Industry

• Big Data solutions typically focus on structured data
• But the majority of information contains unstructured data

In this talk we show that LOD annotations are a powerful tool for semantic enrichment of Social Media microposts, allowing for reasoning with information that is not transmitted directly through the Social Media channels, but available in rich knowledge bases.

Different small group discussions with a large group of participants: Several coffee houses are set up in parallel, focusing on different viewpoints and questions. You enter the Cafés one after the other in a set frequency, mixing with your fellow colleagues and continuing discussions which were started by another group before.

Four to five chairs are arranged in an inner circle. This is the fishbowl. The remaining chairs are arranged in concentric circles outside the fishbowl. A few participants are selected to fill the fishbowl, while the rest of the group sit on the chairs outside the fishbowl. In an open fishbowl, any member of the audience can, at any time, occupy the empty chair and join the fishbowl. When this happens, an existing member of the fishbowl must voluntarily leave the fishbowl and free a chair. The discussion continues with participants frequently entering and leaving the fishbowl. When time runs out, the fishbowl is closed and the moderator summarizes the discussion.

The growing use and popularity of sensors and monitoring devices in the context of industries has paved the way not only for new analysis approaches but also for new insights in the production chains. Nevertheless, the potential of this data, especially for optimizing the maintenance process, has not yet been fully used.

In the Dutch SmartDairyFarming project, better decision support for the dairy farmer on daily questions around feeding, insemination, calving and milk production processes is made possible using a variety of big data sources containing static and dynamic sensor data of individual cows.

In recent years there is a rapid growth of unstructured text content and multimedia documents, which includes audios, videos and images in web as well as in the enterprise and these large volumes of data is not very useful without effective methods for content analysis and retrieval.

Linklaters is one of the world’s leading global law firms. The firm has a wealth of high value information held within our systems however due to the nature of these systems it is not always easy to leverage this value. Our goal was to improve decision making across the firm by transforming access to and ability to query data.

Fact and entity extraction from unstructured, natural language texts is already a very challenging problem. But even if a system can identify persons, organisations or other entities, it is very likely that an essential piece of information is missing: