Big Data Analytics

In recent years, the term Big Data has emerged to describe a new paradigm for data applications. New technologies tend to emerge with a lot of hype, but it can take some time to tell what is new and different. Big Data is a broad terminology for extremely large and complex data sets, which cannot be adequately handled by traditional data processing tools and mechanisms. Most common definitions for Big Data describe the 3Vs for big data, which is: high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making (Gartner, Inc., 2013).

 

The challenge is to fundamentally improve the technology, methods, standards and processes, building on a solid scientific basis, and responding to real needs. At GRIS, we focus on Big Data Analytics (availability, collection, storage, distribution and use), which is becoming a major key driver in data-intensive sectors (Transport, Health, Manufacturing, Energy and Finance). Some of the technical focus areas which we address at GRIS can be described as follows:

 

· Methods for data collection, cleaning and fusion, supporting integration across transactional systems, operational data stores, BI (Business Intelligence) platforms, MDM (Multi-Dimensional Data Modelling) hubs, the cloud, and other Big Data platforms.

· Distributed data and process mining, predictive analytics and visualization at the service of industrial decision support processes, including the development of data-driven approaches such as linear regression, using historical large datasets.

· Real-time complex event processing over extremely large numbers of high volume streams, as for example the development of anomaly detection mechanisms aiming to find patterns in data streams that do not conform to expected behaviours.