2014 IEEE Symposium on Computational Intelligence in Big Data, CIBD 2014, Orlando, FL, USA, December 9-12, 2014

IEEE CIBD'2014 will be held simultaneously with over 20 other symposia and workshops in one location at the 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014). This international event promotes all aspects of the theory and applications of computational intelligence. Sponsored by the IEEE Computational Intelligence Society, this event will attract top researchers, professionals, practitioners and students from around the world. The registration to SSCI 2014 will allow participants to attend all the symposia, including the complete set of the proceedings of all the meetings, coffee breaks, lunches, and the banquet. Scope IEEE CIBD'2014 will bring together scientists, engineers, researchers and students from around the world to present recent advances, explore the challenges and opportunities in the application of Computational Intelligence (CI) techniques to the emerging and exciting field of Big Data and data sciences. This conference will provide a forum for the presentation of recent results in CI algorithms, software and systems for big data analytics, and to discuss the practical and theoretical challenges in big data and to explore CI solutions to tackle these challenges and issues. Papers that are concerned with general issues in data mining should be submitted to CIDM'2014. IEEE CIBD'2014 solicits papers that report new research results that apply CI technologies, such as neural networks and learning algorithms, fuzzy systems, evolutionary computation, and other emerging techniques to Big Data, ranging from theory, methodologies and algorithms for handling the 3Vs (Volume, Variety, and Velocity) of big data, to their applications to the development of big data analytics systems. More specifically, topics of IEEE CIBD'2014 include  Integrative analytics of diverse data resources  Integration of structured and unstructured data  Extracting understanding from distributed, diverse and large-scale data resources  Extracting understanding from real-time large-scale data streams  Predictive analytics and in-memory analytics  New information infrastructure for big data  Big data visualization and visual data analytics  Semantic technologies for big data  Scalable learning techniques for big data  Optimization of complex systems involving big data  Data governance and management in big data  Human-computer interaction and collaboration in big data  Big data and cloud computing