Abstract
Context prediction is used to proactively adapt e.g., services to users’ needs. Due to the fact that context prediction enables proactiveness it has a high significance for UC systems. To the best of our knowledge, research literature on context prediction only focuses on the history of the user whose next context has to be predicted. Does a user suddenly change her behaviour in an unexpected way, the context history of the user does not contain appropriate context information to provide reliable context predictions. Hence, context prediction algorithms will fail to predict a user’s next context if they solely rely on the context history of the user, whose context has to be predicted. To overcome the gap of missing context information in the user’s context history, the Collaborative Context Prediction (CCP) approach is proposed. CCP takes advantage of existing direct and indirect relations which may exist among the context histories of various users. Thereby, CCP bases on the Higher-order Singular Value Decomposition, which is also applied in the field of recommendation systems. To provide an evaluation of CCP it is compared to state-of-the-art context prediction approaches with respect to its prediction accuracy using a collaborative data set. For the reason that context prediction approaches primarily use personal context data legal criteria are presented. These criteria are used to legally assess the context prediction approaches. Subsequently, the resulting consequences are discussed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing, HUC ’99, pp. 304–307. Springer, London (1999). URL http://dl.acm.org/citation.cfm?id=647985.743843
Andrich, R., Gower, V., Caracciolo, A., Zanna, G.D., Rienzo, M.D.: The dat project: A smart home environment for people with disabilities. In: Miesenberger, K., Klaus, J., Zagler, W.L., Karshmer, A.I. (eds.) ICCHP, Lecture Notes in Computer Science, vol. 4061. Springer, New York (2006)
Atzmüller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.E., Mitzlaff, Folke, C.S., Stumme, G.: Enhancing social interactions at conferences. Inform. Tech. 3(53), 101–107 (2011)
Cook, D., Youngblood, M., Heierman E.O., I., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: MavHome: an agent-based smart home. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003), pp. 521–524 (2003)
Danninger, M., Stiefelhagen, R.: A context-aware virtual secretary in a smart office environment. In: MM ’08: Proceeding of the 16th ACM International Conference on Multimedia, pp. 529–538. ACM, New York (2008). DOI http://doi.acm.org/10.1145/1459359.1459430
David, K., Flach, A.: An innovative car-2-x system concept for pedestrian safety. IEEE VTC J. 70–76 (2010)
Fang, H., Ruan, J.: An improved position prediction algorithm based on active LeZi in smart home. In: Computer Science & Service System (CSSS), pp. 1733–1736. IEEE (2012). DOI 10.1109/CSSS.2012.433. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6394752
Gopalratnam, K., Cook, D.J.: Online sequential prediction via incremental parsing: The active LeZi algorithm. IEEE Intell. Syst. 52–58 (2007). URL http://doi.ieeecomputersociety.org/10.1109/10.1109/MIS.2007.15
Gopalratnam, K., J., D.: Active lezi: An incremental parsing algorithm for sequential prediction. In: In Sixteenth International Florida Artificial Intelligence Research Society Conference, pp. 38–42 (2003)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009). DOI 10.1145/1656274.1656278. URL http://doi.acm.org/10.1145/1656274.1656278
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009). DOI 10.1137/07070111X. URL http://dx.doi.org/10.1137/07070111X
Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000). DOI 10.1137/S0895479896305696. URL http://dx.doi.org/10.1137/S0895479896305696
Lau, S.L., König, I., David, K., Parandian, B., Carius-Düssel, C., Schultz, M.: Supporting patient monitoring using activity recognition with a smartphone. In: The Seventh International Symposium on Wireless Communication Systems (ISWCS’10). York, UK (2010)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007). DOI 10.1007/s10618-007-0064-z. URL http://dx.doi.org/10.1007/s10618-007-0064-z
Mayrhofer, R.: Context prediction based on context histories: Expected benefits, issues and current state-of-the-art. Cognit. Sci. Res. Paper University Of Sussex CSRP 577, 31 (2005)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid prototyping for complex data mining tasks. In: Ungar, L., Craven, M., Gunopulos, D., Eliassi-Rad, T. (eds.) KDD ’06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935–940. ACM, New York (2006). DOI http://doi.acm.org/10.1145/1150402.1150531. URL http://rapid-i.com/component/option,com_docman/task,doc_download/gid,25/Itemid,62/
