Dynamic Big Data Applications

Craig C. Douglas (University of Wyoming, USA)

We provide a scientific yet simple definition of a fully dynamic data application and compare it to a variety of application systems that range from traditional static applications to fully dynamic. The ability of an application to control and guide the measurement process and determine when, where, and how it is best to gather additional data has itself the potential of enabling more effective me asurement and prediction methodologies.

Big Data is a new paradigm that asks the question, "What can you do if you have all of the known data for a given problem?" It is subsuming the concept of computational sciences due in large part to the fact that many interesting computational science applications, as they became dynamic data enabled, generate almost infinite amounts of data that has to be mined in order to find the useful information. The data may come from a network of sensors, databases, or a combination.

Creating a usable computer system to create dynamic, big data applications is currently too complex and needs to be simplified. A framework for creating a useful toolkit for developing general dynamic big data driven application systems is described.

Examples of significant problems from a number of different fields that can be solved using our paradigm are described during the talk.