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Link to original content: https://doi.org/10.1007/s12652-015-0288-5
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Service discovery and prediction on Pervasive Information System

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Abstract

Recent evolution of technology and its usages, such as Bring Your Own Device and Internet of Things, transformed the way we interact with Information Systems , leading to a new generation of IS, called the Pervasive Information Systems. These systems have to face heterogeneous pervasive environments and hide the complexity of such environment end-user. In order to reach transparency and proactivity necessary for successful PIS, new discovery and prediction mechanisms are necessary. In this paper, we present a new user-centric approach for PIS and propose new service discovery and prediction based on both user’s context and intentions. Intentions allow focusing on goals user wants to satisfy when requesting a service. Those intentions rise in a given context, which influence the service implementation. We propose a service discovery mechanism that observes user’s context and intention in order to offer him/her the most appropriate service satisfying her/his intention on the current context. We also propose a prediction mechanism that tries to anticipate user’s intentions considering the user’s history and the observed context. We evaluate both mechanisms and discuss advanced features future PIS will have to deal with.

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Notes

  1. In this paper, we use the indices u and svi in order to distinguish information related to the user (index u) from the information obtained from the description of a given service (index svi).

  2. http://jena.sourceforge.net/.

  3. http://www.mindswap.org/2003/pellet/index.shtml.

  4. http://www.semwebcentral.org/projects/owls-tc/.

References

  • Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender Systems Handbook. Springer, US, pp 217–253. doi:10.1007/978-0-387-85820-3_7

    Chapter  Google Scholar 

  • Ali F, Lee SW, Bien Z, Mokhtari M (2008) Combined fuzzy state Q-learning algorithm to predict context aware user activity under uncertainty in assistive environment. In: Ninth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing (SNPD ‘08), pp 57–62. doi:10.1109/SNPD.2008.13

  • Andriole SJ, Bojanova I (2014) Optimizing operational and strategic IT. IEEE IT Profess 16(5):12–15

    Article  Google Scholar 

  • Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiquitous Comput 2(4):263–277

    Article  Google Scholar 

  • Baltrunas L, Ricci F (2013) Experimental evaluation of context-dependent collaborative filtering using item splitting. User Model User-Adapt Interact Spec issue Context-Aware Recomm Syst 24:7–34

    Article  Google Scholar 

  • Baras D, Ronen A, Yom-Tov E (2014) The effect of social affinity and predictive horizon on churn prediction using diffusion modeling. Soc Netw Anal Min Springer 4(1):232. doi:10.1007/s13278-014-0232-2

    Article  Google Scholar 

  • Basiri A, Malek MR (2014) Providing relevant information in an ambient services using service requester’s “logical area”. J Ambient Intell Humaniz Comput Springer 5(4):539–549

    Article  Google Scholar 

  • Broy M, Schmidt A (2014) Challenges in engineering cyber-physical systems. Computer IEEE Comput Soc 47(2):70–72

    Article  Google Scholar 

  • Capilla R, Ortiz O, Hinchey M (2014) Context variability for context-aware systems. Computer IEEE 47(2):85–87. doi:10.1109/MC.2014.33

    Article  Google Scholar 

  • Castro-Leon E (2014) Consumerization in the IT service ecosystem. IEEE IT Prof 16(5):20–27

    Article  Google Scholar 

  • Chaari T, Laforest F, Celentano A (2007) Adaptation in context-aware pervasive information systems: the SECAS project. J Pervasive Comput Commun 3(4):400–425

    Google Scholar 

  • Chang JM, Ho P-C, Chang T-C (2014) Securing BYOD. IEEE IT Prof 16(5):9–11

    Article  Google Scholar 

  • Cremonesi P, Garza P, Quintarelli E, Turrin R (2011) Top-N recommendations on unpopular items with contextual knowledge. In: Adomavicius G, Baltrunas L, Hussein T, Ricci F, Tuzhilin A (Eds) 3rd Workshop on Context-Aware Recommender Systems (CARS) in conjunction with the 5th ACM Conference on Recommender Systems (RecSys 2011), Chicago, USA. CEUR Workshop Proceedings vol 791. http://ceur-ws.org/Vol-791/. Accessed 3 Nov 2014

  • Cubo J, Pimentel E (2012) On the service discovery using context-awareness, semantic matching and behavioural compatibility. In: IEEE 15th international conference on computational science and engineering, pp 259–266. doi:10.1109/ICCSE.2012.43

  • Dey A (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7

    Article  Google Scholar 

  • Earley S, Harmon R, Lee MR, Mithas S (2014) From BYOD to BYOA, Phishing, and Botnets. IEEE IT Prof 16(5):16–18

    Article  Google Scholar 

  • Eikerling H-J, Mazzoleni P, Plaza P, Yankelevich D, Wallet T (2007) Services and mobility: the PLASTIC answer to the Beyond 3G challenge. White paper, Dec 2007. http://plastic.paris-rocquencourt.inria.fr/promotion-material/white_paper_plastic_v1-3.pdf. Accessed Feb 2014

