Abstract
The result quality of queries incorporating impreciseness can be improved by the specification of user-defined weights. Existing approaches evaluate weighted queries by applying arithmetic evaluations on top of the query’s intrinsic logic. This complicates the usage of logic-based optimization. Therefore, we suggest a weighting approach that is completely embedded in a logic.
In order to facilitate the user interaction with the system, we exploit the intuitively comprehensible concept of preferences. In addition, we use a machine-based learning algorithm to learn weighting values in correspondence to the user’s intended semantics of a posed query. Experiments show the utility of our approach.
Similar content being viewed by others
References
Agrawal, S., Chaudhuri, S., Das, G., Gionis, A.: Automated ranking of database query results. In: CIDR (2003)
Birkhoff, G., Neumann, J.v.: The logic of quantum mechanics. Ann. of Math. 37, 823–843 (1936)
Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering, pp. 421–430. IEEE Computer Society, Washington (2001)
Bosc, P., Pivert, O.: SQLf: a relational database language for fuzzy querying. IEEE Trans. Fuzzy Syst. 3(1), 1–17 (1995)
Breslow, L.A., Aha, D.W.: Simplifying decision trees: a survey. Knowl. Eng. Rev. 12(1), 1–40 (1997). doi:10.1017/S0269888997000015
Bruce, V., Green, P.R.: Visual Perception—Physiology, Psychology and Ecology (2nd edn., reprinted). Lawrence Erlbaum Associates, Publishers, Hove (1993)
Chaudhuri, S., Ramakrishnan, R., Weikum, G.: Integrating DB and IR technologies: What is the sound of one hand clapping? In: CIDR, pp. 1–12 (2005)
Chomicki, J.: Preference formulas in relational queries. ACM Trans. Database Syst. 28(4), 427–466 (2003)
Chomicki, J.: Database querying under changing preferences. Ann. Math. Artif. Intell. 50(1–2), 79–109 (2007)
Ciaccia, P., Montesi, D., Penzo, W., Trombetta, A.: Imprecision and user preferences in multimedia queries: A generic algebraic approach. In: Schewe, K.D., Thalheim, B. (eds.) FoIKS: Foundations of Information and Knowledge Systems, First International Symposium, FoIKS 2000, Burg, Germany, February 14–17, 2000, Lecture Notes in Comput. Sci., vol. 1762, pp. 50–71. Springer, Berlin (2000)
Fagin, R., Wimmers, E.L.: A formula for incorporating weights into scoring rules. Theor. Comput. Sci. 239(2), 309–338 (2000)
Kießling, W.: Foundations of preferences in database systems. In: Proc. of the 28th Int. Conf. on Very Large Data Bases, VLDB’02, Hong Kong, China, August 2002, pp. 311–322. Morgan Kaufmann, San Mateo (2002)
Klose, A., Nürnberger, A.: On the properties of prototype-based fuzzy classifiers. IEEE Trans. Syst. Man Cybernet. B 37(4), 817–835 (2007)
Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: Proc. of the 28th Int. Conf. on Very Large Data Bases, VLDB’02, Hong Kong, China, August 2002, pp. 275–286. Morgan Kaufmann, San Mateo (2002)
Lee, J.H.: Properties of extended boolean models in information retrieval. In: SIGIR (ed.) Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’94, pp. 182–190. Springer, New York (1994)
Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2(1), 1–19 (2006). doi:10.1145/1126004.1126005
Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)
Rocchio, J.J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System—Experiments in Automatic Document Processing, pp. 313–323. Prentice Hall International, Englewood Cliffs (1971). Chap. 14
Rowe, L.A., Jain, R.: ACM SIGMM retreat report on future directions in multimedia research. ACM Trans. Multimedia Comput. Commun. Appl. 1(1), 3–13 (2005) doi:10.1145/1047936.1047938
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Tech. Rep., Ithaca (1988)
Salton, G., Fox, E.A., Wu, H.: Extended boolean information retrieval. Commun. ACM 26(11), 1022–1036 (1983). doi:10.1145/182.358466
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Ithaca (1974)
Schmitt, I.: QQL: A DB&IR query language. VLDB J. 17(1), 39–56 (2008)
Schmitt, I., Schulz, N.: Similarity relational calculus and its reduction to a similarity algebra. In: Seipel, D., Turull-Torres, J.M. (eds.) Third Intern. Symposium on Foundations of Information and Knowledge Systems (FoIKS’04), Austria, February 17–20. Lecture Notes in Comput. Sci., vol. 2942, pp. 252–272. Springer, Berlin (2004)
Schmitt, I., Zellhöfer, D., Nürnberger, A.: Towards quantum logic based multimedia retrieval. In: Annual Meeting of the North American Fuzzy Information Processing Society, pp. 1–6 (2008)
Schulz, N., Schmitt, I.: Relevanzwichtung in komplexen Ähnlichkeitsanfragen. In: Weikum, G., Schöning, H., Rahm, E. (eds.) Datenbanksysteme in Business, Technologie und Web, BTW’03, 10. GI-Fachtagung, Leipzig, Februar 2003. Lecture Notes in Informatics, vol. 26, pp. 187–196. Gesellschaft für Informatik, Bonn (2003)
Selfridge, O.G.: Pandemonium. A paradigm for learning. The Mechanics of Thought Processes (1959)
Shneiderman, B., Plaisant, C.: Designing the User Interface: Strategies for Effective Human–Computer Interaction (4 edn.) Pearson, Boston (2005). URL http://www.gbv.de/dms/ilmenau/toc/492668051.pdf
Weikum, G.: DB&IR: both sides now. In: SIGMOD (ed.) Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD’07, pp. 25–30. ACM, New York (2007)
Claremont Workshop: The Claremont database research self assessment. Tech. rep. (2008). URL http://db.cs.berkeley.edu/claremont/claremontreport08.pdf
Zadeh, L.A.: Fuzzy logic. IEEE Comput. 21(4), 83–93 (1988)
Ziegler, M.: Quantum logic: order structures in quantum mechanics. Tech. rep., University Paderborn, Germany (2005). URL http://wwwcs.upb.de/cs/ag-madh/WWW/ziegler/qlogic.html
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zellhöfer, D., Schmitt, I. A preference-based approach for interactive weight learning: learning weights within a logic-based query language. Distrib Parallel Databases 27, 31–51 (2010). https://doi.org/10.1007/s10619-009-7049-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10619-009-7049-4