Arild Waaler

Optique: Zooming in on Big Data

Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.

Optique: Towards OBDA Systems for Industry

The recently started EU FP7-funded project Optique will develop an end-to-end OBDA system providing scalable end-user access to industrial Big Data stores. This paper presents an initial architectural specification for the Optique system along with the individual system components.

Tableaux for Intuitionistic Logics

Despite the fact that for many years intuitionistic logic has served its function primarily in relation to foundational questions in mathematics, there has been a significant revival of interest over the last couple of decades stimulated by the application of intuitionistic formalisms in computer science (1982) . It is beyond the scope of this chapter to comment on these applications in detail which, broadly speaking, either exploit formalisations of the intuitionistic meaning of general mathematical abstractions as programming logics [Martin-Lof, 1984; Martin-Lof, 1996; Constable et al., 1986] , or exploit the similarity of systems of formal intuitionistic proofs under cut-elimination to systems of typed lambda terms under various forms of reduction (e.g. [Howard, 1980; Girard, 1989; Coquand, 1990]) .1 Both types of application rely on the rich proof theory possessed by intuitionistic formalisms in comparison with their classical counterparts.2

Optique: OBDA Solution for Big Data

Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the volume, variety, and velocity dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30-70% of their time gathering and assessing the quality of data [1]. The Optique project ( http://www.optique-project.eu/ ) advocates a next generation of the well known Ontology-Based Data Access (OBDA) approach to address the Big Data dimensions and in particular the data access problem. The project aims at solutions that reduce the cost of data access dramatically.

A Free Variable Sequent Calculus with Uniform Variable Splitting

A system with variable splitting is introduced for a sequent calculus with free variables and run-time Skolemization. Derivations in the system are invariant under permutation, so that the order in which rules are applied has no effect on the leaves. Technically this is achieved by means of a simple indexing system for formulae, variables and Skolem functions. Moreover, the way in which variables are split enables us to restrict the term universe branchwise.

Engineering ontology-based access to real-world data sources

The preparation of existing real-world datasets for publication as high-quality semantic web data is a complex task that requires the concerted execution of a variety of processing steps using a range of different tools. Faced with both changing input data and evolving requirements on the produced output, we face a significant engineering task for schema and data transformation. We argue that to achieve a robust and flexible transformation process, a high-level declarative description is needed, that can be used to drive the entire tool chain. We have implemented this idea for the deployment of ontology-based data access (OBDA) solutions, where semantically annotated views that integrate multiple data sources on different formats are created, based on an ontology and a collection of mappings. Furthermore, we exemplify our approach and show how a single declarative description helps to orchestrate a complete tool chain, beginning with the download of datasets, and through to the installation of the datasets for a variety of tool applications, including data and query transformation processes and reasoning services. Our case study is based on several publicly available tabular and relational datasets concerning the operations of the petroleum industry in Norway. We include a discussion of the relative performance of the used tools on our case study, and an overview of lessons learnt for practical deployment of OBDA on real-world datasets.

Using Semantic Technology to Tame the Data Variety Challenge

Data analytics can be problematic in real-world settings, where data sources are often distributed, heterogeneous, and dynamic. Semantic technologies can now offer a practical solution that addresses scalability and usability issues, and has been successfully deployed in industry applications.

Liberalized Variable Splitting

Variable splitting is a technique applicable to free variable tableaux, sequent calculi, and matrix characterizations that exploits a relationship between β- and γ-rules. Using contextual information to differentiate between occurrences of the same free variable in different branches, the technique admits conditions under which these occurrences may safely be assigned different values by substitutions. This article investigates a system of variable splitting and shows its consistency by a semantical argument. The splitting system is liberalized with respect to β-inferences analogously to a well-known liberalization of δ-rules, and this is used to show an exponential speedup compared to free variable systems without splitting.

Relative trustworthiness

We present a method for trust scenarios with more than one trustee, where sets of trustees are ordered in a relation of relative trustworthiness. We show how a priority structure implicit in a trust relation can be made fully explicit by means of a lattice and how a system of default expectations arises from a systematic interpretation. The default structure lends itself to formal interpretation, but is independent of a particular logical language. The theory is designed to directly extend the analysis of the concept of trust given by Andrew Jones.

Enabling semantic access to static and streaming distributed data with optique: demo

Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work, we show how Semantic Technologies implemented in our system Optique can simplify such complex diagnostics by providing an abstraction layer---ontology---that integrates heterogeneous data. In a nutshell, Optique allows complex diagnostic tasks to be expressed with just a few high-level semantic queries, which can be easily formulated with our visual query formulation system. Optique can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment.