Abstract
A Boolean logic-based evaluation of a database query returns true on match and false on mismatch. Unfortunately, there are many application scenarios where such an evaluation is not possible or does not adequately meet user expectations about vague and uncertain conditions. Consequently, there is a need for incorporating impreciseness and proximity into a logic-based query language. In this work we propose a probabilistic interpretation for our query language CQQL which is based on a geometric retrieval model. In detail, we show that the CQQL can evaluate arbitrary similarity conditions in a probabilistic fashion. Furthermore, we lay a theoretical foundation for the combination of CQQL with other probabilistic semantics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barbar, D., Garcia-Molina, H., Porter, D.: The management of probabilistic data. IEEE Trans. Knowl. Data Eng. 4(5), 487–502 (1992)
Cavallo, R., Pittarelli, M.: The theory of probabilistic databases. In: Stocker, P.M., Kent, W., Hammersley, P. (eds.) VLDB, pp. 71–81. Morgan Kaufmann, San Francisco (1987)
Dalvi, N., Suciu, D.: Efficient query evaluation on probabilistic databases. VLDB J. 16(4), 523–544 (2007)
Dey, D., Sarkar, S.: A probabilistic relational model and algebra. ACM Trans. Database Syst. 21(3), 339–369 (1996)
Fuhr, N., Roelleke, T.: A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Trans. Inf. Syst. 15(1), 32–66 (1997)
Galindo, J., Urrutia, A., Piattini, M.: Fuzzy Databases: Modeling, Design and Implementation. Idea Group Publishing, Hershey (2006)
Koch, C.: MayBMS: A System for Managing Large Uncertain and Probabilistic Databases. In: Managing and Mining Uncertain Data, ch. 6. Springer, Heidelberg (2008)
Lehrack, S., Schmitt, I., Saretz, S.: CQQL: A Quantum Logic-Based Extension of the Relation Domain Calculus. In: Proceedings of the International Workshop Logic in Databases (LID 2009) (October 2009)
Nielson, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
Schmitt, I.: QQL: A DB&IR Query Language. VLDB J. 17(1), 39–56 (2008)
Schmitt, I., Nuernberger, A., Lehrack, S.: On the relation between fuzzy and quantum logic. In: Seising, R. (ed.) Views on Fuzzy Sets and Systems from Different Perspectives. STUDFUZZ, vol. 243, pp. 417–438. Springer, Heidelberg (2009)
van Rijsbergen, C.J.: Information Retrieval, Butterworth (1979)
Widom, J.: Trio: A system for data, uncertainty, and lineage. In: Managing and Mining Uncertain Data, pp. 113–148. Springer, Heidelberg (2008)
Zadeh, L.A.: Fuzzy logic. IEEE Computer 21(4), 83–93 (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lehrack, S., Schmitt, I. (2011). A Probabilistic Interpretation for a Geometric Similarity Measure. In: Liu, W. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2011. Lecture Notes in Computer Science(), vol 6717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22152-1_63
Download citation
DOI: https://doi.org/10.1007/978-3-642-22152-1_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22151-4
Online ISBN: 978-3-642-22152-1
eBook Packages: Computer ScienceComputer Science (R0)