Datalog Query Languages

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https://hdl.handle.net/11705/PROLE/2019/DQL
Artículos en la categoría Datalog Query Languages publicados en las Actas de las XIX Jornadas de Programación y Lenguajes (PROLE 2019).
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  • Miniatura
    Artículo
    Experiencing Intuitionistic Logic Programming in SQL Puzzles
    Sáenz Pérez, Fernando. Actas de las XIX Jornadas de Programación y Lenguajes (PROLE 2019), 2019-09-02.
    This work presents some SQL puzzles making use of the application of intuitionistic logic programming (ILP) to implement an SQL system. ILP provides a way of declaring SQL Common Table Expressions as used to specify recursive queries and local view definitions. We present the concepts of ILP that will be used to translate SQL queries to Hypothetical Datalog, providing its syntax, an inference system and translation rules. Then, several novel SQL puzzles used during teaching standard SQL in a database subject are proposed, showing that when following the SQL standard the implemented system is more expressive than current DMBS's.
  • Miniatura
    Artículo
    Ontology and Constraint Reasoning Based Analysis of SPARQL Queries
    Almendros-Jiménez, Jesus M.; Becerra Terón, Antonio. Actas de las XIX Jornadas de Programación y Lenguajes (PROLE 2019), 2019-09-02.
    The discovery and diagnosis of wrong queries in database query languages have gained more attention in recent years. While for imperative languages well-known and mature debugging tools exist, the case of database query languages has traditionally attracted less attention. SPARQL is a database query language proposed for the retrieval of information in Semantic Web resources. RDF and OWL are standardized formats for representing Semantic Web information, and SPARQL acts on RDF/OWL resources allowing to retrieve answers of user’s queries. In spite of the SPARQL apparent simplicity, the number of mistakes a user can make in queries can be high and their detection, localization, and correction can be difficult to carry out. Wrong queries have as consequence most of the times empty answers, but also wrong and missing (expected but not found) answers. In this paper we present two ontology and constraint reasoning based methods for the discovery and diagnosis of wrong queries in SPARQL. The first method is used for detecting wrongly typed and inconsistent queries. The second method is used for detecting mismatching between user intention and queries, reporting incomplete, faulty queries as well as counterexamples. We formally define the above concepts and a batch of examples to illustrate the methods is shown.