Big Data & Decision Making

Track Coordinators: Jie Xu, George Mason University and Toyotaro Suzumura, IBM Research

The Big Data Simulation and Decision Making track focuses on Big Data, an emerging field of work that leverages the volume, variety and velocity of data in order to make better decisions. Recent Big Data simulations have been focused on a variety of domains, including atomic physics, weather, power grids, traffic networks and urban populations. Policy makers, investors, planners, physicians, supply chain managers, military leaders, teachers and administrators face the challenge of making numerous decisions on a regular basis. In a world that is increasingly complex, it has become virtually impossible to take well-informed decisions by simply relying on intuition and/or static rules of thumb. The challenge is compounded by the availability of large quantities of dynamically-changing data that must be analyzed and understood as well as the tightening of deadlines. In such a setting, simulation can serve as a practical platform for organizing the data, generating and evaluating various scenarios, and supporting the decision making process in a methodological fashion. This track therefore focuses on the role of simulation in supporting decision making in the presence of complex data. The track deals with methodologies developed for processing complex data and creating simulation based approaches for decision making, as well as the deployment of the methodologies in various fields in engineered, social, and natural systems.

Topics include, but are not limited to, the following:

How Big is Your Data?

• Big Data analytics and metrics
• Big Data and simulation systems
• Big Data and simulation model management
• Big Data in smarter planet solutions
• Scientific applications of Big Data
• Real-life case studies of Big Data
• Big Data collaboration framework

Big Data, Bad Data - Raising Situational Awareness

• Big Data mining and uncertainty quantification
• Predictive simulation and Big Data analytics
• Artificial intelligence and cognitive simulation
• Visualization analytics for Big Data

Decision-Making in an Uncertain World

• Simulation and decision modeling
• Risk modeling and management
• Dynamic data-driven simulation
• Automatic tuning
• Validation of Big Data simulation