The key aim of the ASTERI project is to demonstrate the automated creation of a Star Schema relational data warehouse representation of the database for an On-line Transaction Processing (OLTP) system. Database structures for OLTP systems are optimised for data storage and so are poorly structured for obtaining analytics-oriented information. Star Schema-based solutions are optimised for data retrieval i.e. the creation of analytics. Ease of access to analytics by a wide range of users is of increasing commercial importance. The innovation in the ASTERI project is based upon the application of: a) Machine learning techniques to produce the transformation from the original OLTP database format to the relational database form used in the data warehouse. The data engineer is then used to confirm a transformation as opposed to its creation. b) Event-driven visualisation using HTML5 based data presentation. This includes addressing efficient and responsive visualization of 100s of thousands of data points. The associated reports will then reflect the latest data available. While the significance of Star Schema to data warehouse representation has been well known for the past 20 years, it is only in the past 5 years that research into the use of ‘Machine Learning’ to create a Star Schema representation has shown the potential of the approach. With these new capabilities Rosslyn Analytics (RAL) can substantially reduce the effort and cost to engage new customers and can rapidly create new sector database schemas enabling it to expand into new markets and gain market share with smaller organisations. RAL also intends to leverage the new tools to expand its emerging OEM sales to major global data analysis service providers such as Xerox, Pitney Bowes and Qlikview to be used as their data analysis service engine. In addition, the automated tools will permit increased sales by development partners who use the RAL RAPid toolset to deliver value for their own clients