How dirty is your relational database? An axiomatic approach

Maria Vanina Martinez; Andrea Pugliese; Gerardo I. Simari; V. S. Subrahmanian; Henri Prade

doi:10.1007/978-3-540-75256-1_12

How dirty is your relational database? An axiomatic approach

Maria Vanina Martinez^*, Andrea Pugliese, Gerardo I. Simari, V. S. Subrahmanian, Henri Prade

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

45 Scopus citations

Abstract

There has been a significant amount of interest in recent years on how to reason about inconsistent knowledge bases. However, with the exception of three papers by Lozinskii, Hunter and Konieczny and by Grant and Hunter, there has been almost no work on characterizing the degree of dirtiness of a database. One can conceive of many reasonable ways of characterizing how dirty a database is. Rather than choose one of many possible measures, we present a set of axioms that any dirtiness measure must satisfy. We then present several plausible candidate dirtiness measures from the literature (including those of Hunter-Konieczny and Grant-Hunter) and identify which of these satisfy our axioms and which do not. Moreover, we define a new dirtiness measure which satisfies all of our axioms.

Original language	English (US)
Title of host publication	Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 9th European Conference, ECSQARU 2007, Proceedings
Publisher	Springer Verlag
Pages	103-114
Number of pages	12
ISBN (Print)	9783540752554
DOIs	https://doi.org/10.1007/978-3-540-75256-1_12
State	Published - 2007
Externally published	Yes
Event	9th European Conference on Symbolic and Qualitative Approaches to Reasoning with Uncertainty, ECSQARU 2007 - Hammamet, Tunisia Duration: Oct 31 2007 → Nov 2 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4724 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th European Conference on Symbolic and Qualitative Approaches to Reasoning with Uncertainty, ECSQARU 2007
Country/Territory	Tunisia
City	Hammamet
Period	10/31/07 → 11/2/07

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-75256-1_12

Cite this

Martinez, M. V., Pugliese, A., Simari, G. I., Subrahmanian, V. S., & Prade, H. (2007). How dirty is your relational database? An axiomatic approach. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 9th European Conference, ECSQARU 2007, Proceedings (pp. 103-114). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4724 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-75256-1_12

Martinez, Maria Vanina ; Pugliese, Andrea ; Simari, Gerardo I. et al. / How dirty is your relational database? An axiomatic approach. Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 9th European Conference, ECSQARU 2007, Proceedings. Springer Verlag, 2007. pp. 103-114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "There has been a significant amount of interest in recent years on how to reason about inconsistent knowledge bases. However, with the exception of three papers by Lozinskii, Hunter and Konieczny and by Grant and Hunter, there has been almost no work on characterizing the degree of dirtiness of a database. One can conceive of many reasonable ways of characterizing how dirty a database is. Rather than choose one of many possible measures, we present a set of axioms that any dirtiness measure must satisfy. We then present several plausible candidate dirtiness measures from the literature (including those of Hunter-Konieczny and Grant-Hunter) and identify which of these satisfy our axioms and which do not. Moreover, we define a new dirtiness measure which satisfies all of our axioms.",

author = "Martinez, {Maria Vanina} and Andrea Pugliese and Simari, {Gerardo I.} and Subrahmanian, {V. S.} and Henri Prade",

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Martinez, MV, Pugliese, A, Simari, GI, Subrahmanian, VS & Prade, H 2007, How dirty is your relational database? An axiomatic approach. in Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 9th European Conference, ECSQARU 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4724 LNAI, Springer Verlag, pp. 103-114, 9th European Conference on Symbolic and Qualitative Approaches to Reasoning with Uncertainty, ECSQARU 2007, Hammamet, Tunisia, 10/31/07. https://doi.org/10.1007/978-3-540-75256-1_12

How dirty is your relational database? An axiomatic approach. / Martinez, Maria Vanina; Pugliese, Andrea; Simari, Gerardo I. et al.
Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 9th European Conference, ECSQARU 2007, Proceedings. Springer Verlag, 2007. p. 103-114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4724 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - How dirty is your relational database? An axiomatic approach

AU - Martinez, Maria Vanina

AU - Pugliese, Andrea

AU - Simari, Gerardo I.

AU - Subrahmanian, V. S.

AU - Prade, Henri

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AB - There has been a significant amount of interest in recent years on how to reason about inconsistent knowledge bases. However, with the exception of three papers by Lozinskii, Hunter and Konieczny and by Grant and Hunter, there has been almost no work on characterizing the degree of dirtiness of a database. One can conceive of many reasonable ways of characterizing how dirty a database is. Rather than choose one of many possible measures, we present a set of axioms that any dirtiness measure must satisfy. We then present several plausible candidate dirtiness measures from the literature (including those of Hunter-Konieczny and Grant-Hunter) and identify which of these satisfy our axioms and which do not. Moreover, we define a new dirtiness measure which satisfies all of our axioms.

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Martinez MV, Pugliese A, Simari GI, Subrahmanian VS, Prade H. How dirty is your relational database? An axiomatic approach. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 9th European Conference, ECSQARU 2007, Proceedings. Springer Verlag. 2007. p. 103-114. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-75256-1_12

How dirty is your relational database? An axiomatic approach

Abstract

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