A Probabilistic Data Model and Its Semantics

As database systems are increasingly being used in advanced applications, it is becoming common that data in these applications contain some elements of uncertainty. These arise from many factors, such as measurement errors and cognitive errors. As such, many researchers have focused on defining comprehensive uncertainty data models of uncertainty database systems. However, existing uncertainty data models do not adequately support some applications. Moreover, very few works address uncertainty tuple calculus. In this paper we advocate a probabilistic data model for representing uncertain information. In particular, we establish a probabilistic tuple calculus language and its semantics to meet the corresponding probabilistic relational algebra.

[1]  Eugene Wong,et al.  A statistical approach to incomplete information in database systems , 1982, TODS.

[2]  Bill P. Buckles,et al.  Information-theoretical characterization of fuzzy relational databases , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Tomasz Imielinski,et al.  Incomplete Information in Relational Databases , 1984, JACM.

[4]  Frederick E. Petry,et al.  Extending the fuzzy database with fuzzy numbers , 1984, Inf. Sci..

[5]  Henri Prade,et al.  Generalizing Database Relational Algebra for the Treatment of Incomplete/Uncertain Information and Vague Queries , 1984, Inf. Sci..

[6]  Abraham Kandel,et al.  Implementing Imprecision in Information Systems , 1985, Inf. Sci..

[7]  Moshe Y. Vardi.  Querying logical databases , 1985, J. Comput. Syst. Sci..

[8]  Haim Mendelson,et al.  Incomplete information costs and database design , 1986, TODS.

[9]  Arun K. Majumdar,et al.  Fuzzy Functional Dependencies and Lossless Join Decomposition of Fuzzy Relational Database Systems , 1988, ACM Trans. Database Syst..

[10]  Jeffrey D. Uuman.  Principles of database and knowledge- base systems , 1989 .

[11]  Hector Garcia-Molina,et al.  The Management of Probabilistic Data , 1992, IEEE Trans. Knowl. Data Eng..

[12]  Michael Pittarelli,et al.  An Algebra for Probabilistic Databases , 1994, IEEE Trans. Knowl. Data Eng..

[13]  Laks V. S. Lakshmanan,et al.  Probabilistic Deductive Databases , 1994, ILPS.

[14]  V. S. Subrahmanian,et al.  Stable Semantics for Probabilistic Deductive Databases , 1994, Inf. Comput..

[15]  Sumit Sarkar,et al.  A probabilistic relational model and algebra , 1996, TODS.

[16]  Laks V. S. Lakshmanan,et al.  ProbView: a flexible probabilistic database system , 1997, TODS.

[17]  Norbert Fuhr,et al.  A probabilistic relational algebra for the integration of information retrieval and database systems , 1997, TOIS.

[18]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[19]  Xindong Wu,et al.  Mining Both Positive and Negative Association Rules , 2002, ICML.

[20]  Chengqi Zhang,et al.  Discovering causality in large databases , 2002, Appl. Artif. Intell..

[21]  Chengqi Zhang,et al.  Propagating temporal relations of intervals by matrix , 2002, Appl. Artif. Intell..

[22]  C No.  Anytime Mining for Multi-User Applications , 2002 .

[23]  Shichao Zhang,et al.  Product hierarchy-based customer profiles for electronic commerce recommendation , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[24]  Chengqi Zhang,et al.  Discovering Associations in Very Large Databases by Approximating , 2003, Acta Cybern..

[25]  Zili Zhang,et al.  An agent-based hybrid framework for database mining , 2003, Appl. Artif. Intell..

[26]  Zili Zhang,et al.  Temporal constraint satisfaction in matrix method , 2003, Appl. Artif. Intell..

[27]  Xindong Wu,et al.  Synthesizing High-Frequency Rules from Different Data Sources , 2003, IEEE Trans. Knowl. Data Eng..

[28]  Chengqi Zhang,et al.  Post-mining: maintenance of association rules by weighting , 2003, Inf. Syst..

[29]  Li Liu,et al.  Mining Dynamic databases by Weighting , 2003, Acta Cybern..

[30]  Geoffrey I. Webb,et al.  Identifying Approximate Itemsets of Interest in Large Databases , 2004, Applied Intelligence.

[31]  Arbee L. P. Chen,et al.  Answering heterogeneous database queries with degrees of uncertainty , 2005, Distributed and Parallel Databases.