  • Feller W (1968) An introduction to probability theory and its applications. Wiley, New Jersey. ISBN 0-471-25708-7

    MATH  Google Scholar 

  • Fensel D, Facca FM, Simperl E, Toma I (2011) Semantic web services. Springer, Berlin Heidelberg. ISBN 978-3-642-19192-3

    Book  Google Scholar 

  • Foresti GL, Farinosi M, Vernier M (2015) Situational awareness in smart environments: socio-mobile and sensor data fusion for emergency response to disasters. J Ambient Intell Humaniz Comput Springer 6(2):239–257

    Article  Google Scholar 

  • Jaffal A, Kirsch-Pinheiro M, Le Grand B (2014) Unified and conceptual context analysis in ubiquitous environments. In: Mauri JL, Steup C, Knoch S (Eds), 8th international conference on mobile ubiquitous computing, systems, services and technologies (UBICOMM 2014), August 24–28, 2014, Rome, Italy. IARIA, pp 48–55. ISBN:978-1-61208-353-7

  • Javari A, Gharibshah J, Jalili M (2014) Recommender systems based on collaborative filtering and resource allocation. Soc Netw Anal Min Springer 4(1):234. doi:10.1007/s13278-014-0234-0

    Article  Google Scholar 

  • Jones M (2014) Internet of things: shifting from proprietary to standard, ValueWalk, July 2014. http://www.valuewalk.com/2014/07/internet-of-things-iot/. Accessed Oct 2014

  • Kaabi RS, Souveyet C (2007) Capturing intentional services with business process maps. In: Rolland C, Pastor O, Cavarero J (Eds) 1st IEEE international conference on research challenges in information science (RCIS 2007), pp 309–318

  • Kas M, Carley KM, Carley LR (2014) An incremental algorithm for updating betweenness centrality and k-betweenness centrality and its performance on realistic dynamic social network data. Social Netw Anal Min Springer 4(1):235. doi:10.1007/s13278-014-0235-z

    Article  Google Scholar 

  • Kirsch-Pinheiro M, Vanrompay Y, Berbers Y (2008) Context-aware service selection using graph matching. In: Paoli FD, Toma I, Maurino A, Tilly M, Dobson G (Eds.), Proceedings of the 2nd workshop on non functional properties and service level agreements in service oriented computing workshop (NFPSLA-SOC’08) at ECOWS 2008. CEUR Workshop Proceedings vol 411. http://ceur-ws.org/Vol-411/. Accessed Oct 2014

  • König I, Voigtmann C, Klein B, David K (2011). Enhancing alignment based context prediction by using multiple context sources: experiment and analysis. In: Beigl M, Christiansen H, Roth-Berghofer T, Kofod-Petersen A, Coventry K, Schmidtke H (Eds) Modeling and Using Context (Context 2011), Lecture Notes Computer Science, Springer Berlin Heidelberg, vol 6967, pp 159–172

  • Kourouthanassis PE, Giaglis GM (2006) A design theory for pervasive information systems. In: Mostéfaoui SK, Maamar Z, Giaglis GM (Eds) 3rd INTERNATIONAL WORKSHOP ON UBIQUITOUS COMPUTING (IWUC 2006), in conjunction with ICEIS 2006, INSTICC Press, pp 62–70

  • Mayrhofer R (2004) An architecture for context prediction. PhD thesis, Johannes Kepler University of Linz. http://www.mayrhofer.eu.org/downloads/publications/PhD-ContextPrediction-2004.pdf. Accessed 3 Nov 2014

  • Mayrhofer R (2005) Context prediction based on context histories: Expected benefits, issues and current state-of-the-art. In: Prante T, Meyers B, Fitzpatrick G, and Harvel LD (eds), proceeding of the 1st international workshop on exploiting context histories in smart environments (ECHISE 2005), 3rd international conference on pervasive computing (PERVASIVE 2005). http://www.pervasive.ifi.lmu.de/workshops/w8/papers/echise2005-s17-ContextPredictionBasedOnContextHistories-Mayrhofer.pdf. Accessed 3 Nov 2014

  • Mokhtar SB, Kaul A, Georgantas N, Issarny V (2006) Efficient semantic service discovery in pervasive computing environments. In: Steen V, Henning M (eds), 7th Int. Middleware Conference (Middleware’06), Lecture Notes Computer Science, Springer Berlin Heidelberg, vol 4290, pp 240–259

  • Mokhtar SB, Preuveneers D, Georgantas N, Issarny V, Berbers Y (2008) EASY: Efficient semAntic Service discoverY in pervasive computing environments with QoS and context support. J Syst Softw 81(5):785–808

    Article  Google Scholar 

  • Najar S, Kirsch-Pinheiro M, Souveyet C (2012a) Enriched semantic service description for service discovery: bringing context to intentional services. Int J Adv Intell Syst 5(1&2):159–174

    Google Scholar 

  • Najar S, Kirsch Pinheiro M, Souveyet C, Steffenel A (2012b) Service discovery mechanism for an intentional pervasive information system. In: Goble CA, Chen PP, Zhang J (eds) IEEE 19th international conference on web services ICWS. Honolulu, United States, pp 520–527

