Big data applications routinely involve diverse datasets: relations flat or nested, complex-structure graphs, documents, poorly structured logs, or even text data. To handle the data, application designers usually rely on several data stores used side-by-side, each capable of handling one or a few data models, and each very efficient for some, but not all, kinds of processing on the data. A current limitation is that applications are written taking into account which part of the data is stored in which store and how. This fails to take advantage of (i) possible redundancy, when the same data may be accessible (with different performance) from distinct data stores; (ii) partial query results (in the style of materialized views) which may be available in the stores. We present ESTOCADA, a novel approach connecting applications to the potentially heterogeneous systems where their input data resides. ESTOCADA can be used in a polystore setting to transparently enable each query to benefit from the best combination of stored data and available processing capabilities. ESTOCADA leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe the various data models and stored data. Our experiments illustrate the significant performance gains achieved by ESTOCADA.

Data management goes through interesting times1, as the number of currently available data management systems (DMSs in short) is probably higher than ever before. This leads to unique opportunities for data-intensive applications, as some systems provide excellent performance on certain data processing operations. Yet, it also raises great challenges, as a system efficient on some tasks may perform poorly or not support other tasks, making it impossible to use a single DMS for a given application. It is thus desirable to use different DMSs side by side in order to take advantage of their best performance, as advocated under terms such as hybrid or poly-stores. We present ESTOCADA, a novel system capable of exploiting side-by-side a practically unbound variety of DMSs, all the while guaranteeing the soundness and completeness of the store, and striving to extract the best performance out of the various DMSs. Our system leverages recent advances in the area of query rewriting under constraints, which we use to capture the various data models and describe the fragments each DMS stores.

In the presence of an ontology, query answers must reflect not only data explicitly present in the database, but also implicit data, which holds due to the ontology, even though it is not present in the database. A large and useful set of ontology languages enjoys FOL reducibility of query answering : answering a query can be reduced to evaluating a certain first-order logic (FOL) formula (obtained from the query and ontology) against only the explicit facts. We present a novel query optimization framework for ontology-based data access settings enjoying FOL reducibility . Our framework is based on searching within a set of alternative equivalent FOL queries, i.e., FOL reformulations, one with minimal evaluation cost when evaluated through a relational database system. We apply this framework to the DL-Lite R Description Logic underpinning the W3C's OWL2 QL ontology language, and demonstrate through experiments its performance benefits when two leading SQL systems, one open-source and one commercial, are used for evaluating the FOL query reformulations.

International audience

Techniques for efficiently managing Semantic Web data have attracted significant interest from the data management and knowledge representation communities. A great deal of effort has been invested, especially in the database community, into algorithms and tools for efficient RDF query evaluation. However, the main interest of RDF lies in its blending of heterogeneous data and semantics. Simple RDF graphs can be seen as collections of facts, which may be further enriched with ontological schemas, or semantic constraints, based on which reasoning can be applied to infer new information. Taking into account this implicit information is crucial for answering queries.

Answering queries over Semantic Web data, i.e., RDF graphs, must account for both explicit data and implicit data, entailed by the explicit data and the semantic constraints holding on them. Two main query answering techniques have been devised, namely Saturation -based (S at ) which precomputes and adds to the graph all implicit information, and Reformulation -based (R ef ) which reformulates the query based on the graph constraints, so that evaluating the reformulated query directly against the explicit data (i.e., without considering the constraints) produces the query answer. While S at is well known, R ef has received less attention so far. In particular, reformulated queries often perform poorly if the query is complex. Our demonstration showcases a large set of R ef techniques, including but not limited to one we proposed recently. The audience will be able to 1: test them against different datasets, constraints and queries, as well as different well-established systems, 2: analyze and understand the performance challenges they raise, and 3: alter the scenarios to visualize the impact on performance. In particular, we show how a cost-based R ef approach allows avoiding reformulation performance pitfalls.