Intended Audience: The intended audience include designers and developers of analytics systems for any vertical (e.g., defense, healthcare, finance and accounting, human resources, customer support, transportation) who work within business organizations around the world. They will find the tutorial useful as a vehicle for moving towards a new generation of big data fusion and analytics approaches.

Description: Big data has tremendous potential to transform businesses but poses significant challenge in searching, processing, and extracting actionable intelligence. In this tutorial, I will present some techniques for fusion and analytics to process big centralized warehouse data, inherently distributed data, and data residing on the cloud. The fusion and analytics techniques to be discussed will handle both structured transactional and sensor data as well as unstructured textual data such as human intelligence, emails, blogs, surveys, etc.

As a background, this tutorial is intended to provide an account of both the cutting-edge and the most commonly used approaches to high-level data fusion and predictive and text analytics. The demos to be presented are in the areas of distributed search and situation assessment, information extraction and classification, and sentiment analyses.

Some of the tutorial materials are based on the following two books by the speaker: 1) Subrata Das. (2008). “High-Level Data Fusion,” Artech House, Norwell, MA; and 2) Subrata Das. (2014). “Computational Business Analytics,” Chapman & Hall/CRC Press.

Tutorial Topics include the following: High-Level Fusion, Descriptive and Predictive Analytics, Text Analytics, Machine Learning, Decision Support and Prescriptive Analytics, Cloud Computing, Distributed Fusion, Hadoop and MapReduce, Natural Language Query, Big Data Query Processing, Graphical Probabilistic Models, Bayesian Belief Networks, Distributed Belief Propagation, Text Classification, Supervised and Unsupervised Classification, Deep Learning, Information Extraction, Natural Language Processing.

Prerequisites: Some background in the theory of probability and statistics, data mining, programming languages, and databases will be desired.

Presenter: Subrata Das

Dr. Subrata Das is the founder of Machine Analytics (www.machineanalytics.com), a company in the Boston area customizing big analytics and data fusion solutions for clients in government and industry. Subrata is also an adjunct faculty at the Schools of Business in Villanova Unversity.

Subrata recently spent two years in Grenoble, France, as the manager of over forty researchers in the document content laboratory at the Xerox European Research Centre. In the past, Subrata led many projects funded by DARAP, NASA, US Air Force, Army and Navy, ONR and AFRL. In the past, Subrata held research positions at Imperial College, London, received a PhD in Computer Science from Heriot-Watt University in Scotland, and masters from University of Kolkata and Indian Statistical Institute.

Subrata has published many journal and conference articles. He is the author of five books including Computational Business Analytics, published by CRC Press/Chapman and Hall, and High-Level Data Fusion, published by the Artech House.

Subrata has published many conference and journal articles, edited a journal special issue, and regularly gives seminars and training courses based on his books. Subrata served as a member of the editorial board of the Information Fusion journal, published by Elsevier Science.