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. The notion of intention mining has been introduced for the first time in Ph.D. thesis of Dr. Ghazaleh Khodabandelou [1], [2]. This thesis presents a novel approach of process mining, called Map Miner Method (MMM). This method is designed to automate the construction of intentional process models from traces. MMM uses Hidden Markov Models to model the relationship between users' activities and the strategies (i.e., the different ways to fulfill the intentions). The method also includes some specific algorithms developed to infer users' intentions and construct intentional process model (Map), respectively. MMM models the intentions as an oriented graph (with different levels of granularity) in order to have a better understanding of the human way of thinking.[2]

Application[edit]

Intention Mining has already been used in several domains:

  • Software Engineering (Ghazaleh Khodabandelou[3] et al., 2013),[2](Ghazaleh Khodabandelou et al., 2014),[4] (Ghazaleh Khodabandelou et al., 2014),[5]
  • Web search : (Hashemi et al., 2008),[6] (Zheng et al., 2002),[7] (Strohmaier & Kröll, 2012),[8] (Kröll & Strohmaier, 2012),[9] (Park et al., 2010),[10] (Jethava et al., 2011),[11] (González-Caro & Baeza-Yates, 2011),[12] (Baeza-Yates et al., 2006) [13]
  • Business : Workarounds,[14] (Epure, 2013),[15] (Epure et al., 2014) [16]
  • Engineering : Entity Relationship modelling,[2] Method Engineering,[17] (Laflaquière et al., 2006),[18] (Clauzel et al., 2009),[19] Development traces [20]
  • Home video : (Mei et al., 2005) [21]

See also[edit]

References[edit]

  1. ^ http://khodabandelou.com/
  2. ^ 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. 
  3. ^ [1]
  4. ^ 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.
  5. ^ 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.
  6. ^ 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.
  7. ^ 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.
  8. ^ Strohmaier, M. & Kröll, M. (2012). Acquiring knowledge about human goals from Search Query Logs. Information Processing & Management, 48 (1) 63-82.
  9. ^ 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.
  10. ^ 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).
  11. ^ 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.
  12. ^ 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.
  13. ^ 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.
  14. ^ 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.
  15. ^ Epure, E.V. (2013). Intention-mining: A solution to process participant support in process aware information systems (Master thesis). Utrecht University, The Netherlands.
  16. ^ 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)
  17. ^ 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
  18. ^ 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.
  19. ^ 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.
  20. ^ 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
  21. ^ 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.