At the heart of the new enterprise is the ability to continuously gather information about business entities of interest (of any shape/form/size), run analytics against them (to cluster, correlate, learn patterns, predict future behavior), and inform ongoing business transactions with insights gained through such analysis (for fraud detection, cross-selling, customer management etc).
To support this future of operational decision-making, the META project is developing a scalable, contextual reasoning platform. This platform is built on the three legs of event processing, transactional updates of state, and analytic computations against global state and event fluxes. This platform supports situations, an autonomously maintained, stateful, declarative construct for associative, temporal and spatial contexts. Coupled with stateless situation-action rules, situations provide a powerful reactive, timed, event-driven programming model for cloud services, mobile applications and IoT applications.
Vijay Saraswat works in the areas of constraints, logic, concurrency and programming languages, across AI, Theory and Programming Systems. Vijay graduated from IIT Kanpur in 1982 with a B Tech in Electrical Engineering (and the Ratan Swaroop Gold Medal), and from Carnegie-Mellon University in 1989 with a PhD in Computer Science. His thesis on "Concurrent Constraint Programming" (supervised by Dana Scott) won the 1989 ACM Doctoral Dissertation Award. Vijay worked for nine years in the Scientific and Engineering Reasoning Area at Xerox PARC on model-based reasoning and diagnosis, constraints, linear logic, natural language semantics and networked virtual worlds. He moved to AT&T Research in 1996 where he became Technology Consultant and led the creation of the AT&T Matrix and Instant Messaging systems, and co-chaired various IETF Working Groups. After a couple of start-ups and a year as a Professor at Penn State, Vijay joined IBM TJ Watson Research Center in Sep 2003. There, he led the work on the X10 programming language, and parallel programming models. In 2013, he was named Chief Scientist of ODM Insights for his role in the creation of a new IBM product on Real-time Insight. In 2014, he became Chief Scientist for the "Computing as a Service Division" of IBM Research. He is currently spear-heading an effort across IBM Research on large-scale machine learning. He has collaborated with over 100 researchers, and published over 100 papers that have been cited over 7500 times. He has taught at several universities world-wide, and is an Adjunct Professor at Columbia University in New York.