Advances and challenges for scalable provenance in stream processing systems

Abstract

While data provenance is a well-studied topic in both database and workflow systems, its support within stream processing systems presents a new set of challenges. Part of the challenge is the high stream event rate and the low processing latency requirements imposed by many streaming applications. For example, emerging streaming applications in healthcare or finance call for data provenance, as illustrated in the Century stream processing infrastructure that we are building for supporting online healthcare analytics. At anytime, given an output data element (e.g., a medical alert) generated by Century, the system must be able to retrieve the input and intermediate data elements that led to its generation. In this paper, we describe the requirements behind our initial implementation of Century’s provenance subsystem. We then analyze its strengths and limitations and propose a new provenance architecture to address some of these limitations. The paper also includes a discussion on the open challenges in this area.