Query formulation interfaces over semantically enhanced Big Data.

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Abstract

Enabling end-users access to industrial Big Data is a challenging and important problem in many large companies. A large-scale EU funded project Optique (www.optique-projects-page) addresses this problem with Semantic Web technologies and aims at developing a system for Big Data access deployed at Statoil and Siemens. In Oxford (www.oxf-page-for-optique) we contribute in both research and development for several components of this system. In particular, we develop query formulation interfaces (QFIs) powered with Semantic Web technologies that end-users, such as geologists or energy engineers, can use to access Big Data. This student’s project aims at development of novel solutions for QFIs over Semantically enhanced Big Data: development (and adaptation) of techniques for ranking query answers and components of visual query formulation interfaces; development of techniques to support visual formulation of aggregate queries; implementation and experiments with real and synthetic data.

Preferable knowledge: Java, Databases, Semantic Web technologies.