Discovering branching and fractional dependencies in databases

The discovery of dependencies between attributes in databases is an important problem in data mining, and can be applied to facilitate future decision-making. In the present paper some properties of the branching dependencies are examined. We define a minimal branching dependency and we propose an algorithm for finding all minimal branching dependencies between a given set of attributes and a given attribute in a relation of a database. Our examination of the branching dependencies is motivated by their application in a database storing realized sales of products. For example, finding out that arbitrary p products have totally attracted at most q new users can prove to be crucial in supporting the decision making. In addition, we also consider the fractional and the fractional branching dependencies. Some properties of these dependencies are examined. An algorithm for finding all fractional dependencies between a given set of attributes and a given attribute in a database relation is proposed. We examine the general case of an arbitrary relation, as well as a particular case where the problem of discovering the fractional dependencies is considerably simplified.

[1]  Mehmet M. Dalkilic,et al.  Improving Query Evaluation with Approximate Functional Dependency Based Decompositions , 2002, BNCOD.

[2]  Hannu Toivonen,et al.  TANE: An Efficient Algorithm for Discovering Functional and Approximate Dependencies , 1999, Comput. J..

[3]  Heikki Mannila,et al.  Approximate Inference of Functional Dependencies from Relations , 1995, Theor. Comput. Sci..

[4]  Myoung-Ho Kim,et al.  Efficient incremental maintenance of data cubes , 2006, VLDB.

[5]  Tsvetanka L. Georgieva,et al.  FINDING THE FRACTIONAL AND THE FRACTIONAL BRANCHING DEPENDENCIES IN DATABASES , 2007 .

[6]  Mohammad Reza Abbasifard,et al.  Using Automated Database Reverse Engineering for Database Integration , 2008 .

[7]  Hamid Pirahesh,et al.  Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals , 1996, Data Mining and Knowledge Discovery.

[8]  Gary A. Coen Database Lexicography , 2002, Data Knowl. Eng..

[9]  Peter A. Flach,et al.  Database Dependency Discovery: A Machine Learning Approach , 1999, AI Commun..

[10]  Subbarao Kambhampati,et al.  Mining approximate functional dependencies and concept similarities to answer imprecise queries , 2004, WebDB '04.

[11]  Panos Kalnis,et al.  View selection using randomized search , 2002, Data Knowl. Eng..

[12]  G. Bogdanova,et al.  Finding the Error-Correcting Functional Dependency by Using the Fractal Dimension ∗ , 2005 .

[13]  Mehmet M. Dalkilic,et al.  Information dependencies , 2000, PODS '00.

[14]  Edward L. Robertson,et al.  FastFDs: A Heuristic-Driven, Depth-First Algorithm for Mining Functional Dependencies from Relation Instances - Extended Abstract , 2001, DaWaK.

[15]  Ed Tittel,et al.  MCDBA, MCSE, MCSD, MCAD Training Guide (70-229): SQL Server 2000 Database Design and Implementation , 2003 .

[16]  János Demetrovics,et al.  Minimal representations of branching dependencies , 1995 .

[17]  Jennifer Widom,et al.  Database Systems: The Complete Book , 2001 .

[18]  Christie I. Ezeife Selecting and materializing horizontally partitioned warehouse views , 2001, Data Knowl. Eng..

[19]  Siegfried Bell Discovery and Maintenance of Functional Dependencies by Independencies , 1995, KDD.

[20]  Tsvetanka L. Georgieva,et al.  FINDING THE BRANCHING DEPENDENCIES IN RANDOM DATABASES , 2006 .

[21]  Edward L. Robertson,et al.  On approximation measures for functional dependencies , 2004, Inf. Syst..

[22]  Dieter Jungnickel,et al.  Graphs, Networks, and Algorithms , 1980 .

[23]  Hua-Gang Li,et al.  Ranking Aggregates , 2004 .

[24]  Jiawei Han,et al.  Towards on-line analytical mining in large databases , 1998, SGMD.

[25]  Gyula O. H. Katona,et al.  The Characterization of Branching Dependencies , 1992, Discret. Appl. Math..

[26]  Victor Matos,et al.  SQL-based discovery of exact and approximate functional dependencies , 2004, ITiCSE-WGR.

[27]  Dennis P. Groth,et al.  An Entropy-based Approach to Visualizing Database Structure , 2002, VDB.

[28]  Matteo Golfarelli,et al.  Materialization of fragmented views in multidimensional databases , 2004, Data Knowl. Eng..