Context-aware Adaption of Software Entities using Rules

Lauma Jokste, Jãnis Grabis


Context-aware systems gain recognition in rapidly growing information systems market. Systems run time adaption based on contextual information have been considered as a powerful mean towards better systems performance which help to reach overall organizational goals and to improve key performance indicators. This paper describes the concept where information systems can be divided into many software entities and each of them can be context dependent. Context situation dependent software entity execution routines are observed and these observations are used to formulate Context dependency rules either manually or by machine learning. Rule based adaptation allows to monitor adaptation process in a transparent way and allows to take into account human knowledge in adaptation process. The entity based adaption allows for a uniform approach inducing context-dependency to different part of the software.


  1. Alferez, G.H., Pelechano, V., Mazo, R. and Salinesi C., 2014. Dynamic Adaptation of Service Compositions with Variability Models. Journal of Systems and Software, 91, 24-47.
  2. Andersson, J., de Lemos, R., Malek, S., Weyns, D. 2009. Reflecting on self-adaptive software systems. In: Proc. of the ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, pp. 38-47.
  3. Ashbrook D., Starner T., 2003. Using GP to Learn Significant Locations and Predict Movement Across Multiple Users. Personal and Ubiquitous Computing, 7(5), 275-286.
  4. Calisir, F. and Calisir F., 2004. The relation of interface usability characteristics, perceived usefulness, and perceived ease of use to end-user satisfaction with enterprise resource planning (ERP) systems. Computers in Human Behavior, 20, 4, 505-515.
  5. Carvalho, J.E.S., Santoro, F.M. and Revoredo, K., 2015. A method to infer the need to update situations in business process adaptation. Computers in Industry, 71, 128-143.
  6. Dey, A.K., 2000. Providing architectural support for building context-aware applications. PhD thesis, Georgia Institute of Technology.
  7. Dey, A.K., 2001. Understanding and Using Context. In Personal Ubiquitous Computing, vol 5(1), pp. 4-7.
  8. Hallsteinsen, S., Geihs, K., Paspallis, N., Eliassen, F., Horn, G., Lorenzo, J., Mamelli, A., Papadopoulos, G.A., 2012. A development framework and methodology for self-adapting applications in ubiquitous computing environments, Journal of Systems and Software, 85, 12, 2840-2859.
  9. Jokste, L., 2015. Towards a Model of Context-aware Recommender Systems. In Proceedings of the CAiSE 2015 Forum at the 27th International Conference CAISE, vol 1367, pp 145-152.
  10. Ke C.K and Liu D.R., 2011. Context-based knowledge support for problem-solving by rule-inference and case-based reasoning. International Journal of Innovative Computing, Information and Control, 7.
  11. Kiefer, C., Bernstein, A. and Tappolet, J., 2007. Mining Software Repositories with iSPARQL and a Software Evolution Ontology. In Mining Software Repositories, ICSE Workshops MSR 7807.
  12. Kolski, C., Le Strugeon, E., Tendjaoui, M. 1993. Implementation of AI techniques for “intelligent” interface development. Engineering Applications of Artificial Intelligence, 6, 295-305
  13. Lavie, T. And Meyer, J., 2010. Benefits and costs of adaptive user interfaces, Int. J. of Human-Computer Studies, 68, 508-524.
  14. Liu, D.R., Ke, C.K. and Wu M.Y., 2008. Context-based Knowledge Support for Problem-solving by Ruleinference and Case-based Reasoning. In Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, pp 32015- 3210.
  15. Loukil, S., Kallel, S., Jmaiel, M., 2017. An approach based on runtime models for developing dynamically adaptive systems. Future Generation Computer Systems, 68, 365-375.
  16. Macias-Escriva F.D., Haber R., Toro, R. and Hernandez V., 2013. Self-adaptive systems: A survey of current approaches, research challenges and applications. In Expert Systems with Applications, 40(18), 7267-7279.
  17. Pernici, B., 2007. Adaptive Information Systems. Conceptual Modeling in Information Systems Engineering. Eds. J.Krogstie, A.L. Opdahl and S.Brinkkemper, pp.295-304.
  18. Pils, C., Roussaki, I. and Strimpakou, M., 2006. LocationBased Context Retrieval and Filtering, vol. 3987 of the series Lecture Notes in Computer Science, pp 256- 273.
  19. Rubens, N., Kaplan, D. and Sugiyama, M., 2011. Recommender Systems Handbook: Active Learning in Recommender Systems (eds. P.B. Kantor, F. Ricci, L. Rokach, B. Shapira). Springer, pp 735-767.
  20. Salehie, M., Tahvildari L., 2012. Towards a goal-driven approach to action selection in self-adaptive software. Software Practice & Experience, 42, 2, 211-233.
  21. Shakshuhi, E.M., Reid, M., Sheltami, T.R., 2015. An Adaptive User Interface in Healthcare, In: Procedia Computer Science, vol 56, pp 49-48.
  22. Singh, A. and Wesson, J., 2009. Improving the Usability of ERP Systems through the Application of Adaptive User Interfaces. In Proceedings of the 11th International Conference on Enterprise Information Systems (ICEIS), vol SAIC.
  23. Šupulniece, I. and Grabis, J., 2015. Conceptual Model of User Adaptive Enterprise Application. CSIMQ, 3, 84- 96
  24. Uchibayahi, T., Bernady, O.A. and Shiratori N., 2012. Towards an Adaptive Workflow with Multi-Agents in a Semantic Grid. In 12th International Conference on Computational Science and Its Applications, pp 20-25.
  25. Tan, P.N., Steinbach M. and Kumar V., 2006. Introduction to data mining: Association Analysis: Basic Concepts and Algorithms, pp 327-414.
  26. Voigtmann, C., Lun L.S. and Klaus D., 2011. A Collaborative Context Prediction Technique. In: Vehicular Technology Conference, IEEE, pp 1-5.
  27. Yan, H., Zheng, D. and Wang, J., 2012. Research of Quality Based Autonomic Context Processing for Pervasive Applications. In 7th International Conference on Computer Science & Education, pp 1234 - 1238.
  28. Yang W., Liao, Q., Zhang, C., 2013. An Association Rules Mining Algorithm on Context-Factors and Users' Preference. In International Conference on Intelligent Human-Machine Systems and Cybernetics, 190-195.

Paper Citation

in Harvard Style

Jokste L. and Grabis J. (2017). Context-aware Adaption of Software Entities using Rules . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-758-249-3, pages 166-171. DOI: 10.5220/0006366401660171

in Bibtex Style

author={Lauma Jokste and Jãnis Grabis},
title={Context-aware Adaption of Software Entities using Rules},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},

in EndNote Style

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - Context-aware Adaption of Software Entities using Rules
SN - 978-989-758-249-3
AU - Jokste L.
AU - Grabis J.
PY - 2017
SP - 166
EP - 171
DO - 10.5220/0006366401660171