Abstract
In pervasive environments, the Pub/Sub paradigm is regarded as an important means of information sharing and event dissemination. In this paper, we first analyze different context in Pub/Sub systems that has remarkable impacts upon user’s satisfaction to event dissemination and then give corresponding strategies by exploiting time context and event-preference context so as to provide personalized event dissemination. That is, by leveraging time context, we provide the extended matching against long-standing events, and by leveraging event-preference context, we present the recommendation algorithm which is based on hidden Markov process. Performance analysis and experiment evaluation show that both strategies can improve user’s experiences of event dissemination.
Similar content being viewed by others
References
Eugster PT, Felber PA, Guerraoui R, Kermarrec AM (2003) The many faces of publish/subscribe. ACM Comput Surv 35(2):114–131
Carzaniga A, Rosenblum D, Wolf AL (2001) Design and evaluation of a wide-area event notification service. ACM Trans Comput Syst 19(3):332–383
Cugola G, de Cote JEM (2005) On introducing location awareness in publish-subscribe middleware. In: Proceedings of the 25th IEEE international conference on distributed computing systems workshops (ICDCSW’05)
Fiege L, Gärtner FC, Kasten O, Zeidler A (2003) Supporting mobility in content-based publish/subscribe middleware. In: The 4th ACM/IFIP/USENIX international middleware conference
Eugster P, Garbinato B, Holzer A (2008) Design and implementation of the pervaho middleware for mobile context-aware applications. In: International MCETECH conference on e-technologies, pp 125–135
Li GL, Jacobsen HA (2005) Composite subscriptions in content-based publish/subscribe systems. In: The 6th ACM/IFIP/USENIX international middleware conference, pp 249–269
Mansouri S, Sloman M (1997) A generalized event monitoring language for distributed systems. IEE/IOP/BCS Distrib Syst Eng J 4(2):96–108
Pretschner A (1999) Ontology based personalized search. MS Thesis, University of Kansas, Lawrence
Mobasher B, Cooley R, Srivastava J (2000) Automatic personalization based on web usage mining. Commun ACM 43(8):142–151
Konstan J, Miller B, Maltz D et al (1997) GroupLens: applying collaborative filtering to usenet news. Commun ACM 40(3):77–87
Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold starting problem. Inf Sci 178(1):37–51
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on WWW
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 60970027 and the National Hi-Tech Research and Development 863 Program of China under Grant No. 2008AA04A105.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Lin, C., Jin, B., Long, Z. et al. On context-aware distributed event dissemination. Pers Ubiquit Comput 15, 305–314 (2011). https://doi.org/10.1007/s00779-010-0330-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-010-0330-8