Optimization techniques for data-intensive decision flows

For an enterprise to take advantage of the opportunities afforded by electronic commerce it must be able to make decisions about business transactions in near-real-time. In the coming era of segment-of-one marketing, these decisions will be quite intricate, so that customer treatments can be highly personalized, reflecting customer preferences, the customer´s history with the enterprise, and targeted business objectives. This paper describes a paradigm called “decision flows” for specifying a form of incremental decision-making that can combine diverse business factors in near-real-time. This paper introduces and empirically analyzes a variety of optimization strategies for decision flows that are “data-intensive”, i.e. that involve many database queries. A primary focus is on the use of parallelism and eagerness (a.k.a. speculative execution) to minimize work and/or reduce response time. A family of optimization techniques is developed, including algorithms and heuristics for scheduling tasks of the decision flow. Using a prototype execution engine the techniques are compared and analyzed in connection with decision-making applications having differing characteristics