Combining Rewriting and Incremental Materialisation Maintenance for Datalog Programs with Equality

Materialisation precomputes all consequences of a set of facts and a datalog program so that queries can be evaluated directly (i.e., independently from the program). Rewriting optimises materialisation for datalog programs with equality by replacing all equal constants with a single representative; and incremental maintenance algorithms can efficiently update a materialisation for small changes in the input facts. Both techniques are critical to practical applicability of datalog systems; however, we are unaware of an approach that combines rewriting and incremental maintenance. In this paper we present the first such combination, and we show empirically that it can speed up updates by several orders of magnitude compared to using either rewriting or incremental maintenance in isolation.Comment: All proofs contained in the appendix. 7 pages + 4 pages appendix. 7 algorithms and one table with evaluation result

Ontology-Based RDF Integration of Heterogeneous Data

The proliferation of heterogeneous data sources in many application contexts brings an urgent need for expressive and efficient data integration mechanisms. There are strong advantages to using RDF graphs as the integration format: being schemaless, they allow for flexible integration of data from all sources; RDF graphs can be interpreted with the help of an ontology, describing application semantics; last but not least, RDF enables joint querying of the data and the ontology. To address this need, we introduce the novel class of RDF Integration Systems (RIS), going beyond the state of the art in the expressive power, that is, in the ability to expose, integrate and flexibly query data from heterogeneous sources through GLAV (global-local-as-view) mappings. Our second contribution is a set of query answering strategies, two combining existing techniques and three others based on an innovative integration of view-based rewriting; our experiments show that the latter bring strong performance advantages

ExpRalytics: analyse expressive et efficace de graphes RDF

Large (Linked) Open Data are increasingly shared as RDF graphs today. However, such data does not yet reach its full potential in terms of sharing and reuse. We provide new methods to meaningfully summarize data graphs, with a particular focus on RDF graphs. One class of tools for this task are structural RDF graph summaries, which allow users to grasp the different connections between RDF graph nodes. To this end, we introduce our novel RDFQuotient tool that finds compact yet informative RDF graph summaries that can serve as first-sight visualizations of an RDF graph’s structure. We also consider the problem of automatically identifying the k most interesting aggregate queries that can be evaluated on an RDF graph, given an integer k and a user-specified interestingness function. Aggregate queries are routinely used to learn insights from relational data warehouses, and some prior research has addressed the problem of automatically recommending interesting aggregate queries.Les données ouvertes sont souvent partagées sous la forme de graphes RDF, qui sont une incarnation du principe Linked Open Data (données ouvertes liées). De telles données n’ont toutefois pas atteint leur entier potentiel d’utilisation et de partage. L’obstacle pour ce faire réside principalement au niveau de la capacité des utilisateurs à explorer, découvrir et saisir le contenu et des graphes RDF; cette tâche est complexe car les graphes sont naturellement hétérogènes, et peuvent être à la fois volumineux et complexes. Nous proposons de nouvelles méthodes pour résumer de grands graphes de données, avec un accent particulier sur les graphes RDF. A cette fin, nous avons proposé une nouvelle approché pour la construction de résumés structurels de graphes RDF, à savoir RDFQuotient.Nous considérons aussi le problème d’identifier automatiquement les requêtes d’agrégation les plus intéressantes qui peuvent être évaluées sur un graphe RDF

Efficient Query Answering against Dynamic RDF Databases

A promising method for efficiently querying RDF data consists of translating SPARQL queries into efficient RDBMS-style operations. However, answering SPARQL queries requires handling RDF reasoning, which must be implemented outside the relational engines that do not support it. We introduce the database (DB) fragment of RDF, going beyond the expressive power of previously studied RDF fragments. We devise novel sound and complete techniques for answering Basic Graph Pattern (BGP) queries within the DB fragment of RDF, exploring the two established approaches for handling RDF semantics, namely reformulation and saturation. In particular, we focus on handling database updates within each approach and propose a method for incrementally maintaining the saturation; updates raise specific difficulties due to the rich RDF semantics. Our techniques are designed to be deployed on top of any RDBMS(-style) engine, and we experimentally study their performance trade-offs