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Trigger Detection Using Geographical Relation Graph for Social Context Awareness

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Abstract

The concept of context awareness is believed to be a key enabler for the new ubiquitous network service paradigm brought by cloud computing platforms and smartphone OSs. In particular, autonomous context-based service customization is becoming an essential tool in this context because users cannot be expected to pick step by step the appropriate network services by manually and explicitly matching preferences for their current context. In this work, we hence focus on the core problem of how to detect changes of context for network services. In turn, detection of such changes can trigger timely system reconfigurations. We introduce a trigger detection mechanism based on a mixed graph-based representation model able to encode geographical and social relationships among people and social objects like stores, restaurants, and event spots. Our mechanism generates a trigger when a significant change in the graph takes place, and it is able to render significant changes in a geographical relationship that holds among objects socially connected with each other. The main benefits of our method are that (1) it does not require building reference models in advance, and (2) it can deal with different kinds of social objects uniformly once the graph is defined. A computer simulation scenario provides evidence on the expected performance of our method.

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References

  1. Buyya R, Yeo CS, Venugopal S (2008) Market-oriented cloud computing: vision, hype, and reality for delivering IT services as computing utilities. In: Proc. of IEEE international conference on high performance computing and communications, pp 5–13

  2. Kenney M, Pon B (2011) Structuring the smartphone industry: is the mobile internet OS platform the key?. Springer J Ind Compet Trade 11(3):239–261

    Article  Google Scholar 

  3. Toninelli A, Corradi A, Montanari R (2008) Semantic-based discovery to support mobile context-aware service access. Comput Commun 31(5):935–949

    Article  Google Scholar 

  4. Mayrhofer R (2005) Context prediction based on context histories: expected benefits, issues and current state-of-the-art. In: Proc. of the 1st international workshop on exploiting context histories in smart environments

  5. Cooley R, Mobasher B, Srivastava J (1999) Data preparation for mining world wide web browsing patterns. J Knowl Inf Syst 1(1):5–32

    Google Scholar 

  6. Srivastava J, Cooley R, Deshpande M, Tan P (2000) Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor Newsl 1(2):2–23

    Article  Google Scholar 

  7. Deshpande M, Karypis G (2001) Selective Markov models for predicting web-page accesses. In: Proc. SDM’2001

  8. Kubo H, Shinkuma R, Takahashi T, Kasai H, Yamaguchi K, Yates R (2011) Demand prediction based on social context for mobile content services. In: Proc. of IEEE international conference on communications workshops, pp 1–5

  9. Bestavros A (1995) Demand-based document dissemination to reduce traffic and balance load in distributed information systems. In: Proc. of international symposium on parallel and distributed processing

  10. Jin S, Bestavros A (2000) Popularity-aware greedy dual-size web proxy caching algorithms. In: Proc. of international conference on distributed computing systems, pp 254–261

  11. Cherkasova L (1998) Improving WWW proxies performance with greedy-dual-size-frequency caching policy. Technical Report HPL-98-69R1, Hewlett-Packard Laboratories

  12. Plangprasopchok A, Lerman K (2010) Modeling social annotation: a bayesian approach. ACM Trans Knowl Discov Data 5(1):1–32

    Article  Google Scholar 

  13. Raphiphan P, Zaslavsky A, Prathombutr P, Meesad P (2009) Context aware traffic congestion estimation to compensate intermittently available mobile sensors. In: Proc. of 2009 tenth international conference on mobile data management: systems, services and middleware, pp 405–410

  14. Chen X, Chen Y, Rao F (2003) An efficient spatial publish/subscribe system for intelligent location-based services. In: Proc. of the 2nd int. workshop on distributed event-based systems (DEBS)

  15. Traag VA, Browet A, Calabrese F, Morlot F (2011) Social event detection in massive mobile phone data using probabilistic location inference. In: Proc. of IEEE international conference on social computing, pp 625–628

  16. Watanabe K, Ochi M, Okabe M, Onai R (2011) Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs. In: Proc. of the 20th ACM international conference on information and knowledge management, pp 2541–2544

  17. Breese JS, Heckerman D, Kadie C (1988) Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the fourteenth conference on uncertainty in artificial intelligence

  18. Sarwar B, Karypis G, Konstan J, Ried J (2001) Item-based collaborative filtering recommendation algorithms. In: Proc. of the 10th international conference on World Wide Web

  19. Dijkman R, Dumas M, García L (2009) Graph matching algorithms for business process model similarity search. In: Proc. of 7th international conference on business process management

  20. Kirsch-Pinheiro M, Vanrompay Y, Berbers Y (2008) Context-aware service selection using graph matching. In: Proc. of 2nd non functional properties and service level agreements in service oriented computing workshop

  21. Lane ND, Miluzzo E, Hong L, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150

    Article  Google Scholar 

  22. Sun G, Chen J, Guo W, Liu KJR (2005) Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs. IEEE Signal Process Mag 22(4):12–23

    Article  Google Scholar 

  23. Joongheon JP, Govindan KR (2010) Energy-efficient rate-adaptive GPS-based positioning for smartphones. in: Proc. of the 8th international conference on mobile systems, applications, and services

  24. Mtibaa A, Harras KA (2011) Social-based trust in mobile opportunistic networks. In: Proc. ICCCN2011 workshop on social interactive media networking and applications

  25. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgement

This work is supported in part by the National Institute of Information and Communications Technology (NICT), Japan, under the new generation network R&D program for innovative network virtualization platform and its application.

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Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Kyoto, Japan

    Takayuki Nishio, Ryoichi Shinkuma & Tatsuro Takahashi

  2. CREATE-NET, Trento, Italy

    Francesco De Pellegrini

  3. The University of Electro-Communications, Chofu, Tokyo, Japan

    Hiroyuki Kasai

  4. Kobe Digital Labo Inc., Kobe, Hyogo, Japan

    Kazuhiro Yamaguchi

Authors
  1. Takayuki Nishio
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  2. Ryoichi Shinkuma
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  3. Francesco De Pellegrini
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  4. Hiroyuki Kasai
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  5. Kazuhiro Yamaguchi
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  6. Tatsuro Takahashi
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Corresponding author

Correspondence to Takayuki Nishio.

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Nishio, T., Shinkuma, R., De Pellegrini, F. et al. Trigger Detection Using Geographical Relation Graph for Social Context Awareness. Mobile Netw Appl 17, 831–840 (2012). https://doi.org/10.1007/s11036-012-0398-7

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  • DOI: https://doi.org/10.1007/s11036-012-0398-7

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