Aims and Scope
In today's competitive and rapidly changing environment, analysing data to understand how the organization (commercial, health, government and security) is performing, to predict outcomes and trends, and to improve the effectiveness of business processes underlying business operations has become critical. The traditional approach of scheduled analytic reports, preconfigured key performance indicators and fixed dashboards is no longer adequate. Increasingly, organizations want to be able to base their decisions on the latest set of raw data and the real-time analytics derived from them. What users demand now are easy-to- use intelligent platforms and applications capable of analysing real-time business data to provide insight and actionable information at the right time. The end goal is to improve the enterprise performance by better and timelier decision making, enabled by the availability of up-to- date, high quality information. The so-called "Big Data" systems have high Velocity (fast arrival rate of data) as one of the defining "3V" characteristics. We are in the midst of an era where acting fast can make all the difference between succeeding and failing. Hence, it is natural that there is an increasing focus on real-time processing in the academia as well as in industry.
As a response, the notion of "real-time enterprise" emerged a few years ago and was recognized in the industry. Gartner defines it as "using up-to- date information, getting rid of delays, and using speed for competitive advantage is what the real-time enterprise is all about. Indeed, the goal of the real-time enterprise is to act on events as they happen".
Although there has been progress in this direction and many companies are introducing products towards making this vision a reality, there is still a long way to go. In particular, the whole lifecycle of business intelligence requires new techniques and methodologies capable of dealing with the new requirements imposed by the real-time enterprise. From the capturing of business performance data to the injection of actionable information back into business processes, all the stages of the Business Intelligence (BI) cycle call for new algorithms and paradigms as the basis of new functionalities including dynamic integration of event streams and data feeds from operational sources, evolution of ETL transformations and analytical models, query processing over stream and historical data, and dynamic generation of adaptive dashboards that support data exploration and infrastructure that facilitates all of the above, just to name a few.
This workshop, in its twelfth edition, will provide a perfect forum to bring together researchers and practitioners interested in the different facets of business intelligence, from data acquisition, to processing, to transformation and analysis, to data visualization, applied towards the real-time enterprise. Participants will have the opportunity to present their current work and innovations in the context of real problems to which they have applied their solutions, innovations and infrastructure and discuss challenges, opportunities and advances in this field. At the same time, as with any other workshop, it will provide the right environment to network with people working on the workshop topics, fostering future collaboration and the consolidation of a solid community that sets the path towards the future of this increasingly important field.