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Context-Awareness in Location Based Services in the Big Data Era

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Abstract

Integrating contextual information into the process of location-based service delivering is an emerging trend towards more advanced techniques aiming at personalization and intelligence of location-based services in the big data era. This chapter provides a systematic review of current context-aware location-based service systems using big data by analysing the methodological and practical choices that their developers made during the main phases of the context awareness process (i.e. context acquisition, context representation, and context reasoning and adaptation). Specifically, the chapter analyses ten location-based services, developed over the five years 2010–2014, by focusing on (1) context categories, data sources and level of automation of the context acquisition, (2) context models applied for context representation, and (3) adaptation strategies and reasoning methodologies used for context reasoning and adaptation. For each of these steps, a set of research questions and evaluation criteria are extracted that we use to evaluate and compare the surveyed context-aware location-based services. The results of this comparison are used to outline challenges and opportunities for future research in this research field.

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References

  1. Xi, W., Han, J., Li, K., Jiang, Z., Ding, H.: Location inferring in internet of things and big data. In: Buyya, R., Calheiros, R.N., Dastjerdi, A.V. (eds.) Big Data: Principles and Paradigms, pp. 309–335. Morgan Kaufmann (2016)

    Google Scholar 

  2. Koeppel, I.: What are Location Services? From a GIS Perspective. ESRI white paper (2000)

    Google Scholar 

  3. Shiode, N., Li, C., Batty, M., Longley, P., Maguire, D.: The impact and penetration of location-based services. In: Karimi, H.A., Hammad, A. (eds.), Telegeoinformatics: Location-Based Computing and Services, pp. 349–366. CRC Press (2004)

    Google Scholar 

  4. Spiekermann, S.: General aspects of location-based services. In: Schiller, J., Voisard, A. (eds). Location-Based Services. Morgan Kaufman (2004)

    Google Scholar 

  5. Shek, S.: Next Generation Location Based Services for Mobile Devices, pp. 1–66. Computer Science Corporation, Leading Edge Forum (2010)

    Google Scholar 

  6. Hadjioannou, V., Mavromoustakis, C.X., Papanikolaou, K., Mastorakis, G., Goleva, R., Dobre, C., Batalla, J.M.: On the comparison of location based software solutions used for tracking purposes in ambient assisted living applications. In: 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 5–11. IEEE, Sept 2016

    Google Scholar 

  7. Skourletopoulos, G., Mavromoustakis, C.X., Mastorakis, G., Batalla, J.M., Sahalos, J.N.: An evaluation of cloud-based mobile services with limited capacity: a linear approach. Soft Comput. 1–8 (2016)

    Google Scholar 

  8. Bourdena, A., Mavromoustakis, C.X., Mastorakis, G., Rodrigues, J.J.P.C., Dobre, C.: Using socio-spatial context in mobile cloud process offloading for energy conservation in wireless devices. IEEE Trans. Cloud Comput. 1 (2015)

    Google Scholar 

  9. Virrantaus, K., Markkula, J., Garmash, A., Terziyan, Y.V.: Developing GIS-supported location-based services, 423–432. In: Proceedings of WGIS’2001–First International Workshop on Web Geographical Information Systems, Kyoto, Japan (2001)

    Google Scholar 

  10. Schiller, J., Voisard, A.: Location Based Services. Morgan Kaufmann, San Francisco, CA (2004)

    Google Scholar 

  11. Reichenbacher, T.: Mobile Cartography: Adaptive Visualisation of Geographic Information on Mobile Devices (PhD thesis) (2004). https://www.tumb1.biblio.tu-muenchen.de/pub1/diss/bv/2004/reichenbacher.pdf

  12. Themistocleous, M., Azab, N.A., Kamal, M.M., Ali, M., Morabito, V.: Location-based services for public policy making: The direct and indirect way to e-participation. Inf. Syst. Manag. 29(4), 269–283 (2012)

    Article  Google Scholar 

  13. Malik, N., Mahmud, U., Javed, Y.: Future challenges in context-aware computing. In: proceedings of the IADIS International Conference WWW/Internet, pp. 306–310 (2007)

