Profiling relational data: a survey

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases

Contributions à l’Optimisation de Requêtes Multidimensionnelles

Analyser les données consiste à choisir un sous-ensemble des dimensions qui les décriventafin d'en extraire des informations utiles. Or, il est rare que l'on connaisse a priori les dimensions"intéressantes". L'analyse se transforme alors en une activité exploratoire où chaque passe traduit par une requête. Ainsi, il devient primordiale de proposer des solutions d'optimisationde requêtes qui ont une vision globale du processus plutôt que de chercher à optimiser chaque requêteindépendamment les unes des autres. Nous présentons nos contributions dans le cadre de cette approcheexploratoire en nous focalisant sur trois types de requêtes: (i) le calcul de bordures,(ii) les requêtes dites OLAP (On Line Analytical Processing) dans les cubes de données et (iii) les requêtesde préférence type skyline

Discovering XSD keys from XML data

A great deal of research into the learning of schemas from XML data has been conducted in recent years to enable the automatic discovery of XML Schemas from XML documents when no schema, or only a low-quality one is available. Unfortunately, and in strong contrast to, for instance, the relational model, the automatic discovery of even the simplest of XML constraints, namely XML keys, has been left largely unexplored in this context. A major obstacle here is the unavailability of a theory on reasoning about XML keys in the presence of XML schemas, which is needed to validate the quality of candidate keys. The present paper embarks on a fundamental study of such a theory and classifies the complexity of several crucial properties concerning XML keys in the presence of an XSD, like, for instance, testing for consistency, boundedness, satisfiability, universality, and equivalence. Of independent interest, novel results are obtained related to cardinality estimation of XPath result sets. A mining algorithm is then developed within the framework of levelwise search. The algorithm leverages known discovery algorithms for functional dependencies in the relational model, but incorporates the above mentioned properties to assess and refine the quality of derived keys. An experimental study on an extensive body of real world XML data evaluating the effectiveness of the proposed algorithm is provided.info:eu-repo/semantics/publishe