Extracting functional dependencies from existing databases is a useful technique in relational theory, database design and data mining. Functional dependencies are a key property of relational schema design. A functional dependency is a database constraint between two sets of attributes. In this study we present a comparative study over TANE, FUN, FD_Mine, FastFDs and Dep_Miner, and we propose a new technique, KlipFind, to extract dependencies from relations efficiently. KlipFind employs a depth-first, heuristic driven approach as a solution. Our study indicates that KlipFind is more space efficient than any of the existing solutions and highly efficient in finding keys for relations.

Title: Efficient Detection of XML Integrity Constraints Author: Michal Švirec Department: Department of Software Engineering Supervisor: RNDr. Irena Mlýnková, Ph.D. Abstract: Knowledge of integrity constraints covered in XML data is an impor- tant aspect of efficient data processing. However, although integrity constraints are defined for the given data, it is a common phenomenon that data violate the predefined set of constraints. Therefore detection of these inconsistencies and consecutive repair has emerged. This work extends and refines recent approaches to repairing XML documents violating defined set of integrity constraints, specif- ically so-called functional dependencies. The work proposes the repair algorithm incorporating the weight model and also involve a user into the process of de- tection and subsequent application of appropriate repair of inconsistent XML documents. Experimental results are part of the work. Keywords: XML, functional dependency, functional dependencies violations, vi- olations repair

La qualité des données présente un grand enjeu au sein d'une organisation et influe énormément sur la qualité de ses services et sur sa rentabilité. La présence de données erronées engendre donc des préoccupations importantes autour de cette qualité. Ce rapport traite la problématique de l'amélioration de la qualité des données dans les grosses masses de données. Notre approche consiste à aider l'utilisateur afin de mieux comprendre les schémas des données manipulées, mais aussi définir les actions à réaliser sur celles-ci. Nous abordons plusieurs concepts tels que les anomalies des données au sein d'une même colonne, et les anomalies entre les colonnes relatives aux dépendances fonctionnelles. Nous proposons dans ce contexte plusieurs moyens de pallier ces défauts en nous intéressons à la performance des traitements ainsi opérés. / Data quality represents a major challenge because the cost of anomalies can be very high especially for large databases in enterprises that need to exchange information between systems and integrate large amounts of data. Decision making using erroneous data has a bad influence on the activities of organizations. Quantity of data continues to increase as well as the risks of anomalies. The automatic correction of these anomalies is a topic that is becoming more important both in business and in the academic world. In this report, we propose an approach to better understand the semantics and the structure of the data. Our approach helps to correct automatically the intra-column anomalies and the inter-columns ones. We aim to improve the quality of data by processing the null values and the semantic dependencies between columns.