In healthcare, manufacturing, or pretty much anywhere that large volumes of data arise - big data tools are being used for analysis and management purposes. However, the complexity of data analysis always presents the risk that these tools might deliver incorrect or misleading results, for instance if data is missing or inaccurate. The Hannover-based German National Library of Science and Technology (TIB) is a public law foundation run by the federal state of Lower Saxony, but it is also active in research and, as part of its remit, creates new services. Its key lines of research cover data science, non-textual materials, open science and visual analytics - which brings us back to the challenge in question. On home turf at CEBIT 2018, the TIB is exhibiting a Big Data Integration Framework based on semantic web technologies such as linked data, controlled vocabularies and ontologies. This can be used to generate a knowledge graph that combines both the data and knowledge derived from big data sources.
The big data analyses performed using these knowledge graphs now make it possible to precisely identify meaningful patterns and associations between entities. The TIB’s Big Data Framework is already being used to integrate biomedical and scientific databases, for instance to analyze interactions between medicines and proteins. Examples include the EU-funded research projects "IASIS - Big Data for Precision Medicine" and “"BigMedilytics - Big Data for Medical Analytics", which are also being showcased in Hannover. Another project, called “"BOOST 4.0 - Big Data for Factories", is examining potential industrial applications. In all of these projects, the TIB's integration framework is being used to transform big data into genuinely useful results that help expand knowledge and simplify decision-making.