On April 4, 2017 we held our third BDE webinar for Societal Challenge 1: Health. The goal of this webinar was to present the progress we’ve made on our pilot project in this area, our plans for the near future, and for the BDE community and public to ask any questions they may have about the pilot. 22 people attended this hangout.

Ronald Siebes (VU University Amsterdam) started the seminar by introducing the BDE project and the seven Horizon 2020 Societal Challenges. He gave an overview of the pilot projects in each area, the kinds of data they are working with, and the kinds of problems they aim to solve.

Next Hajira Jabeen (University of Bonn) gave a more detailed explanation of the architecture of the BDE infrastructure, and its flexibility in addressing different kinds of big data challenges. The BDE infrastructure is open source, and designed to make it simple for anyone to get started working with big data; Hajira explained how it can be set up and integrated with custom components to suit different use cases.

Nick Lynch, CTO of the Open PHACTS Foundation, then presented more detailed information about the SC1 pilot and its progress. Last year we successfully completed the first phase of this pilot, replicating the functionality of the Open PHACTS Discovery Platform on the BDE infrastructure. The Open PHACTS Discovery Platform brings together pharmacological data resources in an integrated, interoperable infrastructure, and has been developed to reduce barriers to drug discovery for industry, academia, and small businesses.

Nick also gave an overview of how individual users can set up and use the Open PHACTS Discovery Platform on their local machine or network. Our plans for the next stage of the pilot involve updating and refreshing the data integrated by the platform, and updating the API as part of a wider architecture review.

Finally, we opened up discussion to all attendees to ask any questions they might have about the platform. We talked about how the identity mapping service maintains links between different entities, and the benefits of being able to substitute different modules within the BDE infrastructure for different purposes.

You can find a recording of the webinar on YouTube, and view all the slides on Slideshare.

On 9 December 2016, the second workshop for the Big Data Europe Health, Demographic Change and Wellbeing societal challenge was held in Brussels. The aim of this workshop was to highlight progress from the BigDataEurope project in building the foundations of a generically applicable big data platform which can be applied across all Horizon 2020 societal challenges. This workshop specifically focused on health, and showcased our first pilot’s application to early bioscience research data.

The workshop had 15 participants, from within the health domain and outside it, including many participants from the European Commission. Together we discussed different perspectives on how we may use appropriate H2020 instruments and work programmes to better integrate the ecosystem of linked data repositories, data management services and virtual collaboration environments to increase the pace of knowledge sharing in health.

The workshop featured presentations from BDE’s Simon Scerri and Aad Versteden on the general goals and progress of the BigDataEurope project and the BDE infrastructure respectively. After lunch, Ronald Siebes (BDE / VU Amsterdam) presented the first pilot in this specific domain. More information on that pilot can be found here. An extensive round-table discussion followed, in which possible options for new applications and connections were considered.

One question raised was whether the generic BDE infrastructure can be used by European SMEs. The fact that the BDE infrastructure is completely Open Source, very easy to install and features intuitive interface components makes re-use relatively simple even for smaller institutions and companies.

A significant part of the discussion focussed on possible new use cases for expanding the scope of the pilot. One suggestion was to look at post-hoc integration of clinical data, which represents a typical problem of data ‘variance’. This would require integrating information from different versions of medical questionnaires, which may be recorded or stored in different ways. Data provenance is also a key concern, as keeping a trail of what has happened to clinical data is crucial to tracking patients’ histories. Once integrated, this data could then be mined to identify biases or data patterns.

Finally, the workshop participants discussed potential connections to other European projects. Here many projects were mentioned including the MIDAS project, the Big-O project on childhood obesity, the PULSE projects and IMI / IMI2 projects including EMIF. We will be seeking collaborations with these projects and will continue to develop new and interesting Big Data use cases in this domain in the coming year.

More images can be found below:

This blog post is based on a forthcoming paper by Antonis Troumpoukis, Angelos Charalambidis, Giannis Mouchakis, Stasinos Konstantopoulos, Ronald Siebes, Victor de Boer, Stian Soiland-Reyes and Daniela Digles which will be presented at  BLINK workshop at the ISWC conference.

In the Big-Data-Europe project, the technical infrastructure is based on requirements and insights from the seven societal domains. A nice example in which domain-specific insights can be used to test generic infrastructures is found in the Health domain. 

Photo: Bill Dickinson CC by-nc-nd

For measuring the performance of Semantic Web query processing platforms, query benchmarks are needed. Current benchmarks are often based on technological features of the queries and not so much on how realistic these queries. The SC1 Health pilot replicates the functionalities of the Open PHACTS drug discovery platform (https://dev.openphacts.org/) . To both develop and validate this original platform, 20 questions were gathered by pharmacological domain experts based on realistic workflows. An example question is Question 16: “Targets in Parkinson’s disease or Alzheimer’s disease are activated by which compounds?”. The effectiveness and efficiency of the platform was evaluated based on these questions.

These rather complex queries and workflows form the basis for the query set of our benchmark. As such it is a queryset that is simultaneously complex and frequently used and independently motivated. The benchmark engine and the queries are available as open source (https://github.com/semagrow/kobe)  while all datasets are also publicly available. This is work in progress and we are currently using the new benchmark to test state-of-the-art federated query processing systems.