Big data poses new challenges to query answering, from computational complexity theory to query evaluation techniques. Several questions arise. What query classes can be considered tractable in the context of big data? How can we make query answering feasible on big data? What should we do about the quality of the data, the other side of big data? Tale Research aims to provide an overview of recent advances in tackling these questions, using social network analysis as an example.  

Nowadays, with the volume of data growing at an unprecedented rate, big data mining and knowledge discovery have become a new challenge. Rough set theory for knowledge acquisition has been successfully applied in data mining. The MapReduce technique has received much attention from both scientific community and industry for its applicability in big data analysis. To mine knowledge from big data, we present parallel rough set based methods for knowledge acquisition using MapReduce. Comprehensive experimental evaluation on large data sets shows that the proposed parallel methods can effectively process big data.

Business analytics, occupying the intersection of the worlds of management science, computer science and statistical science, is a potent force for innovation in both the private and public sectors. The successes of business analytics in strategy, process optimization and competitive advantage has led to data being increasingly recognized as a valuable asset in many organizations. In recent years, thanks to a dramatic increase in the volume, variety and velocity of data, the loosely defined concept of "Big Data" has emerged as a topic of discussion in its own right -- with different viewpoints in both the business and technical worlds. From our perspective, it is important for discussions of "Big Data" to start from a well-defined business goal, and remain moored to fundamental principles of both cost/benefit analysis as well as core statistical science. With practical lessons from Big Data deployments in business, we also pose a number of research challenges that may be addressed to enable the business analytics community bring best data analytic practices when confronted with massive data sets.