Decisions as a Service for Application Centric Real Time Analytics

The need for application-level intelligence cannot be easily satisfied with existing architectures or methodologies that separate methods and tools for application developers and data scientists. We aim, therefore, to develop a framework (an architecture and a methodology) to make it possible to add intelligence capabilities to existing applications (decision-enablement) and to facilitate building new decision-enabled applications. The proposed approach starts by instrumenting the existing code with logging and instrumented decision points. The execution of the application produces information that is initially used to reengineer its behavior and then the decision points are used to conduct search-based experimentation to optimize its behavior. This lightweight instrumentation allows the application developer and data scientist to fully exploit their capabilities, with the framework providing the glue needed to put their work together easily and transparently. In particular, the analytic capabilities, such as analysis of operational data, are better dealt with in the decision-making part, without complicating the mechanics of how the application functions. We plan to apply this framework to decision enable existing systems and to build new systems from scratch and measure the effectiveness of the approach and of the resulting products.