Abstract. In this work we present the Partner Units Problem as a novel challenge for optimization methods. It captures a certain type of configuration problem that frequently occurs in industry. Unfortunately, it can be shown that in the most general case an optimization version of the problem is intractable. We present and evaluate encodings of the problem in the frameworks of answer set programming, propositional satisfiability testing, constraint solving, and integer programming. We also show how to adapt these encodings to a class of problem instances that we have recently shown to be tractable.

Abstract. Ontology Based Data Access (OBDA) is a prominent approach to query databases which uses an ontology to expose data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying data. The ontology is 'connected' to the data via mappings that allow to automatically translate queries posed over the ontology into data-level queries that can be executed by the underlying database management system. Despite a lot of attention from the research community, there are still few instances of real world industrial use of OBDA systems. In this work we present data access challenges in the data-intensive petroleum company Statoil and our experience in addressing these challenges with OBDA technology. In particular, we have developed a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion, and a query processing module to perform and optimise the process of translating ontological queries into data queries and their execution. Our modules have been successfully deployed and evaluated for an OBDA solution in Statoil.

Ontologies have recently became a popular mechanism to expose relational database (RDBs) due to their ability to describe the domain of data in terms of classes and properties that are clear to domain experts. Ontological terms are related to the schema of the underlying databases with the help of mappings, i.e., declarative specifications associating SQL queries to ontological terms. Developing appropriate ontologies and mappings for given RDBs is a challenging and time consuming task. In this work we present BOOTOX, a system that aims at facilitating ontology and mapping development by their automatic extraction (i.e., bootstrapping) from RDBs, and our experience with the use of BOOTOX in industrial and research contexts. BOOTOX has a number of advantages: it allows to control the OWL 2 profile of the output ontologies, bootstrap complex and provenance mappings, which are beyond the W3C direct mapping specification. Moreover, BOOTOX allows to import pre-existing ontologies via alignment. Well(id, name NN , type) Field(id, name, status CK , intersects field FK) Operator(id, name NN) Operator Field(operator FK , field FK) Wellbore(id, name UQ , content CK , depth CK , well FK , location FK) ExplorationWellbore(wellbore FK , seismic location) DevelopmentWellbore(wellbore FK , production facility)

In this paper we study mapping analysis in ontology-based data access (OBDA), providing an initial set of foundational results for this problem. We start by defining general, language-independent notions of mapping inconsistency, mapping subsumption, and mapping redundancy in OBDA. Then, we focus on specific mapping languages for OBDA and illustrate techniques for verifying the above properties of mappings

Ontology-based data access (OBDA) is a recent paradigm for accessing data sources through an ontology that acts as a conceptual, integrated view of the data, and declarative mappings that connect the ontology to the data sources. We study the formal analysis of mappings in OBDA. Specifically, we focus on the problem of identifying mapping inconsistency and redundancy, two of the most important anomalies for mappings in OBDA. We consider a wide range of ontology languages that comprises OWL 2 and all its profiles, and examine mapping languages of different expressiveness over relational databases. We provide algorithms and establish tight complexity bounds for the decision problems associated with mapping inconsistency and redundancy. Our results prove that, in our general framework, such forms of mapping analysis enjoy nice computational properties, in the sense that they are not harder than standard reasoning tasks over the ontology or over the relational database schema.