    Google Scholar 

  14. Khattak, A.M., Akbar, N., Aazam, M., Ali, T., Khan, A.M., Jeon, S., Lee, S.: Context representation and fusion: advancements and opportunities. Sensors 14(6), 9628–9668 (2014)

    Article  Google Scholar 

  15. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. Commun. Surv. Tutor. IEEE 16(1), 414–454 (2014)

    Article  Google Scholar 

  16. Schilit, B., Adams, N., Want, R.: Context-aware computing applications. In: Proceedings of the First Workshop Mobile Computing Systems and Applications (WMCSA ’94), pp. 85–90 (1994)

    Google Scholar 

  17. Chen, G., Kotz, D.: A survey of context-aware mobile computing research. Technical Report TR2000–381, Department of Computer Science, Dartmouth College (2000)

    Google Scholar 

  18. Zimmermann, A., Lorenz, A., Oppermann, R.: An operational definition of context. In: Proceedings of the 6th International and Interdisciplinary Conference on Modeling and using Context (CONTEXT07), pp. 558–571. Springer Press (2007)

    Google Scholar 

  19. Vieira, V., Tedesco, P., Salgado, A.C.: Towards an Ontology for Context Representation in Groupware. Proceedings of the International Workshop on Groupware (CRIWG’05); Porto de Galinhas, Brazil, pp. 367–375, 25–29 Sept 2005

    Google Scholar 

  20. Nieto, I., Bota, J.A., Gómez-Skarmeta, A.F.: Information and hybrid architecture model of the OCP contextual information management system. J. Univ. Comput. Sci. 12, 357–366 (2006)

    Google Scholar 

  21. Hervás, R., Bravo, J., Fontecha, J.: A context model based on ontological languages: a proposal for information visualization. J. Univ. Comput. Sci. 16, 1539–1555 (2010)

    Google Scholar 

  22. Kϋpper, A.: Location Based Service, Fundamental and Operation. England, Chichester (2005)

    Book  Google Scholar 

  23. Tu, Y.: xTolk-A Context-Aware Mobile Application on the Nokia N95 8 GB Smartphone (Doctoral dissertation, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark) (2008)

    Google Scholar 

  24. Bellavista, P., Corradi, A., Fanelli, M., Foschini, L.: A survey of context data distribution for mobile ubiquitous systems. ACM Comput. Surv. (CSUR) 44(4), 24 (2012)

    Article  Google Scholar 

  25. Zhang, D., Yu, Z., Guo, B., Wang, Z.: Exploiting personal and community context in mobile social networks. In: Mobile Social Networking, pp. 109–138. Springer, New York (2014)

    Google Scholar 

  26. Kaplan, A.M., Haenlein, M.: Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53(1), 59–68 (2010)

    Article  Google Scholar 

  27. Dasgupta, R., Chattopadhyay, D., Pal, A., Chakravarty, T. (2014). A comprehensive seven layer sensor model: cyber-physical system. In: Sensing Technology: Current Status and Future Trends I, pp. 57–81. Springer International Publishing

    Google Scholar 

  28. Schwinger, W., Grün, C., Pröll, B., Retschitzegger, W., Schauerhuber, A.: Context-awareness in mobile tourism guides–a comprehensive survey. Rapport Technique. Johannes Kepler University Linz (2005)

    Google Scholar 

  29. Guo, C., Liu, J., Fang, Y., Wan, Y., Cui, J.: iWISE: A location-based service cloud computing system with content aggregation and social awareness. In: Principle and Application Progress in Location-Based Services, pp. 139–157. Springer International Publishing (2014)

    Google Scholar 

  30. Shankar, P., Huang, Y.W., Castro, P., Nath, B., Iftode, L.: Crowds replace experts: Building better location-based services using mobile social network interactions. In: 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 20–29. IEEE (2012)

    Google Scholar 

  31. Foursquare. https://www.it.foursquare.com/

  32. Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G. : Social tagging for personalized location-based services. In: Proceedings of the 2nd International Workshop on Social Recommender Systems (2011)

