Intention mining

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In data mining, intention mining or intent mining is the problem of determining a user's intention from logs of their behavior in interaction with a computer system, such as a search engine. This notion is introduced for the first time in the paper of Dr. Ghazaleh Khodabandelou.[1]

Some authors model the intentions as an intentional process model in order to have a better understanding of the human way of thinking.[1]

Application

Intention Mining has already been used in several domains:

  • Software Engineering (Ghazaleh Khodabandelou[2] et al., 2013),[1](Ghazaleh Khodabandelou et al., 2014),[3] (Ghazaleh Khodabandelou et al., 2014),[4]
  • Web search : (Hashemi et al., 2008),[5] (Zheng et al., 2002),[6] (Strohmaier & Kröll, 2012),[7] (Kröll & Strohmaier, 2012),[8] (Park et al., 2010),[9] (Jethava et al., 2011),[10] (González-Caro & Baeza-Yates, 2011),[11] (Baeza-Yates et al., 2006) [12]
  • Business : Workarounds,[13] (Epure, 2013),[14] (Epure et al., 2014) [15]
  • Engineering : Entity Relationship modelling,[1] Method Engineering,[16] (Laflaquière et al., 2006),[17] (Clauzel et al., 2009),[18] Development traces [19]
  • Home video : (Mei et al., 2005) [20]

See also

References

  1. ^ a b c d Khodabandelou, G.; Hug, C.; Deneckere, R.; Salinesi, C. (2013). "Supervised intentional process models discovery using Hidden Markov models". IEEE Seventh International Conference on Research Challenges in Information Science (RCIS). doi:10.1109/RCIS.2013.6577711.
  2. ^ [1]
  3. ^ Khodabandelou, Ghazaleh, et al. "Unsupervised discovery of intentional process models from event logs." Proceedings of the 11th Working Conference on Mining Software Repositories. ACM, 2014.
  4. ^ Khodabandelou, Ghazaleh, Charlotte Hug, and Camille Salinesi. "A novel approach to process mining: Intentional process models discovery." Research Challenges in Information Science (RCIS), 2014 IEEE Eighth International Conference on. IEEE, 2014.
  5. ^ Hashemi, R.R., Bahrami, A., LaPlant, J. & Thurber, K. (2008). Discovery of Intent through the Analysis of Visited Sites. In Arabnia, H.A & Hashemi, R.R., (Eds.), Proceedings of the 2008 International Conference on Information & Knowledge Engineering (pp. 417-422). CSREA Press.
  6. ^ Zheng, C., Fan, L., Huan, L., Yin, L., Wei-Ying, M. & Liu, W. (2002, November). User Intention Modeling in Web Applications Using Data Mining. World Wide Web, 5 (3) 181-191.
  7. ^ Strohmaier, M. & Kröll, M. (2012). Acquiring knowledge about human goals from Search Query Logs. Information Processing & Management, 48 (1) 63-82.
  8. ^ Kröll, M. & Strohmaier, M. (2009). Analyzing Human Intentions in Natural Language Text. In Gil, Y., & Fridman Noy, N. (Eds.), Proceedings of the 5th International Conference on Knowledge Capture (pp. 197-198). New York, NY, USA: ACM.
  9. ^ Park, K., Lee, T., Jung, S., Lim, H. & Nam, S. (2010). Extracting Search Intentions from Web Search Logs. In 2nd International Conference on Information Technology Convergence and Services (pp. 1-6).
  10. ^ Jethava, V., Calderón-Benavides, L., Baeza-Yates, R., Bhattacharyya, C. & Dubhashi, D. (2011). Scalable Multi-Dimensional User Intent Identification using Tree Structured Distributions. In Ma, W.-Y., Nie, J.-Y., Baeza-Yates, R.A., Chua, T.-S. & Croft, W.B. (Eds.), Proceedings of the 34th International ACM Conference on Research and development in Information Retrieval (pp. 395-404). New York, NY, USA: ACM.
  11. ^ González-Caro, C. & Baeza-Yates, R. (2011). A multi-faceted approach to query intent classification. In Grossi, R., Sebastiani, F. & Silvestri F. (Eds.), Proceedings of the 18th International Conference on String Processing and Information Retrieval (pp. 368-379). Berlin, Heidelberg: Springer.
  12. ^ Baeza-Yates, R., Calderón-Benavides, R. & González-Caro, C. (2006). The intention behind web queries. In Crestani, F., Ferragina, P. & Sanderson, M. (Eds.), Proceedings of the 13th International Conference on String Processing and Information Retrieval (pp. 98-109). Berlin, Heidelberg: Springer.
  13. ^ Outmazgin, N. & Soffer, P. (2010). Business Process Workarounds: What Can and Cannot Be Detected by Process Mining. Lecture Notes in Business Information Processing, 147, 48-62.
  14. ^ Epure, E.V. (2013). Intention-mining: A solution to process participant support in process aware information systems (Master thesis). Utrecht University, The Netherlands.
  15. ^ What Shall I Do Next? Intention Mining for Flexible Process Enactment Elena V. Epure, Charlotte Hug, Rebecca Deneckere, Sjaak Brinkkemper, 26th International Conference on Advanced Information Systems Engineering (CAiSE), Thessaloniki : Greece (2014)
  16. ^ Intelligent Agile Method Framework, Jankovic M., Bajec M., Khodabandelou G., Deneckere R., Hug C., Salinesi C., 8th International Conference on Evaluation of Novel Approaches to Software Engineering 2013
  17. ^ Laflaquière, J., Lotfi, Settouti, S., Prié, Y. & Mille, A. (2006). Trace-Based framework for experience management and engineering. In Gabrys, B, Howlett, R.J. & Jain, L.C. (Eds.), Proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 1 (1) Berlin, Heidelberg: Springer, 1171-1178.
  18. ^ Clauzel, D., Sehaba, K., & Prié, Y. (2009). Modelling and Visualising Traces for Reflexivity in Synchronous Collaborative Systems. In Badr, Y., Caballé, S., Xhafa, F., Abraham, A., & Gros, B. (Eds.), Proceedings of the 1st International Conference on Intelligent Networking and Collaborative Systems (pp. 16-23). IEEE.
  19. ^ Supervised vs. Unsupervised Learning for Intentional Process Model Discovery Khodabandelou G., Hug C., Deneckere R., Salinesi C. Dans Proceedings of Business Process Modeling, Development, and Support (BPMDS) pp. 282-291, 2014
  20. ^ Mei, T., Hua, X.-S. & Zhou, H.-Q. (2005). Tracking users' capture intention: a novel complementary view for home video content analysis. In Proceedings of the 13th annual ACM International Conference on Multimedia (pp. 531-534). New York, NY, USA: ACM.
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