Data Science

TNO’s Data Science department has expertise on how to share and analyse structured and unstructured data. In a world where people and machines are becoming increasingly interconnected, data – both complex data and big data – is becoming increasingly important. Our primary mission is to understand and give meaning to this data, using an explainable, collaborative and responsible approach. We help companies and governments to share and manage their data, and to make better decisions –both from a social and a business-economics perspective.

understanding and giving meaning to data

TData is playing an increasingly important part in our society and in our economy. People and the devices all around us are generating more and more data (e.g. text, video, or images). The amount of data sources is growing, thanks to the increasing number of sensors, smartphones, smart meters, and wearables, commonly known as the Internet of Things (IoT). It is becoming increasingly important to handle this data properly – which data can be shared, how should it be shared, and how can you derive meaning from it?  This growth in data sources is reflected by an increased demand for responsible and explainable data analysis, for data standards, for data infrastructures required to exchange and analyse data, and for better data governance. More data also means that data security will become an extremely important issue for society, in addition to the impact on policy and regulations.

 

The Data Science department covers three knowledge areas that include various aspects of data sharing and data analysis. These knowledge areas are ‘Explainable, Responsible and Interoperable Data Science’. The key focus for each of these areas is understanding and giving meaning to data using an explainable, collaborative and responsible approach.

Explainable data science

This knowledge area focuses on making data analysis - mainly based on artificial intelligence - more transparent. The outcome of the data analysis is intended to support decision-making processes in operational situations. Therefore, it is important that the outcome of the data analysis is explainable. We are developing automated reasoning technologies that can derive relations between data and explain analysis results in terms that are comprehensible to human decision makers. We attempt to develop forms of artificial intelligence capable of communicating with people in a natural and easily accessible way, using text mining and natural language, for example.  In addition, we are developing methods to automatically determine the validity and reliability of data-driven decision-support systems. The most important areas of application currently being explored by TNO involve the Health domain, and the Security, Safety and Defence domain. For instance, we developed a Dark web Monitor that can analyse trends on Dark web market places selling illegal trade like drugs and weapons.

Responsible data science

Nowadays, it is becoming more important to perform data and trend analysis in a responsible way. A large part of data is derived from individuals, or it might contains company confidential information. We develop solutions based on privacy-friendly designs. Furthermore, we develop data analysis methods that use various types of encryption. In this way, the data can be analysed without revealing the content of the sensitive data. To ensure that the analysis is transparent for the party that owns or uses the data, we give them control over the privacy settings. Our work involves cooperation with various strategic partners (e.g. LIACS) and encryption experts (TNO-CSR, CWI), as well as with European research consortia. This knowledge area focuses on the domains of Security and Health. In the Heath domain, we enable data analyses in multi-stakeholder settings, by sharing the outcome of the data analysis, but without revealing the content of the data. In the Safety- and Security domain we develop methods to perform responsible data analysis for Law Enforcement Agencies.

Interoperable data science

This knowledge area focuses on data exchange in a structured and meaningful way, both within and between organizations and devices. This knowledge area creates solutions that are designed to share electronic information in an efficient and managed way. These solutions facilitate the meaningful exchange of information between industrial and commercial sectors, and across domains. In a dynamic world, TNO uses this approach to assist companies, governments and individuals by enabling them to engage in new forms of cooperation. The Data Science department specializes in the following areas:

  • Semantics: domain modelling and ontologies, standardization of vocabularies and data models
  • Interoperability architectures: linked and open data, federated platforms, blockchain technology
  • Digital ecosystem governance: data governance, management of open standards and open data

The most important areas of application currently being explored by this knowledge area are logistics, agrifood, smart industry, government, Internet of Things and business services.

Interested in data science?

Please get in touch with us if you want a better understanding of your data, or if you are interested in starting a dialogue with TNO about how to organize your information exchanges in a managed and meaningful way.

Our work

Standardisation of semantics

In today's network economy, organisations work closely together in 'chains', and information exchange takes place on a vast scale. Interoperability makes cooperation between organisations more effective... Read more
News

Wessel Kraaij of TNO recognized as Distinguished Member of the ACM for outstanding contributions to computing

10 November 2017
On November 8, the Association for Computing Machinery (ACM) named TNO scientist Wessel Kraaij as one of the 2017 Distinguished Members. ACM Distinguished Members have made a significant contribution to... Read more
News

INTERPOL training on Darknet and cryptocurrencies targets organized crime

22 August 2016
Identifying criminals and organized crime networks within the Darknet and their modus operandi to avoid law enforcement detection was the focus of a five-day specialized training course hosted at the INTERPOL... Read more
27September

Big Data meets Applied AI-congress 2018

TNO Den Haag
On Thursday 27th of September TNO organizes the Big data meets Applied AI! congress. Big Data is becoming more mature, the focus of 'typical' Big Data topics such as storage and interoperability is shifting... Read more
29October

Workshop: Handling uncertainties in big data (HUBD)

Amsterdam | Mövenpick Hotel Amsterdam City Centre
Workshop to create insights in the relations between all sources of uncertainty in big data processing pipelines, to create a more comprehensive approach that enables business to deploy reliable, accurate... Read more
Expertise groups

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