Nurmi, P., Martin, M., Flanagan, J.A.: Enabling proactiveness through context prediction. In: Proceedings of the Workshop on Context Awareness for Proactive Systems, Helsinki, vol. 53 (2005). URL http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.5529&rep=rep1&type=pdf
Paradiso, J.A., Gips, J., Laibowitz, M., Sadi, S., Merrill, D., Aylward, R., Maes, P., Pentland, A.: Identifying and facilitating social interaction with a wearable wireless sensor network. Pers. Ubiquitous Comput. 14(2), 137–152 (2010). DOI 10.1007/s00779-009-0239-2
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Global and local state context prediction. In: In Artificial Intelligence in Mobile Systems 2003 (AIMS 2003), Seattle, WA (2003)
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: The state predictor method for context prediction. In: In Adjunct Proceedings Fifth International Conference on Ubiquitous Computing, Seattle, WA (2003)
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Next location prediction within a smart office building. In: Proceedings of 1st International Workshop on Exploiting Context Histories in Smart Environments at the 3rd International Conference on Pervasive Computing (2005)
Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T.: Prediction of indoor movements using bayesian networks. In: Proceedings of Location- and Context-Awareness (LoCA 2005) (2005)
Satyanarayanan, M.: Pervasive computing: vision and challenges. IEEE Pers. Comm. 8, 10–17 (2001)
Sigg, S.: Development of a novel context prediction algorithm and analysis of context prediction schemes. Ph.D. thesis, University of Kassel (2008)
Sigg, S., Gordon, D., Zengen, G.v., Beigl, M., Haseloff, S., David, K.: Investigation of context prediction accuracy for different context abstraction levels. IEEE Trans. Mobile Comput. 11(6), 1047–1059 (2012). DOI 10.1109/TMC.2011.170. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6188347
Sigg, S., Haseloff, S., David, K.: Prediction of context time series. In: 5th Workshop on Applications of Wireless Communications, pp. 31–45 (2007). URL http://www2.it.lut.fi/comlab/WAWC/wawc07/wawc07_sigg.pdf
Sigg, S., Haseloff, S., David, K.: An alignment approach for context prediction tasks in UbiComp environments. IEEE Pervasive Comput. 9(4), 90–97 (2010). DOI 10.1109/MPRV.2010.23
Silc, J., Robic, B., Ungerer, T.: Processor Architecture - from Dataflow to Superscalar and Beyond. Springer, New York (1999)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 43–50 (2008). URL http://dl.acm.org/citation.cfm?id=1454017
Vintan, L., Gellert, A., Petzold, J., Ungerer, T.: Person movement prediction using neural networks. In: In First Workshop on Modeling and Retrieval of Context (2004)
Voigtmann, C., David, K.: A Survey To Location-Based Context Prediction. In: Proceedings of the First Workshop on recent advances in behavior prediction and pro-active pervasive computing (AwareCast) (2012). URL http://www.ibr.cs.tu-bs.de/dus/Awarecast/awarecast2012_submission_9.pdf
Voigtmann, C., Lau, S.L., David, K.: An approach to collaborative context prediction. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 438–443 (2011)
Voigtmann, C., Schütte, C., Wacker, A., David, K.: A new approach for distributed and collaborative context prediction. In: 10th IEEE Workshop on Context Modeling and Reasoning 2013 (CoMoRea 2013), pp. 20–24. IEEE, San Diego (2013)
Voigtmann, C., Skistims, H., David, K., Roßnagel, A.: Legal assessment of context prediction techniques. In: Vehicular Technology Conference (VTC Fall), pp. 1–5. IEEE, Quebec City (2012)
Voigtmann, C., Zirfas, J., Skistims, H., David, K., Roßnagel, A.: Prospects for context prediction despite the principle of informational self-determination. In: 4th Context-Awareness and Trust 2010 Workshop (CAT2010), 24 August 2010
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Voigtmann, C., David, K. (2014). Collaborative Context Prediction. In: David, K., et al. Socio-technical Design of Ubiquitous Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-05044-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-05044-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05043-0
Online ISBN: 978-3-319-05044-7
eBook Packages: Computer ScienceComputer Science (R0)