For centuries, sheet music scores have been the traditional way to preserve and disseminate Western music works. Nowadays, their content can be encoded in digital formats, making possible to store music score data in digital score libraries (DSL). To supply intelligent services (extracting and analysing relevant information from data), the new generation of DSL has to rely on digital representations of the score content as structured objects apt at being manipulated by high-level operators. In the present paper, we propose the Muster model, a graph-based data model for representing the music content of a digital score, and we discuss the querying of such data through graph pattern queries. We then present a proof-of-concept of this approach, which allows storing graph-based representations of music scores in the Neo4j database, and performing musical pattern searches through graph pattern queries with the Cypher query language. A benchmark study, using (real) datasets stemming from the Neuma Digital Score Library, complements this implementation.

This 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.

In a relational database context, fuzzy quantified queries have been long recognized for their ability to express different types of imprecise and flexible information needs. In this paper, we introduce the notion of fuzzy quantified statements in a (fuzzy) RDF database context. We show how these statements can be defined and implemented in FURQL, which is a fuzzy extension of the SPARQL query language that we previously proposed. Then, we present some experimental results that show the feasibility of this approach.

We examine the problem of quality awareness when querying graph databases. According to quality annotations that denote quality problems appearing in data subgraphs (the annotations typically result from collaborative practices in the context of open data usage like e.g. users' feedbacks), we propose a notion of quality aware (graph pattern) query based on (usage-dependent) quality profiles. In this paper, we present the formal foundations of the approach. We also show how to simply extend a generic state-of-the-art algorithm for graph pattern queries evaluation in order to implement quality awareness at evaluation time and we study its complexity. We then expose implementation guidelines, supported by a proof-of-concept prototype based on the Neo4J graph database management system.

The Resource Description Framework (RDF) is the graph-based standard data model for representing semantic web information, and SPARQL is the standard query language for querying RDF data. Because of the huge volume of linked open data published on the web, these standards have aroused a large interest in the last years. This paper proposes a fuzzy extension of the SPARQL language that improves its expressiveness and usability. This extension allows (1) to query a fuzzy RDF data model, and (2) to express fuzzy preferences on data and on the structure of the data graph, which has not been proposed in any previous fuzzy extensions of SPARQL.

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.

In this work, the first stones of a flexible approach to linked data querying based on fuzzy set theory are laid. Flexibility refers to the capability of expressing flexible queries over a RDF model containing gradual information.

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.

To improve the expressivity and accuracy of database query interfaces, a keyword-based constrained query language is introduced to let users explicitly express the intent of their search using keywords linked by meaningful grammatical connectives. Individually, keywords and connectives correspond to textual descriptions attached to components of the database graph schema, and as a whole, a so-called connected keywords query corresponds to a textual description of an SQL query. The translation process of such a query into SQL is mainly composed of two steps: first, the syntactic structure of the keyword query is analyzed to exhibit projection and selection statements using predefined graph patterns, then non explicit joins are deduced to obtain a complete translation of the keyword query as a meaningful connected subgraph. Experimentations show the relevance of the approach in terms of expressivity and efficiency.

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.

The demonstration is devoted to the desktop-level interactions offered by Cador, a content-based document management system currently under development. Cador provides a rule-based language to query and manipulate large collections of documents distributed in repositories. The language is able to define the content of Virtual File Systems (VFS) as views over the document collections. This feature allows users to combine their familiar interface and desktop-based softwares with the powerful search and transformation tools provided by the underlying system.

We consider the class of applications that manage time series (TS) and propose a data model and a query language that let these applications manipulate TS data sets at a logical level. We introduce the concept of synchronized time series (STS) to model the alignment of several time series in a common time space. We show how this concept helps to analyze and compare information extracted from time series, and how it constitutes a convenient tool that supports an extended set of operations. The main contribution of the paper is a formal query language that combines generic operators on TS with user-defined functions. The language can be evaluated in closed form over STS data sets and gives rise to rewriting and optimization techniques. Throughout the paper, we illustrate our approach with several examples drawn from a few representative TS applications.

We consider the class of database programs and address the problem of minimizing the cost of their exchanges with the database server. This cost partly consists of query execution at the server side, and partly of query submission and network exchanges between the program and the server. The natural organization of database programs leads to submit an intensive flow of elementary SQL queries to the server, and exploits only locally its optimization power. In this paper, we develop a global optimization approach. We base this approach on an execution model where queries can be executed asynchronously with respect to the flow of the application program. Our method aims at choosing an efficient query scheduling which limits the penalty of client/server interactions. Our results show that the technique can improve the execution time of database programs by several orders of magnitude.