Dr. Rob Brennan

Summary

Data quality impacts data's fitness for use for the application at hand. There are a wide range of quality indicators that can be used to quantify quality. However, it is a challenge for consumers to identify the most appropriate quality indicators for choosing a dataset for a specific task. This problem stems from the fact that different domains require different quality indicators to demonstrate fitness for use. Whilst a number of quality assessment frameworks exist, none of these tackle the fundamental challenge of indicator selection. The novelty of this project is that it provides an intelligent recommender system that builds on top of existing quality frameworks in order to assist data consumers by automatically suggesting quality indicators for the task at hand. These quality indicators will then facilitate the task dependent appraisal of datasets on the Web of Data. The goal is to support data consumers and enhance their productivity by finding fit for use data sources for their tasks.

Funding Agency

Irish Research Council

Programme

Postdoctoral Fellowship (Government of Ireland)

Person Months

24

Summary

Investigating methods and tools to publish OSi's reference geospatial data for Ireland as Linked Data.

Funding Agency

OSi/SFI

Programme

ADAPT Spokes

Summary

Funded Collaborator in SFI research centre focused on digital data management, quality and interoperability (Theme E within the research centre).

Funding Agency

SFI

Programme

Research Centre

Project Type

Research Centre

Person Months

48

Summary

The rapid growth of web data creates demand for software engineering methods which can build and maintain applications that extract, process and publish web data. However, converting Big Data sources into high-quality, structured knowledge for use in business processes is usually considered data engineering. ALIGNED will develop models, methods and tools for engineering information systems based on co-evolving software and web data. Tools for model-driven software evolution based on Linked Data sources, runtime data quality analytics, human data curation and process integration will aid more efficient governance, increased agility and higher productivity. The opportunities for web data have recently led to intense research and innovation, but most efforts are siloed in the software or data spaces. Past integration of data and software engineering used formal ontologies rather than Linked Data. ALIGNED will provide lightweight methods for European data and software engineering industries to exploit the new opportunities in web data. Information companies like Wolters Kluwer need better ways to extract web data and build applications on top of it. Public bodies like the UK National Health Service (NHS) need evolving systems for data collection and re-use. Web data publishers like DBpedia need new methods to improve data quality. Scientific publications, industry workshops, training programs, open source tools and engaging the OMG, W3C and ISO standards bodies will transfer ALIGNED outputs.ALIGNED combines world class researchers in model driven software engineering (Oxford are transforming NHS systems), Linked Data quality (Leipzig and Trinity College have published foundational papers) and web systems (Leipzig are co-creators of DBpedia) with innovative enterprises (Wolters Kluwer have Linked Data in production systems, Semantic Web Company lead the world in enterprise Linked Data) and pioneering expert-curated data publishers (Oxford Anthropology and Posnan).

Funding Agency

European Commission

Programme

H2020

Project Type

Research and Innovation

Person Months

122

Summary

This project funds my development of the ValueDate H2020 proposal for value-driven data governance. It includes writing support, consortium meetings and buy-out of my regular research responsibilities so that I can focus on proposal development.

Funding Agency

Enterprise Ireland

Programme

H2020 coordinator support grant

Person Months

2