Journal of Intelligent Information Systems

, Volume 25, Issue 3, pp 251–273 | Cite as

An Interface Agent Approach to Personalize Users' Interaction with Databases

Article

Abstract

Making queries to a database system through a computer application can become a repetitive and time-consuming task for those users who generally make similar queries to get the information they need to work with. We believe that interface agents could help these users by personalizing the query-making and information retrieval tasks. Interface agents are characterized by their ability to learn users' interests in a given domain and to help them by making suggestions or by executing tasks on their behalf. Having this purpose in mind we have developed an agent, named QueryGuesser, to assist users of computer applications in which retrieving information from a database is a key task. This agent observes a user's behavior while he is working with the database and builds the user's profile. Then, QueryGuesser uses this profile to suggest the execution of queries according to the user's habits and interests, and to provide the user information relevant to him by making time-demanding queries in advance or by monitoring the events and operations occurring in the database system. In this way, the interaction between database users and databases becomes personalized while it is enhanced.

interface agents personalization user profiling Bayesian networks databases 

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References

  1. Amandi, A., Campo, M., Armentano, M., and Berdún, L. (2003). Intelligent Agents for Distance Learning. Informatics in Education 2(2), 161–180.Google Scholar
  2. Amandi, A., Campo, M., Armentano, M., and Berdún, L. (2003). Intelligent Agents for Distance Learning. Informatics in Education 2(2), 161–180.Google Scholar
  3. Beeferman, D. and Berger, A. (2000). Agglomerative Clustering of a Search Engine Query Log. In: KDD 2000 (pp. 407–416).Google Scholar
  4. Beeferman, D. and Berger, A. (2000). Agglomerative Clustering of a Search Engine Query Log. In: KDD 2000 (pp. 407–416).Google Scholar
  5. Billsus, D. and Pazzani, M.J. (1999). A personal news agent that talks, learns and explains. In O. Etzioni, J.P. Müller, and J.M. Bradshaw (Eds.), Proceedings of the Third International Conference on Autonomous Agents (Agents'99). ACM Press: Seattle, WA, USA (pp. 268–275).Google Scholar
  6. Bonett, M. (2001). Personalization of Web Services: Opportunities and Challenges. Ariadne Issue 28.Google Scholar
  7. Bonett, M. (2001). Personalization of Web Services: Opportunities and Challenges. Ariadne Issue 28.Google Scholar
  8. Boone, G. (1998). Concept Features in Re:Agent, an Intelligent Email Agent. In Proceedings of the Second Interntational Conference on Autonomous Agents—Agents 98 (pp. 141–148).Google Scholar
  9. Boone, G. (1998). Concept Features in Re:Agent, an Intelligent Email Agent. In Proceedings of the Second Interntational Conference on Autonomous Agents—Agents 98 (pp. 141–148).Google Scholar
  10. Burke, R., Hammond, K., Kulyukin, V., Lytinen, S., Tomuro, N., and Schoenberg, S. (1997). Question Answering from Frequently Asked Question Files. AI Magazine, 18(2), 57–66.Google Scholar
  11. Burke, R., Hammond, K., Kulyukin, V., Lytinen, S., Tomuro, N., and Schoenberg, S. (1997). Question Answering from Frequently Asked Question Files. AI Magazine, 18(2), 57–66.Google Scholar
  12. Conati, C., Gertner, A., and VanLehn, K. (2002). Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and User-Adapted Interaction, 12(4), 371–417.Google Scholar
  13. Conati, C., Gertner, A., and VanLehn, K. (2002). Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and User-Adapted Interaction, 12(4), 371–417.Google Scholar
  14. Cordero, D., Roldán, P., Schiaffino, S., and Amandi, A. 1999. Intelligent Agents Generating Personal Newspapers. In Proceedings ICEIS 99, International Conference on Enterprise Information Systems (pp. 195–202).Google Scholar
  15. Cordero, D., Roldán, P., Schiaffino, S., and Amandi, A. 1999. Intelligent Agents Generating Personal Newspapers. In Proceedings ICEIS 99, International Conference on Enterprise Information Systems (pp. 195–202).Google Scholar
