Grégory Smits

On a Fuzzy Algebra for Querying Graph Databases

This paper proposes a notion of fuzzy graph database and describes a fuzzy query algebra that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. The algebra, based on fuzzy set theory and the concept of a fuzzy graph, is composed of a set of operators that 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. In a similar way as relational algebra constitutes the basis of SQL, the fuzzy algebra proposed here underlies a user-oriented query language and an associated tool implementing this language that are also presented in the paper.

ReqFlex: fuzzy queries for everyone

In this demonstration we present a complete fuzzy-set-based approach to preference queries that tackles the two main questions raised by the introduction of flexibility and personalization when querying relational databases: i) how to efficiently execute preference queries? and, ii) how to help users define preferences and queries? As an answer to the first question, we propose PostgreSQL_f, a module implemented on top of PostgreSQL to handle fuzzy queries. To answer the second question, we propose ReqFlex an intuitive user interface to the definition of preferences and the construction of fuzzy queries.

Expression and efficient processing of fuzzy queries in a graph database context

Graph 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.

A Model Based on Outranking for Database Preference Queries

In this paper, we describe an approach to database preference queries based on the notion of outranking, suited to the case where partial preferences are incommensurable. This model constitutes an alternative to the use of Pareto order. Even though outranking does not define an order in the strict sense of the term, we describe a technique which yields a complete pre-order, based on a global aggregation of the outranking degrees computed for each pair of tuples.

Efficient detection of minimal failing subqueries in a fuzzy querying context

This paper deals with conjunctive fuzzy queries that yield an empty or unsatisfactory answer set. We propose a cooperative answering approach which efficiently retrieves the minimal failing subqueries of the initial query (which can then be used to explain the failure). The detection of the minimal failing subqueries relies on a prior step of fuzzy cardinalities computation. The main advantage of this strategy is to imply a single scan of the database. Moreover, the storage of such knowledge about the data distributions easily fits in memory.

A soft computing approach to agile business intelligence

In this paper, a novel approach is introduced to let users extract knowledge from a raw dataset in an intuitive way and using their own vocabulary. The inner structure of a raw data set is first identified using a clustering algorithm, structure on which specificity-driven measures are defined to extract the most informative knowledge. To let domain experts interact with the cluster-based structure and its embedded knowledge, a graphical visualisation is proposed as well as dedicated query operators.

Linguistic and Graphical Explanation of a Cluster-Based Data Structure

On the one hand, clustering methods are of a particular interest to automatically identify the inner structure of a data set. On the other hand, fuzzy partitions are particularly suitable to define a subjective and domain dependent vocabulary that may then be used to personalize an information system. To make the translation of raw data into knowledge easier, we propose in this paper to generate personalized linguistic and graphical explanations of a cluster-based data structure.

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.

A database preference query model based on a fuzzy outranking relation

In this paper, we describe an approach to database preference queries based on the notion of fuzzy outranking, suited to the case where partial preferences are incommensurable. This model constitutes an alternative to the use of Pareto order. Even though outranking does not define an order in the strict sense of the term, we describe a technique which yields a complete pre-order, based on a global aggregation of the outranking degrees computed for each pair of tuples.

SUGAR: A graph database fuzzy querying system

Graph databases have aroused a large interest in the last years thanks to their large scope of potential applications. Defining a language allowing a flexible querying of graph databases may greatly improve usability of data. In this paper, we present a system for querying graph databases in a flexible way. The preferences are based on fuzzy set theory and may concern i) the content of the vertices and ii) the structure of the graph.