Data-Driven Secure Business Intelligence (DataBIN)

Research Project, 2012 – 2016

Develop scalable system architectures, algorithms, development methods, and working demonstrators for temporal analysis of large data sets harvested from open sources (web, social media etc.) as well as corporate databases (customer data, business intelligence data) to enable new forms of collaborative innovation. These analysis services must scale to handle very large data sets, and have mechanisms for ensuring privacy and personal integrity for individuals as well as security for customers. Applications include competitive business intelligence, continuous product development, predictive analytics and many other areas of great importance to Swedish industry, both as providers and users of these services. Concrete demonstrators include: - Predictions of financial markets (Recorded Future, First Swedish Research) - Analysis of consumer behaviour and predictions about future behaviour respecting privacy (RF, TeliaSonera) - Service development through experimental and collaborative innovation (RF, Tibco Spotfire) The work will proceed in an iterative fashion with implementation and testing of methods leading to feedback into disciplinary research for improved methods which are then implemented and tested again. In the first two years we will develop prototypes to test our methods on the Recorded Future database and in the following years we will scale them up to integrate into the RF system implemented on the Amazon EC2 cloud architecture.