  16. Cozman, F. (2000). Generalizing Variable Elimination in Bayesian Networks. In Workshop on Probabilistic Reasoning in AI—IBERAMIA-SBIA 2000 (pp. 27–32).Google Scholar
  17. Cozman, F. (2000). Generalizing Variable Elimination in Bayesian Networks. In Workshop on Probabilistic Reasoning in AI—IBERAMIA-SBIA 2000 (pp. 27–32).Google Scholar
  18. D'Ambrosio, B. (1999). Inference in Bayesian Networks. AI Magazine, 20(2), 21–35.Google Scholar
  19. D'Ambrosio, B. (1999). Inference in Bayesian Networks. AI Magazine, 20(2), 21–35.Google Scholar
  20. Dechter, R. (1996). Bucket Elimination: A Unifying Framework for Probabilistic Inference. In 12th Conf. On Uncertainty in AI (pp. 211–219).Google Scholar
  21. Dechter, R. (1996). Bucket Elimination: A Unifying Framework for Probabilistic Inference. In 12th Conf. On Uncertainty in AI (pp. 211–219).Google Scholar
  22. Friedman, N. and Goldszmidt, M. (1997). Sequential Update of Bayesian Network Structure. In Thirteenth Conference on Uncertainty in Artificial Intelligence (pp. 165–174).Google Scholar
  23. Friedman, N. and Goldszmidt, M. (1997). Sequential Update of Bayesian Network Structure. In Thirteenth Conference on Uncertainty in Artificial Intelligence (pp. 165–174).Google Scholar
  24. Godoy, D. and Amandi, A. (2000). PersonalSearcher: An Intelligent Agent for Searching Web Pages. Advances in Artificial Intelligence—Lectures Notes in Artificial Intelligence, LNAI 1952 (pp. 43–52).Google Scholar
  25. Godoy, D. and Amandi, A. (2000). PersonalSearcher: An Intelligent Agent for Searching Web Pages. Advances in Artificial Intelligence—Lectures Notes in Artificial Intelligence, LNAI 1952 (pp. 43–52).Google Scholar
  26. Haddawy, P. (1999). An Overview of Some Recent Developments in Bayesian Problem-Solving Techniques. AI Magazine 20(2), 11–19.Google Scholar
  27. Haddawy, P. (1999). An Overview of Some Recent Developments in Bayesian Problem-Solving Techniques. AI Magazine 20(2), 11–19.Google Scholar
  28. Han, Y. and Sterling, L. (1997). Agents for Citation Finding on the World Wide Web. In PAAM 97 (pp. 303–318).Google Scholar
  29. Han, Y. and Sterling, L. (1997). Agents for Citation Finding on the World Wide Web. In PAAM 97 (pp. 303–318).Google Scholar
  30. Heckerman, D. (1999). A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models (Also appears as Technical Report MSR-TR-95-06, Microsoft Research, March, 1995).Google Scholar
  31. Heckerman, D. (1999). A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models (Also appears as Technical Report MSR-TR-95-06, Microsoft Research, March, 1995).Google Scholar
  32. Horvitz, E. (1997). Agents with Beliefs: Reflections on Bayesian Methods for User Modeling. Invited Talk at 6th International Conference on User Modeling.Google Scholar
  33. Horvitz, E. (1997). Agents with Beliefs: Reflections on Bayesian Methods for User Modeling. Invited Talk at 6th International Conference on User Modeling.Google Scholar
  34. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, K. (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Fourteenth Conference on Uncertainty in Artificial Intelligence (pp. 256–265).Google Scholar
  35. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, K. (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Fourteenth Conference on Uncertainty in Artificial Intelligence (pp. 256–265).Google Scholar
  36. Jensen, F.V. (1996). An Introduction to Bayesian Networks, Springer Verlag: New York.Google Scholar
  37. Jensen, F.V. (1996). An Introduction to Bayesian Networks, Springer Verlag: New York.Google Scholar
  38. Lau, T. and Horvitz, E. (1999). Patterns of Search: Analyzing and Modeling Web Query Refinement. In Proceeding 7th International Conference On User Modeling (pp. 119–128).Google Scholar
  39. Lau, T. and Horvitz, E. (1999). Patterns of Search: Analyzing and Modeling Web Query Refinement. In Proceeding 7th International Conference On User Modeling (pp. 119–128).Google Scholar
  40. Lee, S.-I., Sung, C., and Cho, S.-B. (2001). An Effective Conversational Agent with User Modeling Based on Bayesian Network. Web Intelligence: Research and Development: First Asia-Pacific Conference, WI 2001—Lecture Notes in Computer Sciences 2198 (pp. 428–432).Google Scholar
