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T07 - Big Data Fusion and Analytics

Distributed and parallel processing paradigms provide efficient ways to handle big data for high- level fusion and analytics. In this tutorial, I will present some techniques for fusion and analytics to process data that are inherently distributed or residing on the cloud. The underlying foundational techniques are distributed belief propagation for graphical probabilistic models and traditional database query processing on the cloud via the map-reduce paradigm.

The tutorial materials are based on the following two books by the speaker:

  • Subrata Das. (2008). “High-Level Data Fusion,” Artech House, Norwell, MA.
  • Subrata Das. (2014). “Computational Business Analytics,” Chapman & Hall/CRC Press.

The fusion and analytics techniques to be discussed will handle both structured and unstructured data. Structured data refers to computerized information which can be easily interpreted and used by a computer program supporting a range of tasks. For example, the information stored in a relational database is structured, whereas text documents, videos, and images are usually unstructured. 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 descriptive and predictive analytics of both structured sensor/transactional and unstructured human intelligence data. The demos to be presented are in the areas of distributed situation assessment and sentiment analyses. 

Instructor biography

Dr. Subrata Das is the founder of Machine Analytics, a company in the Boston area providing business analytics and data fusion consultancy services and developing customized solutions for clients in government and industry. Subrata is also providing consulting services to several companies. Subrata’s technical expertise includes mathematical logics, probabilistic reasoning including Bayesian belief networks, symbolic argumentation, particle filtering, and a broad range of computational artificial intelligence techniques.

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. Subrata guided applied analytics research and development in the areas of unstructured data analyses, machine translation, image processing, and decision-making under uncertainty. Subrata was one of the five- members in the high-profile Xerox task force Knowledge Work 2020, alongside colleagues from the Palo Alto Research Center (PARC), to explore a strategic vision of the future of work.

Before joining Xerox, Subrata held the Chief Scientist position at Charles River Analytics in Cambridge, MA, working on projects funded by DARAP, NASA, US Air Force, Army and Navy, ONR and AFRL. In the past, Subrata held research positions at Imperial College and Queen Mary and Westfield College, both part of the University of London. He received his PhD in Computer Science from Heriot-Watt University in Scotland, a Master’s in Mathematics from the University of Kolkata, and an M.Tech from the Indian Statistical Institute.

Subrata has published many journal and conference articles. He is the author of the books Computational Business Analytics, published by CRC Press/Chapman and Hall, High-Level Data Fusion, published by the Artech House, Foundations of Decision Making Agents: Logic, Modality, and Probability, published by the World Scientific/Imperial College Press, and Deductive Databases and Logic Programming, published by Addison-Wesley. Subrata has also co-authored the book entitled Safe and Sound: Artificial Intelligence in Hazardous Applications, published by the MIT Press.

Subrata served as a member of the editorial board of the Information Fusion journal, published by Elsevier Science. He has been a regular contributor, a technical committee member, a panel member, and a tutorial lecturer at the International Conference on Information Fusion. 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 is proficient in multiple programming languages including Java, C++ and Prolog, and in various database systems. He has conceived and developed the in-house tools aText, iDAS and RiskAid.