    Google Scholar 

  33. Yus, R., Mena, E., Ilarri, S., Illarramendi, A.: SHERLOCK: semantic management of location-based services in wireless environments. Pervasive Mob. Comput. 15, 87–99 (2014)

    Article  Google Scholar 

  34. Balduini, M., Celino, I., Dell’Aglio, D., Della Valle, E., Huang, Y., Lee, T., Tresp, V.: BOTTARI: An augmented reality mobile application to deliver personalized and location-based recommendations by continuous analysis of social media streams. Web Semant. Sci. Serv. Agents World Wide Web 16, 33–41 (2012)

    Article  Google Scholar 

  35. Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. Artif. Intell. 184, 17–37 (2012)

    Article  MathSciNet  Google Scholar 

  36. D’Ulizia, A., Ferri, F., Grifoni, P.: A similarity assessment method for discovering and adapting business services. Int. J. Comput. Sci. Eng. 5(2), 97–109 (2010)

    Article  Google Scholar 

  37. Viktoratos, I., Tsadiras, A., Bassiliades, N.: Providing a context-aware location based web service through semantics and user-defined rules. In: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics, p. 9. ACM (2014)

    Google Scholar 

  38. Rizia, R., Tanviruzzaman, M., Ahamed, S.I.: KnockAround: location based service via social knowledge. In: 2012 IEEE 36th Annual Computer Software and Applications Conference (COMPSAC), pp. 623–631. IEEE (2012)

    Google Scholar 

  39. Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th International Conference on Advances in GIS, pp. 199–208. ACM (2012)

    Google Scholar 

  40. Strang, T., Linnhoff-Popien, C.: A context modeling survey. In Workshop Proceedings, Sept 2004

    Google Scholar 

  41. Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161–180 (2010)

    Article  Google Scholar 

  42. Pop, F., Cristea, V.: The art of scheduling for big data science. In: Li, K.-C., Jiang, H., Yang, L.T., Cuzzocrea, A. (eds.) Big Data: Algorithms, Analytics, and Applications, pp. 105–120. Chapman & Hall/CRC Big Data Series (2015). ISBN 978-1482240559

    Google Scholar 

  43. Bray, T., Paoli, J., Sperberg-McQueen, C.M.: Extensible Markup Language (XML). Present World Wide Web J. 27–66 (1997)

    Google Scholar 

  44. Schmohl, R., Baumgarten, U.: The contextual map-a context model for detecting affinity between contexts. In: Mobile Wireless Middleware, Operating Systems, and Applications, pp. 171–184. Springer, Berlin (2009)

    Google Scholar 

  45. Ye, J., Coyle, L., Dobson, S., Nixon, P.: Ontology-based models in pervasive computing systems. Knowl. Eng. Rev. 22(04), 315–347 (2007)

    Article  Google Scholar 

  46. Brickley, D., Guha, R.: Resource Description Framework (RDF) Schema Specification (2000). https://www.w3.org/TR/RDF-schema

  47. OWL Web Ontology Language. https://www.w3.org/TR/owl-ref/

  48. Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U., Franklin, M., Jagadish, H.V.: Challenges and Opportunities with Big Data. A Community White Paper Developed by Leading Researchers Across the United States. Computing Research Association, Washington (2012)

    Google Scholar 

  49. Berrueta, D., et al.: SIOC core ontology specification. W3C Member Submission (2007)

    Google Scholar 

  50. http://www.w3.org/2003/01/geo/

  51. Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: an architecture for storing and querying RDF data and schema information. In: Lieberman, H., Fensel, D., Hendler, J., Wahlster, W. (eds.) Semantics for the WWW. MIT Press (2001)

    Google Scholar 

  52. Klein, C., Schmid, R., Leuxner, C., Sitou, W., Spanfelner, B.: A survey of context adaptation in autonomic computing. In: Fourth International Conference on Autonomic and Autonomous Systems, 2008. ICAS 2008, pp. 106–111. IEEE (2008)

    Google Scholar 

  53. Benazzouz, Y.: Context discovery for autonomic service adaptation in intelligent space. Adv. Next Gener. Serv. Serv. Archit. 14, 281 (2011)

