A Distance-Based Decision in the Credal Level

Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed in [4]. Second, we give experiments showing that our rule is able to decide on a set of hypotheses. Some experiments are handled on a set of mass functions generated randomly, others on real databases

Expression and Efficient Processing of Fuzzy Queries in a Graph Database Context

International audienceGraph databases have aroused a large interest in the last years thanks to their large scope of potential applications (e.g. social networks, biomedical networks, data stemming from the web). In a similar way as what has already been proposed in relational databases, defining a language allowing a flexible querying of graph databases may greatly improve usability of data. This paper focuses on the notion of fuzzy graph database and describes a fuzzy query language that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. This language, called FUDGE, can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. The FUDGE language is implemented in a system, called SUGAR, that we present in this article. We also discuss implementation issues of the FUDGE language in SUGAR

Expression and Efficient Processing of Fuzzy Queries in a Graph Database Context

International audienceGraph databases have aroused a large interest in the last years thanks to their large scope of potential applications (e.g. social networks, biomedical networks, data stemming from the web). In a similar way as what has already been proposed in relational databases, defining a language allowing a flexible querying of graph databases may greatly improve usability of data. This paper focuses on the notion of fuzzy graph database and describes a fuzzy query language that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. This language, called FUDGE, can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. The FUDGE language is implemented in a system, called SUGAR, that we present in this article. We also discuss implementation issues of the FUDGE language in SUGAR

On Dissimilarity Measures at the Fuzzy Partition Level

International audienceOn the one hand, a user vocabulary is often used by soft-computing-based approaches to generate a linguistic and subjective description of numerical and categorical data. On the other hand, knowledge extraction strategies (as e.g. association rules discovery or clustering) may be applied to help the user understand the inner structure of the data. To apply knowledge extraction techniques on subjective and linguistic rewritings of the data, one first has to address the question of defining a dedicated distance metric. Many knowledge extraction techniques indeed rely on the use of a distance metric, whose properties have a strong impact on the relevance of the extracted knowledge. In this paper , we propose a measure that computes the dissimilarity between two items rewritten according to a user vocabulary

Fuzzy Query By Example

International audienceThis paper describes Fuzzy Query By Example, an approach helping users retrieve data without any prior knowledge of the database schema or any formal querying language. The user is solicited to evaluate, in a binary way, pre-selected items of the database. We provide a characterization-based strategy that identifies the properties shared by the examples (resp. counterexamples) positively (resp. negatively) evaluated by the user. These properties are expressed using linguistic terms from a fuzzy vocabulary to ensure that the user has a good understanding of the inferred query

Processing Fuzzy Relational Queries Using Fuzzy Views

International audienceThis paper proposes two original approaches to the processing of fuzzy queries in a relational database context. The general idea is to use views, either materialized or not. In the first case, materialized views are used to store the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime by means of user functions embedded in the query (which induces an important overhead). In the second case, abstract views are used to efficiently access the tuples that belong to the α-cut of the query result, by means of a derived Boolean selection condition

A distance-based decision in the credal level

Abstract. Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed i

PANDA: Human-in-the-Loop Anomaly Detection and Explanation

International audienceThe paper addresses the tasks of anomaly detection and explanation simultaneously, in the human-in-the-loop paradigm integrating the end-user expertise: it first proposes to exploit two complementary data representations to identify anomalies, namely the description induced by the raw features and the description induced by a user-defined vocabulary. These representations respectively lead to identify so-called data-driven and knowledge-driven anomalies. The paper then proposes to confront these two sets of instances so as to improve the detection step and to dispose of tools towards anomaly explanations. It distinguishes and discusses three cases, underlining how the two description spaces can benefit from one another, in terms of accuracy and interpretability

Exploration de données massives à l'aide d'estimations de cardinalités

National audienceThis paper describes FuzViz, a tool to explore interactively massive relational data. FuzViz relies on a method building automatically linguistic summaries, that provide concise and intelligible insights in the data content. It offers an interactive view of these summaries, dynamically recomputed on demand. To ensure a fluid exploration of the data, FuzViz exploits the proposition of a highly efficient method for estimating the cardinality ofthe summary properties, estimated from statistics about the data distribution stored in the relational data base, consolidated by a sampling-based approach. The proposed workflow also involves a vocabulary inference mechanism from these statistics.Cet article présente un outil d'exploration interactive de données massives stockées dans un système de gestion de base de données (SGBD), nommé FuzViz. Il repose sur une méthode de construction automatique de résumés linguistiques, qui fournissent une synthèse concise et intelligible du contenu des données. Il offre une vue interactive de ces résumés recalculée dynamiquement selon les demandes de l'utilisateur. Pour assurer une exploration fluide des propriétés décrites par ces résumés, FuzViz s'appuie sur la proposition d'une méthode efficace d'estimations de leurs cardinalités, produites à partir des statistiques sur la distribution des données gérées par le SGBD et consolidées par une approche basée sur un échantillonnage. L'outil propose de plus un mécanisme d'inférence de vocabulaire flou à partir de ces statistiques

PANDA : Personnaliser les ANomalies Détectées par Apprentissage

International audienceThe paper addresses the tasks of anomaly detection and explanation simultaneously, in the human-in-the-loop paradigm integrating the end-user expertise : it first proposes to exploit two complementary data representations to identify anomalies, namely the description induced by the raw features and the description induced by a userdefined vocabulary. These representations respectively lead to identify so-called data-driven and knowledge driven anomalies. The paper then proposes to confront these two sets of instances so as to improve the detection step and to dispose of tools towards anomaly explanations. It distinguishes and discusses three cases, underlining how the two description spaces can benefit from oneanother, in terms of accuracy and interpretability.La personnalisation par prise en compte de l'expertise de l'utilisateur des processus de détection d'anomalies est le sujet principal de cet article. Les anomalies sont identifiées dans deux espaces de représentation des données, l'espace initial composé des valeurs brutes et l'espace induit par un vocabulaire flou défini par l'utilisateur sur ces attributs initiaux. L'application de toute méthode de détection d'anomalies sur ces deux espaces conduit à différencier des anomalies issues des données brutes de celles issues du vocabulaire. Afin à la fois d'améliorer la détection des exceptions et de disposer d'outils pour les expliquer, ces deux ensembles d'anomalies sont confrontés. Trois situations émanant de cette comparaison sont étudiées afin de montrer comment l'utilisation combinée des deux espaces améliore l'efficacité de la procédure de détection d'anomalies ainsi que l'interprétabilité des résultats qu'elle génère