Structured context prediction: A generic approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6115 LNCS, Issue , 2010, Pages 84-97

  Meiners, Matthias ^a   Zaplata, Sonja ^a   Lamersdorf, Winfried ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SOFTWARE REUSABILITY; FORECASTING; MIDDLEWARE;

APPLICATION DEVELOPERS; CONTEXT AWARE APPLICATIONS; CONTEXT PREDICTIONS; DOMAIN-SPECIFIC KNOWLEDGE; MIDDLEWARE PLATFORMS; MOBILE APPLICATIONS; PERVASIVE SYSTEMS; PREDICTION METHODS; SYSTEM COMPONENTS;

INTEROPERABILITY;

EID: 77953804676     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13645-0_7     Document Type: Conference Paper

References (19)

1. Satyanarayanan, M.: Pervasive Computing: Vision and Challenges. IEEE Personal Communications 8(4), 10-17 (2001)
2. Mayrhofer, R.: Context Prediction based on Context Histories: Expected Benefits, Issues and Current State-of-the-Art. In: Proc. of ECHISE 2005, pp. 31-36 (2005)
3. Salber, D., Dey, A.K., Abowd, G.D.: The Context Toolkit: Aiding the Development of Context-Enabled Applications. In: Proc. of CHI 1999, ACM, New York (1999)
4. Zaplata, S., Kunze, C.P., Lamersdorf, W.: Context-based Cooperation in Mobile Business Environments: Managing the Distributed Execution of Mobile Processes. BISE 2009(4) (2009)
5. Chen, G., Kotz, D.: A Survey of Context-Aware Mobile Computing Research. Technical Report TR2000-381, Dartmouth College (2000)
6. Jameson, A.,Wittig, F.: Leveraging Data About Users in General in the Learning of Individual User Models. In: Proc. of. 17th Int. Joint Conf. on Artificial Intelligence, pp. 1185-1192. Morgan Kaufmann, San Francisco (2001)
7. Sigg, S.: Development of a Novel Context Prediction Algorithm and Analysis of Context Prediction Schemes. PhD thesis, University of Kassel (2008)
8. Satyanarayanan, M.: Fundamental Challenges in Mobile Computing. In: Proceedings of PODC 1996, pp. 1-7. ACM, New York (1996)
9. Dey, A.K., Abowd, G.D.: Towards a Better Understanding of Context and Context- Awareness. Technical report, Georgia Institute of Technology (1999)
10. Symeonidis, A.L., Mitkas, P.A.: Agent Intelligence through Data Mining. Springer, Heidelberg (2005)
11. Mayrhofer, R.: An Architecture for Context Prediction. PhD thesis, Johannes Kepler University Linz (2004)
12. Petzold, J.: State Predictors for Context Prediction in Ubiquitous Systems. PhD thesis, University of Augsburg (2005) (in German)
13. Petzold, J., et al.: Hybrid Predictors for Next Location Prediction. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.J.-P. (eds.) UIC 2006. LNCS, vol.4159, pp. 125-134. Springer, Heidelberg (2006)
14. Hilario, M.: An Overview Of Strategies For Neurosymbolic Integration. In: Connectionist-Symbolic Integration, pp. 13-35. Lawrence Erlbaum Assoc., Mahwah (1995)
15. Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol.1857, pp. 1-15. Springer, Heidelberg (2000)
16. Borgelt, C., Kruse, R.: Graphical Models - Methods for Data Analysis and Mining. John Wiley & Sons, Chichester (2002)
17. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
18. Dagum, P., Galper, A., Horvitz, E.: Dynamic network models for forecasting. In: Proceedings of UAI 19, pp. 41-48. Morgan Kaufmann, San Francisco (1992)
19. Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2001