Stéphane Lopes

Efficient Discovery of Functional Dependencies and Armstrong Relations

In this paper, we propose a new efficient algorithm called Dep-Miner for discovering minimal non-trivial functional dependencies from large databases. Based on theoretical foundations, our approach combines the discovery of functional dependencies along with the construction of real-world Armstrong relations (without additional execution time). These relations are small Armstrong relations taking their values in the initial relation. Discovering both minimal functional dependencies and real-world Armstrong relations facilitate the tasks of database administrators when maintaining and analyzing existing databases. We evaluate Dep-Miner performances by using a new benchmark database. Experimental results show both the efficiency of our approach compared to the best current algorithm (i.e. Tane), and the usefulness of real-world Armstrong relations.

Efficient Algorithms for Mining Inclusion Dependencies

Foreign keys form one of the most fundamental constraints for relational databases. Since they are not always defined in existing databases, algorithms need to be devised to discover foreign keys. One of the underlying problems is known to be the inclusion dependency (IND) inference problem. In this paper a new data mining algorithm for computing unary INDs is given. From unary INDs, we also propose a levelwise algorithmto discover all remaining INDs, where candidate INDs of size i + 1 are generated fromsatisfied INDs of size i, (i > 0).An implementation of these algorithms has been achieved and tested against synthetic databases. Up to our knowledge, this paper is the first one to address in a comprehensive manner this data mining problem, from algorithms to experimental results.

Functional and approximate dependency mining: database and FCA points of view

In this article, we deal with the functional and approximate dependency inference problem by pointing out some relationships between relational database theory and formal concept analysis (FCA). More precisely, the notion of functional dependency in database is compared to the notion of implication in FCA. We propose a framework and several algorithms for mining these dependencies from large database relations. The common data centric step of this framework is the discovery of agree sets, which are closed sets with respect to the closure operator for functional dependency. Two approaches for discovering agree sets from database relations are proposed: the former is a database approach based on SQL queries and the latter is a data mining approach based on partitions. Experiments were performed in order to compare the two proposed methods.

Unary and n-ary inclusion dependency discovery in relational databases

Foreign keys form one of the most fundamental constraints for relational databases. Since they are not always defined in existing databases, the discovery of foreign keys turns out to be an important and challenging task. The underlying problem is known to be the inclusion dependency (IND) inference problem. In this paper, data-mining algorithms are devised for IND inference in a given database. We propose a two-step approach. In the first step, unary INDs are discovered thanks to a new preprocessing stage which leads to a new algorithm and to an efficient implementation. In the second step, n-ary IND inference is achieved. This step fits in the framework of levelwise algorithms used in many data-mining algorithms. Since real-world databases can suffer from some data inconsistencies, approximate INDs, i.e. INDs which almost hold, are considered. We show how they can be safely integrated into our unary and n-ary discovery algorithms. An implementation of these algorithms has been achieved and tested against both synthetic and real-life databases. Up to our knowledge, no other algorithm does exist to solve this data-mining problem.

Discovering interesting inclusion dependencies: application to logical database tuning

Inclusion dependencies together with functional dependencies form the most important data dependencies used in practice. Inclusion dependencies are important for various database applications such as database design and maintenance, semantic query optimization and efficient view maintenance of data warehouse. Existing approaches for discovering inclusion dependencies consist in producing the whole set of inclusion dependencies holding in a database, leaving the task of selecting the interesting ones to an expert user.In this paper, we take another look at the problem of discovering inclusion dependencies. We exploit the logical navigation, inherently available in relational databases through workloads of SQL statements, as a guess to automatically find out only interesting inclusion dependencies. This assumption leads us to devise a tractable algorithm for discovering interesting inclusion dependencies. Within this framework, approximate dependencies, i.e. inclusion dependencies which almost hold, are also considered.As an example, we present a novel application, namely self-tuning the logical database design, where the discovered inclusion dependencies can be used effectively.

Analysis of existing databases at the logical level: the DBA companion project

Whereas physical database tuning has received a lot of attention over the last decade, logical database tuning seems to be under-studied. We have developed a project called DBA Companion devoted to the understanding of logical database constraints from which logical database tuning can be achieved.In this setting, two main data mining issues need to be addressed: the first one is the design of efficient algorithms for functional dependencies and inclusion dependencies inference and the second one is about the interestingness of the discovered knowledge. In this paper, we point out some relationships between database analysis and data mining. In this setting, we sketch the underlying themes of our approach. Some database applications that could benefit from our project are also described, including logical database tuning.

A personalized access model: concepts and services for content delivery platforms

Access to relevant information, adapted to users needs, preferences and environment, is a challenge in many applications running in content delivery platforms, like IPTV, VoD and mobile Video. In order to provide users with personalized content, applications use various techniques such as content recommendation, content filtering, preference-driven queries, etc. These techniques exploit different knowledge organized into profiles and contexts. However, there is not a common understanding of these concepts and there is no clear foundation of what a personalized access model should be. This paper contributes to this concern by providing, through a meta model, a clear distinction between profile and context, and by providing a set of services which constitutes a basement to the definition of a personalized access model (PAM). Our PAM definition allows applications to interoperate in multiple personalization scenarios, including, preference-based recommendation, context-aware content delivery, personalized access to multiple contents, etc. Concepts and services proposed are tightly defined with respect to real applications requirements provided by Alcatel-Lucent.

Discovery of Interesting Data Dependencies from a Workload of SQL Statements

Discovering data dependencies consists in producing the whole set of a given class of data dependencies holding in a database, the task of selecting the interesting ones being usually left to an expert user. In this paper we take another look at the problems of discovering inclusion and functional dependencies in relational databases. We define rigourously the so-called logical navigation from a workload of SQL statements. This assumption leads us to devise tractable algorithms for discovering “interesting” inclusion and functional dependencies.

Accès personnalisé à des sources de données multiples : Evaluation de deux approches de reformulation de requêtes

Mediation systems allow transparent access to a set of heterogeneous data sources. Personalization is a mechanism that helps users to express their needs and enables them to obtain relevant results by exploiting a set of preferences in their profiles. In a mediation context, the personalization process must take into account not only the user profile but also the semantic description of the data sources given in mediation queries. This paper describes and evaluates two query reformulation techniques at compile and at execution level.

Samples for Understanding Data-Semantics in Relations

From statistics, sampling technics were proposed and some of them were proved to be very useful in many database applications. Rather surprisingly, it seems these works never consider the preservation of data semantics. Since functional dependencies (FDs) are known to convey most of data semantics, an interesting issue would be to construct samples preserving FDs satisfied in existing relations.To cope with this issue, we propose in this paper to define Informative Armstrong Relations (IARs); a relation s is an IAR for a relation r if s is a subset of r and if FDs satisfied in s are exactly the same as FDs satisfied in r. Such a relation always exists since r is obviously an IAR for itself; moreover we shall point out that small IARs with interesting bounded sizes exist. Experiments on relations available in the KDD archive were conducted and highlight the interest of IARs to sample existing relations.