  41. Lee, S.-I., Sung, C., and Cho, S.-B. (2001). An Effective Conversational Agent with User Modeling Based on Bayesian Network. Web Intelligence: Research and Development: First Asia-Pacific Conference, WI 2001—Lecture Notes in Computer Sciences 2198 (pp. 428–432).Google Scholar
  42. Lenz, M., Hübner, A., and Kunze, M. (1998). Question Answering with Textual CBR. In 3rd International Conference (FQAS' 98), Vol. 1495 of Lecture Notes in Computer Science (pp. 236–247), Springer-Verlag.Google Scholar
  43. Lieberman, H., Fry, C., and Weitzman, L. (2001). Exploring the Web with Reconnaissance Agents. Communications of the ACM, 44(8), 475–484.CrossRefGoogle Scholar
  44. Lieberman, H., Fry, C., and Weitzman, L. (2001). Exploring the Web with Reconnaissance Agents. Communications of the ACM, 44(8), 475–484.CrossRefGoogle Scholar
  45. Maes, P. (1994). Agents that Reduce Work and Information Overload. Communications of the ACM, 37(7), 31–40.CrossRefGoogle Scholar
  46. Maes, P. (1994). Agents that Reduce Work and Information Overload. Communications of the ACM, 37(7), 31–40.CrossRefGoogle Scholar
  47. Mitchell, T., Caruana, R., Dermott, J.M., and Zabowski, D. (1994). Experience with a Learning Personal Assistant. Communications of the ACM 37(7), 80–91.CrossRefGoogle Scholar
  48. Mitchell, T., Caruana, R., Dermott, J.M., and Zabowski, D. (1994). Experience with a Learning Personal Assistant. Communications of the ACM 37(7), 80–91.CrossRefGoogle Scholar
  49. Morris, J., Ree, P., and Maes, P. (2000). Sardine: Dynamic Seller Strategies in an Auction Marketplace. In Proceedings 2nd ACM Conference on Electronic Commerce EC 00 (pp. 128–134).Google Scholar
  50. Morris, J., Ree, P., and Maes, P. (2000). Sardine: Dynamic Seller Strategies in an Auction Marketplace. In Proceedings 2nd ACM Conference on Electronic Commerce EC 00 (pp. 128–134).Google Scholar
  51. Nilsson, N. (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann.Google Scholar
  52. Nilsson, N. (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann.Google Scholar
  53. Rocchio, J. (1971). Relevance Feedback in Information Retrieval. The SMART Retrieval System: Experiments in Automatic Document Processing—Chapter 4 (pp. 313–323).Google Scholar
  54. Rocchio, J. (1971). Relevance Feedback in Information Retrieval. The SMART Retrieval System: Experiments in Automatic Document Processing—Chapter 4 (pp. 313–323).Google Scholar
  55. Segal, R. and Kephart, J. (2000). Swiftfile: An intelligent assistant for organizing e-mail. In In AAAI 2000 Spring Symposium on Adaptive User Interfaces.Google Scholar
  56. Segal, R. and Kephart, J. (2000). Swiftfile: An intelligent assistant for organizing e-mail. In In AAAI 2000 Spring Symposium on Adaptive User Interfaces.Google Scholar
  57. Sheth, B. and Maes, P. (1993). Evolving Agents for Personalized Information Filtering. In Proceedings 9th IEEE Conference on Artificial Intelligence for Applications (CAIA 93) (pp. 345–352).Google Scholar
  58. Sheth, B. and Maes, P. (1993). Evolving Agents for Personalized Information Filtering. In Proceedings 9th IEEE Conference on Artificial Intelligence for Applications (CAIA 93) (pp. 345–352).Google Scholar
  59. Spiegelhalter, D.J. and Lauritzen, S.L. (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks 20, 579–605.MathSciNetGoogle Scholar
  60. Spiegelhalter, D.J. and Lauritzen, S.L. (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks 20, 579–605.MathSciNetGoogle Scholar
  61. Wen, J., Nie, J., and Zhang, H. (2002). Query Clustering Using User Logs. ACM Transactions on Information Systems, 20(1), 59–81.CrossRefGoogle Scholar
  62. Wen, J., Nie, J., and Zhang, H. (2002). Query Clustering Using User Logs. ACM Transactions on Information Systems, 20(1), 59–81.CrossRefGoogle Scholar
  63. Wen, J.-R., Nie, J.-Y., and Zhang, H.-J. (2001). Clustering user queries of a search engine. In World Wide Web (pp. 162–168).Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.ISISTAN Research Institute, Fac. Ciencias ExactasUniv. Nac. del Centro de la Pcia. Bs. As.Bs. As.Argentina
  2. 2.CONICETComisión Nacional de Investigaciones Científicas y TécnicasArgentina

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