BigData 2015 - Special Session on Big Data and Predictive Business Analytics

Today, the amount of data we collect has been exploding. As a result, big data has become a new buzzword in information technology. Storing, managing and analyzing big data is challenging and will become a key basis of competition and a major differentiator between high performing and low performing organizations. Big data analytics, e.g. predictive analytics, apply techniques from statistics, data mining, text mining, machine learning, and mathematical modeling to predict future outcomes based on historical data. Using the patterns and information discovered from historical data allows businesses to identify potential future opportunities and risks. This session provides a forum for researchers to present and discuss latest research results, to summarize recent advances, to evaluate existing algorithms and methods, and to timely identify and address emerging problems and challenges with regard to big data and predictive business analytics. We invite the submission of high-quality, original and unpublished papers in this area. Topics for this session include, but are not limited to:
Supply Chain Analytics
Business Forecasting
Business Data Visualization
Business Predictive Analytics
Business Intelligence
Demand Forecasting and Planning
Business Data Storage, Analysis, and Modeling
Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.