    Google Scholar 

  • Najar S, Kirsch-Pinheiro M, Souveyet C (2014) A user-centric vision of service-oriented Pervasive Information Systems. In: Bajec M, Collard M, Deneckère R (eds), 8th international conference on research challenges in information science, IEEE, pp 359–370

  • Nazerfard E, Cook DJ (2015) CRAFFT: an activity prediction model based on Bayesian networks. J Ambient Intell Humaniz Comput Springer 6(2):193–205

    Article  Google Scholar 

  • Olsson T, Bjurling B, Chong M, Ohlman B (2011) Goal refinement for automated service discovery. 3rd international conferences on advanced service computing, pp 46–51. http://www.thinkmind.org/index.php?view=article&articleid=service_computation_2011_3_10_1013. Accessed 3 Nov 2014

  • Paolucci M, Kawamura T, Payne T, Sycara K (2002). Semantic matching of web services capabilities. In: Horrocks I, Hendler J (eds) The Semantic Web (ISWC 2002—Lecture Notes in Computer Science, Springer Berlin Heidelberg, vol 2342, pp 333–347

  • Paridel K, Mantadelis T, Yasar A, Preuveneers D, Janssens G, Vanrompay Y, Berbers Y (2014) Analyzing the efficiency of context-based grouping on collaboration in VANETs with large-scale simulation. J Ambient Intell Humaniz Comput Springer 5(4):475–490

    Article  Google Scholar 

  • Ramakrishnan A, Preuveneers D, Berbers Y (2013). A modular and distributed bayesian framework for activity recognition in dynamic smart environments. In: Augusto JC, Wichert R, Collier R, Keyson D, Salah AA, Tan AH (eds), Ambient Intelligence, 4th International Joint Conference, AmI 2013, Lecture Notes in Computer Science, Springer Berlin Heidelberg, vol 8309, pp 293–298

  • Ramakrishnan A, Preuveneers D, Berbers Y (2014) Enabling self-learning in dynamic and open IoT environments. In: Shakshuki E, Yasar A (eds) The International Conference on Ambient Systems, Networks and Technologies (ANT-2014), the 4th International Conference on Sustainable Energy Information Technology (SEIT-2014), Hasselt, Belgium, June 2–5, 2014, Procedia Computer Science, Elsevier, vol 32, 207–214

  • Sigg S, Haseloff S, David K (2010) An alignment approach for context prediction tasks in UbiComp environments. IEEE Pervasive Comput 9(4):90–97

    Article  Google Scholar 

  • Santos LOB da Silva, da Silva EG, Pires LF, van Sinderen M (2009). Towards a goal-based service framework for dynamic service discovery and composition. In: 3rd. International Conference on Information Technology: New Generations, IEEE Computer Society, pp 302–307. doi:10.1109/ITNG.2009.27

  • Sundmaeker H, Guillemin P, Friess P, Woelfflé S (2010) Vision and Challenges for realising the internet of things. Cluster of European Research projects on the Internet of Things (CERP-IoT). doi:10.2759/26127. http://www.theinternetofthings.eu/sites/default/files/Rob%20van%20Kranenburg/Clusterbook%202009_0.pdf. Accessed 3 Nov 2014

  • Suraci V, Mignanti S, Aiuto A (2007) Context-aware semantic service discovery. 16th IST Mobile and Wireless Communications Summit, pp 1–5

  • 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 

  • Vanrompay Y, Berbers Y (2012) A methodological approach to quality of future context for proactive smart systems. In: Andreev S, Balandin S, Koucheryavy Y (eds) Internet of things, smart spaces and next generation networking, Lecture Notes Computer Science, Springer Berlin Heidelberg, vol 7469, pp 152–163

  • Vanrompay Y, Kirsch-Pinheiro M, Berbers Y (2011) Service selection with uncertain context information. In: Reiff-Marganiec S, Tilly M (eds) Handbook of research on service-oriented systems and non-functional properties: future directions. IGI Global, Hershey, PA, pp 192–215. doi:10.4018/978-1-61350-432-1.ch009

    Google Scholar 

  • Xiao H, Zou Y, Ng J, Nigul L (2010). An approach for context-aware service discovery and recommendation. In: IEEE International conference on web services (ICWS 2010), pp 163–170. doi:10.1109/ICWS.2010.95

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  1. Centre de Recherche en Informatique/Université Paris 1 Panthéon Sorbonne, 90 Rue Tolbiac, 75013, Paris, France

    Salma Najar, Manuele Kirsch Pinheiro & Carine Souveyet

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  1. Salma Najar
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  2. Manuele Kirsch Pinheiro
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  3. Carine Souveyet
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Correspondence to Manuele Kirsch Pinheiro.

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Najar, S., Pinheiro, M.K. & Souveyet, C. Service discovery and prediction on Pervasive Information System. J Ambient Intell Human Comput 6, 407–423 (2015). https://doi.org/10.1007/s12652-015-0288-5

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