Information Value-Driven Near Real-Time Decision Support Systems

In this paper, we focus on challenges of supporting a decision support system (DSS) based on a hybrid approach (i.e. a federation system with data placement) for agile business intelligence applications. A DSS needs to be designed to handle a workload of potentially complex queries for important decision-making processes. The response time requirement (and a realistic goal) for such a DSS is near real time. The users of a DSS care about not only the response time but also the time stamp of the business operation report since both of them introduce uncertainty and risks to business decision-making. In our proposed DSS, each report is assigned with a business value; denoting its importance to business decision-making. An Information Value (IV) is a business value of a report discounted by time to reflex the uncertainty and risks associated with the computational latency and synchronization latency. We propose a novel Information Value-driven Query Processing (IVQP) framework specific for near real time DSS applications. The framework enables dynamic query plan selection by taking into account of information value and adaptation for online-arrival ad hoc queries. The framework works with single query as well as a workload of queries. The experimental results based on synthetic data and TPC-H show the effectiveness of our approach in achieving optimal information values for the workloads.