Airport Context Analytics

  • Eli Katsiri
  • George Papastefanatos
  • Manolis Terrovitis
  • Timos Sellis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8741)


Airports today can constitute a perfect environment for developing novel digital marketplaces offering location-specific and semantically rich context-aware services, such as personalized marketing campaigns, last minute, discounted airline tickets while helping users access the airport and speed through the airport process.

Underpinning the above vision is the ability to target service content to users’ current context, e.g., their location, intent, environment, in real time. The contribution of this work is that it uses a pervasive computing system with three key ingredients: (a) a data model, comprising user and service content entities, (b) a user context model and (c) rules for simple pattern matching on service content and user context event streams. This modus operandi is encapsulated inside a SOA architecture, the Common Airport Portal - CAP and it is illustrated through the description of a real application, Offers and Coupons Services that was deployed recently at Athens International Airport (AIA) (


airport information systems context-awareness real-time analytics personalisation rule-based reasoning system implementation 


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  1. 1.
    Jagadish, H.V.: Big Data: It’s not just the analytics,
  2. 2.
    Weiser, M.: Ubiquitous Computing. IEE Computer, Hot Topics (1993)Google Scholar
  3. 3.
    Arsanjani, A.: Service-oriented Modeling and Architecture. How to identify, specify and realize services for your SOA,
  4. 4.
    Schilit, B., Adams, N., Want, R.: Context-aware computing applications. In: First International Workshop on Mobile Computing Systems and Applications, pp. 85–90 (1994)Google Scholar
  5. 5.
    Schilit, B.: Disseminating active map information to mobile hosts. IEEE Network 8(5), 22–23 (1994)CrossRefGoogle Scholar
  6. 6.
    Dey, K., Abowd, G.D., Wood, A.: CyberDesk: A framework for providing Self-Integrating context-aware services. Knowledge-base Systems 11, 3–13 (1999)CrossRefGoogle Scholar
  7. 7.
    Brown, P.J.: The Stick-e Document: A framework for creating context-aware applications. Electronic Publishing 96, 259–272 (1996)Google Scholar
  8. 8.
    Abowd, G.D., Dey, A.K.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  9. 9.
    Mahout: Scalable machine learning and Data Mining,
  10. 10.
    Welcome to Apache Hadoop,
  11. 11.
    PAWS: Processes with adaptive web services,
  12. 12.
    Truong, H.L., Dustdar, H.: A survey on context-aware Web Service Systems. International Journal of Web Information Systems of Context and Context-Awareness 5(1), 5–31 (2009)CrossRefGoogle Scholar
  13. 13.
    Drools: The business logic integration platform,
  14. 14.
    Sanchez-Pi, N., Carbo, J., Molina, J.M.: Building a Knowledge Base System for an Airport Case of Use. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds.) DCAI 2008. AISC, vol. 50, pp. 739–747. Springer, Heidelberg (2009)Google Scholar
  15. 15.
    Capra, L., Emmerich, W., Mascolo, C.: Carisma: Context-aware reflective middleware system for mobile applications. IEEE Transactions on Software Engineering 29, 929–944 (2003)CrossRefGoogle Scholar
  16. 16.
    Chakraborty, D., Lei, H.: Pervasive enablement of business processes. In: Second IEEE Annual Conference on Pervasive Computing and Communications (PERCOM 2004), Orlando, Florida, USA (2004)Google Scholar
  17. 17.
    Team, T., Mais, M.: Multichannel adaptive information systems. In: International Conference on Web Information Systems Engineering, Rome, Italy (2003)Google Scholar
  18. 18.
    Capiello, C., Comuzzi, M., Mussi, E., Pernici, B.: Context Management for Adaptive Information SystemsGoogle Scholar
  19. 19.
    Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W., Wielinga, B.: Knowledge Engineering and Management, The CommonKADS Methodology. MIT Press, Cambridge (1999)Google Scholar
  20. 20.
    Forgy, C.: Rete: a fast algorithm for the many pattern/many object pattern match problem. In: Expert Systems, pp. 324–341. IEEE Computer Society Press (1990)Google Scholar
  21. 21.
    Katsiri, E.: Knowledge-base representation and reasoning for the autonomic management of pervasive healthcare. In: Eighth Join Conference on Knowledge-Based Engineering (JCKBSE 2008), August 25-28. University of Piraeus, Greece (2008)Google Scholar
  22. 22.
    Katsiri, E., Bacon, J., Mycroft, A.: Linking sensor data to context-aware applications using abstract events. International Journal of Pervasive Computing and Communications 3(4), 347–377 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eli Katsiri
    • 1
    • 2
  • George Papastefanatos
    • 2
  • Manolis Terrovitis
    • 2
  • Timos Sellis
    • 3
  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece
  2. 2.Institute for the Management of Information Systems, Research and Innovation Centre in Information, Communication and Knowledge Technologies - “Athena”AthensGreece
  3. 3.School of Computer Science and Information TechnologyRMIT UniversityAustralia

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