamed Obaidat, and Andreas Pomportsis. 2002. On the use of learning automata in the control of broadcast networks: A methodology. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, 6 (2002), 781--790.

Digital Library

[47]

Jing Peng, Daniel D. Zeng, and Zan Huang. 2008. Latent subject-centered modeling of collaborative tagging: An application in social search. ACM Transactions on Management Information Systems 2, 3 (2008), 15:1--15:23. http://doi.acm.org/10.1145/2019618.2019621

Digital Library

[48]

L. Pietronero, E. Tosatti, V. Tosatti, and A. Vespignani. 2001. Explaining the uneven distribution of numbers in nature: The laws of Benford and Zipf. Physica A: Statistical Mechanics and its Applications 293, 1--2 (2001), 297--304.

[49]

Sebastiano Pizzutilo, Berardina de Carolis, Giovanni Cozzolongo, and Francesco Ambruoso. 2005. Group modeling in a public space: Methods, techniques, experiences. In Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications (AIC'05). World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, 175--180.

Digital Library

[50]

Till Plumbaum, Andreas Lommatzsch, Stefan Rudnitzki, E. W. D. Luca, H. Dwiger, and S. Albayrak. 2010. Adaptive music news recommendations based on large semantic datasets. In Proceedings of the 1st Workshop on Music Recommendation And Discovery (WOMRAD).

[51]

R. Polikar. 2006. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6, 3 (2006), 21--45.

[52]

Majid Raissi-Dehkordi and John S. Baras. 2007. Broadcast scheduling for time-constrained information delivery. In Proceedings of the 2007 IEEE Global Telecommunications Conference (GLOBECOM'07). 5298--5303.

[53]

Nutcha Rattanajitbanjong and Saranya Maneeroj. 2009. Multi criteria pseudo rating and multidimensional user profile for movie recommender system. In 2009 2nd IEEE International Conference on Computer Science and Information Technology. 596--601.

[54]

Huaxia Rui and Andrew Whinston. 2012. Designing a social-broadcasting-based business intelligence system. ACM Transactions on Management Information Systems 2, 4 (2012), 22:1--22:19. http://doi.acm.org/ 10.1145/2070710.2070713.

Digital Library

[55]

Uwe Schoning. 1999. A probabilistic algorithm for k-SAT and constraint satisfaction problems. In Proceedings of the 40th Annual Symposium on Foundations of Computer Science. 410--414. 10.1109/SFFCS.1999.814612

Digital Library

[56]

Dimitrios Serpanos. 2004. Scheduling objects in broadcast systems with energy-limited clients. Computer Communications 27, 10 (2004), 1036--1042.

Digital Library

[57]

University of California Irvine, School of Information and Computer Sciences. 1998. Machine Learning Repository: Internet Advertisements Data Set. Retrieved from https://archive.ics.uci.edu/ml/ datasets/Internet+Advertisements.

[58]

University of Eastern Finland, School of Computing. 2012. Clustering datasets: S-sets. Retrieved from http://cs.joensuu.fi/sipu/datasets/.

[59]

Ramaprasad Unni and Robert Harmon. 2007. Perceived effectiveness of push vs. pull mobile location-based advertising. Journal of Interactive Advertising 7, 2 (2007), 28--40.

[60]

Nitin H. Vaidya and Sohail Hameed. 1999. Scheduling data broadcast in asymmetric communication environments. Wireless Networks 5, 3 (1999), 171--182.

Digital Library

[61]

Rohit Valecha, Raj Sharman, H. Raghav Rao, and Shambhu Upadhyaya. 2013. A dispatch-mediated communication model for emergency response systems. ACM Transactions on Management Information Systems 4, 1 (2013), 2:1--2:25. http://doi.acm.org/10.1145/2445560.2445562.

Digital Library

[62]

András Varga and Babak Fakhamzadeh. 1997. The K-split algorithm for the PDF approximation of multi-dimensional empirical distributions without storing observations. In Proceedings of the 9th European Simulation Symposium (ESS'97). 94--98.

[63]

Zhiwen Yu, Xingshe Zhou, Yanbin Hao, and Jianhua Gu. 2006. TV program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction 16, 1 (2006), 63--82.

Digital Library

[64]

Cheng Zhan, Lee, Victor C. S., Jianping Wang, and Yinlong Xu. 2011. Coding-based data broadcast scheduling in on-demand broadcast. IEEE Transactions on Wireless Communications 10, 11 (2011), 3774--3783.

[65]

Zhu Zhang, Daniel D. Zeng, Ahmed Abbasi, Jing Peng, and Xiaolong Zheng. 2013. A random walk model for item recommendation in social tagging systems. ACM Transactions on Management Information Systems 4, 2 (2013), 8.

Digital Library

[66]

Baihua Zheng, Xia Wu, Xing Jin, and Dik Lun Lee. 2005. TOSA: A near-optimal scheduling algorithm for multi-channel data broadcast. In Proceedings of the 6th International Conference on Mobile Data Management (MDM'05). 29--37.

Digital Library

Cited By

(2019)CDLBInternational Journal of Computational Science and Engineering10.5555/3302674.330268018:1(44-53)Online publication date: 9-Feb-2019
https://dl.acm.org/doi/10.5555/3302674.3302680
Nicopolitidis P(2017)On efficient downlink channel aggregation in adaptive wireless push systemsInternational Journal of Communication Systems10.1002/dac.326230:12Online publication date: 20-Jan-2017
https://doi.org/10.1002/dac.3262

Index Terms

Service Ratio-Optimal, Content Coherence-Aware Data Push Systems

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 6, Issue 4

January 2016

73 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2869770

Editor:
Alexander Tuzhilin
New York University, USA

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Copyright © 2016 ACM.

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Publication History

Published: 13 January 2016

Accepted: 01 November 2015

Revised: 01 September 2015

Received: 01 March 2014

Published in TMIS Volume 6, Issue 4

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European Union (European Social Fund)
Operational Program “COOPERATION” of the National Strategic Reference Framework (NSRF) - Research Funding Program: “PANDA: Asymmetric Passive Optical Network for xDSL and FTTH Access”

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Cited By

(2019)CDLBInternational Journal of Computational Science and Engineering10.5555/3302674.330268018:1(44-53)Online publication date: 9-Feb-2019
https://dl.acm.org/doi/10.5555/3302674.3302680
Nicopolitidis P(2017)On efficient downlink channel aggregation in adaptive wireless push systemsInternational Journal of Communication Systems10.1002/dac.326230:12Online publication date: 20-Jan-2017
https://doi.org/10.1002/dac.3262

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