    Google Scholar 

  54. Mokbel, M., Bao, J., Eldawy, A., Levandoski, J., Sarwat, M.: Personalization, socialization, and recommendations in location-based services 2.0. In: PersDB 2011 Workshop, 2 Sept 2011, Seattle, Washington, USA (2011)

    Google Scholar 

  55. Zimmermann, A., Specht, M., Lorenz, A.: Personalization and context management. User Model User Adapt. 15(3–4), 275–302 (2005)

    Article  Google Scholar 

  56. Chaudhuri, S., Gravano, L.: Evaluating Top-K selection queries. In: VLDB (1999)

    Google Scholar 

  57. Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE (2001)

    Google Scholar 

  58. Chan, C.-Y., Jagadish, H., Tan, K.-L., Tung, A.K., Zhang, Z.: Finding k-Dominant skylines in high dimensional space. In: SIGMOD (2006)

    Google Scholar 

  59. Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD (1995)

    Google Scholar 

  60. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)

    Article  Google Scholar 

  61. Guessoum, D., Miraoui, M., Tadj, C.: Survey of semantic similarity measures in pervasive computing. Int. J. Smart Sens. Intell. Syst. 8(1), 125–158 (2015)

    Google Scholar 

  62. Chen, A.: Context-Aware collaborative filtering system: predicting the user’s preference in the ubiquitous computing environment. Location- and Context-Awareness, pp. 244–253. Springer, Berlin (2005)

    Chapter  Google Scholar 

  63. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  64. Liu, L., Lecue, F., Mehandjiev, N., Xu, L.: Using context similarity for service recommendation. In: 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), pp. 277–284. IEEE (2010)

    Google Scholar 

  65. García-Crespo, A., Chamizo, J., Rivera, I., Mencke, M., Colomo-Palacios, R., Gómez-Berbís, J.M.: SPETA: social pervasive e-tourism advisor. Telemat. Inf. 26(3), 306–315 (2009)

    Article  Google Scholar 

  66. Grifoni, P., D’Ulizia, A., Ferri, F.: A semantic-based approach for context-aware service discovery. Int. J. Inf. Syst. Serv. Sect. (IJISSS) 6(4), 1–26 (2014)

    Article  Google Scholar 

  67. Anand, S.S., Mobasher, B.: Intelligent Techniques for Web Personalization, pp. 1–36. Springer, Berlin (2005)

    Book  Google Scholar 

  68. Wang, X.H., Zhang, D.Q., Gu, T., Pung, H.K.: Ontology based context modeling and reasoning using OWL. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004, pp. 18–22. IEEE (2004)

    Google Scholar 

  69. Baader, F., Horrocks, I., Sattler, U.: Description logics as ontology languages for the semantic web. In: Mechanizing Mathematical Reasoning, in: Lecture Notes in Computer Science, vol. 2605, pp. 228–248. Springer (2005)

    Google Scholar 

  70. Boley, H., Paschke, A., Shafiq, O.: RuleML 1.0: the overarching specification of web rules. Lect. Notes Comput. Sci. 6403(4), 162–178 (2010)

    Article  Google Scholar 

  71. Friedman-Hill, E.: Jess, the Rule Engine for the Java Platform (2008)

    Google Scholar 

  72. Schou, S.: Context-based service adaptation platform: improving the user experience towards mobile location services. In: International Conference on Information Networking, 2008. ICOIN 2008, pp. 1–5. IEEE, Jan 2008

    Google Scholar 

  73. Reichenbacher, T.: Mobile Cartography—Adaptive Visualisation of Geographic Information on Mobile Devices. Verlag Dr. Hut, München (2004)

    Google Scholar 

  74. Stephanidis, C., Paramythis, A., Akoumianakis, D., Sfyrakis, M.: Self-adapting web-based systems: towards universal accessibility. In: 4th Workshop on User Interface For All, Stockholm, Sweden (1998)

    Google Scholar 

  75. Cheptsov, A., et al.: Large knowledge collider. A service-oriented platform for large-scale semantic reasoning. In: Proceedings of WIMS 2011 (2011)

