IDFQ: An Interface for Database Flexible Querying

In traditional database management systems, imprecision has not been taken into account so one can say that there is some sort of lack of flexibility. The main cause is that queries retrieve only elements which precisely match to the given Boolean query. Many works were proposed in this context. The majority of these works are based on Fuzzy logic. In this paper, we discuss the flexibility in databases by referring to the Formal Concept Analysis theory. We propose an environment based on this theory which permits the flexible modelling and querying of a database with powerful retrieval capability. The architecture of this environment reuses the existing structure of a traditional database and adds new components (Metaknowledge Base, Context Base, Concept Base, etc.) while guaranteeing interoperability between them.

[1]  José Galindo,et al.  Fuzzy Databases: Modeling, Design, and Implementation , 2006 .

[2]  Galia Angelova,et al.  Conceptual Structures: Integration and Interfaces , 2002, Lecture Notes in Computer Science.

[3]  Olga Pons,et al.  Client-server arquitecture for fuzzy relational databases , 1996 .

[4]  Habib Ounelli,et al.  Improving Cluster Method Quality by Validity Indices , 2007, FLAIRS Conference.

[5]  Minyar Sassi Hidri,et al.  Using Gaussians Functions to Determine Representative Clustering Prototypes , 2006, 17th International Workshop on Database and Expert Systems Applications (DEXA'06).

[6]  Huan Liu,et al.  Discretization: An Enabling Technique , 2002, Data Mining and Knowledge Discovery.

[7]  Ramez Elmasri,et al.  Fundamentals of Database Systems , 1989 .

[8]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[9]  Rudolf Wille,et al.  Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts , 2009, ICFCA.

[10]  Gerd Stumme,et al.  Local Scaling in Conceptual Data Systems , 1996, ICCS.

[11]  Adnan Yazici,et al.  IFOOD: An Intelligent Fuzzy Object-Oriented Database Architecture , 2003, IEEE Trans. Knowl. Data Eng..

[12]  Ramez Elmasri,et al.  Fundamentals of Database Systems, 5th Edition , 2006 .

[13]  Amel Grissa Touzi,et al.  A Migration Approach from Crisp Databases to Fuzzy Databases , 2007, 2007 IEEE International Fuzzy Systems Conference.

[14]  Patrick Bosc,et al.  SQLf: a relational database language for fuzzy querying , 1995, IEEE Trans. Fuzzy Syst..

[15]  María Amparo Vila Miranda,et al.  Client/server architecture for fuzzy relational databases , 1996 .

[16]  Gerd Stumme,et al.  Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods , 1998, PKDD.

[17]  Gerd Stumme,et al.  Formal Concept Analysis: foundations and applications , 2005 .

[18]  Juan Miguel Medina,et al.  FREDDI: A fuzzy RElational deductive database interface , 1997 .

[19]  S. Kotsiantis,et al.  Discretization Techniques: A recent survey , 2006 .

[20]  Joachim Hereth.  Relational Scaling and Databases , 2002 .

[21]  Rokia Missaoui,et al.  INCREMENTAL CONCEPT FORMATION ALGORITHMS BASED ON GALOIS (CONCEPT) LATTICES , 1995, Comput. Intell..

[22]  Usama M. Fayyad,et al.  On the Handling of Continuous-Valued Attributes in Decision Tree Generation , 1992, Machine Learning.

[23]  Amel Grissa Touzi,et al.  How to Achieve Fuzzy Relational Databases Managing Fuzzy Data and Metadata , 2008, Handbook of Research on Fuzzy Information Processing in Databases.