VADA: Value Added Data Systems -- Principles and Architecture
Data is everywhere, generated by increasing numbers of applications, devices and users, with few or no guarantees
on the format, semantics, and quality. The economic potential of data-driven innovation is enormous, estimated to reach as
much as £40B in 2017, by the Centre for Economics and Business Research. To realise this potential, and to provide meaningful
data analyses, data scientists must first spend a significant portion of their time (estimated as 50% to 80%) on "data wrangling"
- the process of collection, reorganising, and cleaning data.
This heavy toll is due to what is referred as the four V's of big data: Volume - the scale of the data, Velocity - speed of change, Variety - different forms of data, and Veracity - uncertainty of data. There is an urgent need to provide data scientists with a new generation of tools that will unlock the potential of data assets and significantly reduce the data wrangling component. As many traditional tools are no longer applicable in the 4 V's environment, a radical paradigm shift is required. The proposal aims at achieving this paradigm shift by adding value to data, by handling data management tasks in an environment that is fully aware of data and user contexts, and by closely integrating key data management tasks in a way not yet attempted, but desperately needed by many innovative companies in today's data-driven economy.
The VADA research programme will define principles and solutions for Value Added Data Systems, which support users in discovering, extracting, integrating, accessing and interpreting the data of relevance to their questions. In so doing, it uses the context of the user, e.g. requirements in terms of the trade-off between completeness and correctness, and the data context, e.g., its availability, cost, provenance and quality. The user context characterises not only what data is relevant, but also the properties it must exhibit to be fit for purpose. Adding value to data then involves the best efort provision of data to users, along with comprehensive information on the quality and origin of the data provided. Users can provide feedback on the results obtained, enabling changes to all data management tasks, and thus a continuous improvement in the user experience.
Establishing the principles behind Value Added Data Systems requires a revolutionary approach to data management, informed by interlinked research in data extraction, data integration, data quality, provenance, query answering, and reasoning. This will enable each of these areas to benefit from synergies with the others. Research has developed focused results within such sub-disciplines; VADA develops these specialisms in ways that both transform the techniques within the sub-disciplines and enable the development of architectures that bring them together to add value to data.
The commercial importance of the research area has been widely recognised. The VADA programme brings together university researchers with commercial partners who are in desperate need of a new generation of data management tools. They will be contributing to the programme by funding research staff and students, providing substantial amounts of staff time for research collaborations, supporting internships, hosting visitors, contributing challenging real-life case studies, sharing experiences, and participating in technical meetings. These partners are both developers of data management technologies (LogicBlox, Microsoft, Neo) and data user organisations in healthcare (The Christie), e-commerce (LambdaTek, PricePanda), finance (AllianceBernstein), social networks (Facebook), security (Horus), smart cities (FutureEverything), and telecommunications (Huawei).
EPSRC funding link: http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/M025268/1
Many Facets of Reasoning Under Uncertainty‚ Inconsistency‚ Vagueness‚ and Preferences: A Brief Survey
Gabriele Kern−Isberner and Thomas Lukasiewicz
In Künstliche Intelligenz. 2017.
Location−Aware Personalized News Recommendation with Deep Semantic Analysis
Cheng Chen‚ Xiangwu Meng‚ Zhenghua Xu and Thomas Lukasiewicz.
In IEEE Access. 2017.
Tag−Aware Personalized Recommendation Using a Deep−Semantic Similarity Model with Negative Sampling
Zhenghua Xu‚ Cheng Chen‚ Thomas Lukasiewicz‚ Yishu Miao and Xiangwu Meng
In Elisa Bertino‚ Fabio Crestani‚ Javed Mostafa‚ Jie Tang‚ Luo Si and Xiaofang Zhou, editors, Proceedings of the 25th ACM International Conference on Information and Knowledge Management‚ CIKM 2016‚ Indianapolis‚ USA‚ October 24−28‚ 2016. Pages 1921−1924. ACM Press. October, 2016.