    Google Scholar 

  76. D’Ulizia, A., Ferri F., Formica, A., Grifoni, P.: Approximating geographical queries. J. Comput. Sci. Technol. 24(6), 1109–1124, Nov 2009

    Google Scholar 

  77. D’Ulizia, A., Ferri, F., Grifoni, P., Rafanelli, M.: Relaxing constraints on GeoPQL operators for improving query answering. In: 17th International Conference on Database and Expert Systems Applications (DEXA’06), Lecture Notes in Computer Science 4080, pp 728–737. Springer, (2006)

    Google Scholar 

  78. Shin, K.G., Ju, X., Chen, Z., Hu, X.: Privacy protection for users of location-based services. Wirel. Commun. IEEE 19(1), 30–39 (2012)

    Article  Google Scholar 

  79. Pan, J., Zuo, Z., Xu, Z., Jin, Q.: Privacy protection for LBS in mobile environments: progresses, issues and challenges. Int. J. Secur. Its Appl. 9(1), 249–258 (2015)

    Google Scholar 

  80. Zelenik, D., Bielikova, M.: Reducing the sparsity of contextual information for recommender systems. In: Proceedings of the sixth ACM conference on Recommender systems, pp. 341–344. ACM (2012)

    Google Scholar 

  81. Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 9–16. ACM, Oct 2007

    Google Scholar 

  82. Grifoni, P., Ferri, F., Caschera, M.C., D’Ulizia, A., Mazzei, M.: MIS: Multimodal interaction services in a cloud perspective. J. Next Gener. Inf. Technol. 5(4), 1 (2014)

    Google Scholar 

  83. D’Ulizia, A.: Exploring multimodal input fusion strategies. The Handbook of Research on Multimodal Human Computer Interaction and Pervasive Services: Evolutionary Techniques for Improving Accessibility, pp. 34–57 (2009)

    Google Scholar 

  84. D’Andrea, A., D’Ulizia, A., Ferri, F., Grifoni, P.: EMAG: An Extended Multimodal Attribute Grammar for Behavioural Features, Digital Scholarship in the Humanities, vol. 32(2), pp. 251–275. Oxford University Press (2017)

    Google Scholar 

  85. D’Ulizia, A., Ferri, F., Grifoni, P.: Moving GeoPQL: a Pictorial Language towards Spatio-Temporal Queries. GeoInformatica 16(2), 357–389 (2012)

    Article  Google Scholar 

  86. Feng, J., Liu, Y.: Intelligent context-aware and adaptive interface for mobile LBS. Comput. Intell.Neurosci. (2015)

    Google Scholar 

  87. Grifoni, P., D’Ulizia, A., Ferri, F.: Computational methods and grammars in language evolution: a survey. Artif. Intell. Rev. 45(3), 369–403 (2016)

    Article  Google Scholar 

  88. Braunhofer, M.: Hybrid solution of the cold-start problem in context-aware recommender systems. In: User Modeling, Adaptation, and Personalization, pp. 484–489. Springer International Publishing (2014)

    Google Scholar 

  89. Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014)

    Article  Google Scholar 

  90. Huang, L.W., Chen, G.S., Liu, Y.C., Li, D.Y.: Enhancing recommender systems by incorporating social information. J. Zhejiang Univ. Sci. C 14(9), 711–721 (2013)

    Article  Google Scholar 

  91. Than, C., Han, S.: Improving recommender systems by incorporating similarity, trust and reputation. J. Internet Serv. Inf. Secur. (JISIS) 4(1), 64–76 (2014)

    Google Scholar 

  92. Ferri, F., Grifoni, P., Caschera, M.C., D’Ulizia, A., Praticò, C.: KRC: KnowInG crowdsourcing platform supporting creativity and innovation. Adv. Inf. Sci. Serv. Sci. 5(16), 1–15 (2013)

    Google Scholar 

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Grifoni, P., D’Ulizia, A., Ferri, F. (2018). Context-Awareness in Location Based Services in the Big Data Era. In: Skourletopoulos, G., Mastorakis, G., Mavromoustakis, C., Dobre, C., Pallis, E. (eds) Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-67925-9_5

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