Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs

In stream data processing, data arrives continuously and is processed by decision making, process control and e-science applications. To control and monitor these applications, reproducibility of result is a vital requirement. However, it requires massive amount of storage space to store fine-grained provenance data especially for those transformations with overlapping sliding windows. In this paper, we propose techniques which can significantly reduce storage costs and can achieve high accuracy. Our evaluation shows that adaptive inference technique can achieve almost 100% accurate provenance information for a given dataset at lower storage costs than the other techniques. Moreover, we present a guideline about the usage of different provenance collection techniques described in this paper based on the transformation operation and stream characteristics