Trusted and Auditable Decision Aids over Data Streams

Data stream management systems exist to support dynamic analysis of streaming data, often to inform decision-making. Decision support systems exist to enable decisions to be made that take into account user priorities. However, although these categories of system are now quite mature, there has been little work investigating their use together. In this paper we bring together a well established streaming platform (Storm) and a widely used decision-support methodology (Analytic Hierarchy Process) to provide dynamic decision support over data streams. In so doing, we also investigate approaches making recommendations auditable (using provenance) and trustable (using explanations). The resulting stream decision support system is illustrated using an application that supports train journey planning.