Techniques to Adjust Computational Trends Involving Changing Data (TACTIC-D)

The integration of data from numerous, disparate sources makes the transformation of those data into actionable information extremely difficult. Applying analysis, synthesis, and predictive modeling techniques using human interaction is impractical, especially for converting data to information in situations which are rapidly changing and in which human life might be at risk, e.g. battlefield. Combining data which are sparse, varying in reliability, and with a time dependent factor to other data such as inventory, capability, and availability to make a decision falls into the category of complex decision support systems. Addressing this problem requires adaptive Big streaming data analytics that can: (a) model cyber physical infrastructures encompassing realistically complex warfighter scenarios, (b) ingest the massive data sets to capture large-scale dynamic systems complexity, and (c) process and update the analytics results in a timely manner to test contrasting mechanistic models and drive the next set of analyses. Currently, there is no adaptive big data analytics framework processing data streams in real-time and adopt to changes in cyber physical environments. The goal of this project is to develop an Adaptive Big Data Analytics Environment (ABDAE) that can adjust computations to respond promptly to rapid changes in data and cyber physical environments.