Efficient and scalable techniques for minimization and rewriting of conjunctive queries

Query rewriting as an approach to query answering has been a challenging issue in database and information integration systems. In general, rewriting of a conjunctive query Q using a set of views in conjunctive form consists of two phases: (1) generating proper building blocks using the views, and (2) combining them to generate a union of conjunctive queries which is maximally contained in Q . While the problem of query rewriting is known to be exponential in the number of subgoals of Q , there is a demand for increased efficiency for practical queries. We revisit this problem for conjunctive queries, and show that Stirling numbers can be used to determine the optimal number of combinations in the second phase, and hence the number of rules in the generated union of conjunctive queries. Based on these numbers, we introduce the notion of combination patterns and develop a rewriting algorithm that uses these numeral patterns to break down the large combinatorial problem in the second phase into several smaller ones. The results of our numerous experiments indicate that the proposed rewriting technique outperforms existing techniques including Minicon algorithm in terms of computation time, memory requirements, and scalability. On a related context, we studied query minimization, motivated by the fact that queries with fewer or no redundant subgoals can be evaluated faster, in general. However, such redundancies are often present in automatically generated queries. We propose an algorithm that, given a conjunctive query, repeatedly identifies and eliminates all the redundant subgoals. We also illustrate its performance superiority over existing minimization algorithms. It has been shown that query rewriting naturally generates queries with redundant subgoals. We also show that redundant subgoals in the input of query rewriting result in redundant rules in its output. Based on this, we investigate the impact of minimization as pre-processing and post-processing phases to query rewriting technique. Our experimental results using different synthetic data show that our query rewriting technique coupled with pre/post minimization phases produces the best quality of rewriting in a more efficient way compared to existing rewriting techniques, including the Treewise algorithm. It has been shown that extending conjunctive queries with constraints adds to the complexity of query rewriting. Previous studies identified classes of conjunctive queries with constraints in the form of arithmetic comparisons for which the complexity of rewriting does not change. Such classes are said to satisfy homomorphism property. We identify new classes of conjunctive queries with linear arithmetic constraints that enjoy this property, and extend our query rewriting algorithm accordingly to support such queries

Using Ontologies for Semantic Data Integration

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed

View Selection in Semantic Web Databases

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.Comment: VLDB201

Ontology-based data access with databases: a short course

Ontology-based data access (OBDA) is regarded as a key ingredient of the new generation of information systems. In the OBDA paradigm, an ontology defines a high-level global schema of (already existing) data sources and provides a vocabulary for user queries. An OBDA system rewrites such queries and ontologies into the vocabulary of the data sources and then delegates the actual query evaluation to a suitable query answering system such as a relational database management system or a datalog engine. In this chapter, we mainly focus on OBDA with the ontology language OWL 2QL, one of the three profiles of the W3C standard Web Ontology Language OWL 2, and relational databases, although other possible languages will also be discussed. We consider different types of conjunctive query rewriting and their succinctness, different architectures of OBDA systems, and give an overview of the OBDA system Ontop

The combined approach to ontology-based data access

The use of ontologies for accessing data is one of the most exciting new applications of description logics in databases and other information systems. A realistic way of realising sufficiently scalable ontology- based data access in practice is by reduction to querying relational databases. In this paper, we describe the combined approach, which incorporates the information given by the ontology into the data and employs query rewriting to eliminate spurious answers. We illustrate this approach for ontologies given in the DL-Lite family of description logics and briefly discuss the results obtained for the EL family