Efficient Provenance Management via Clustering and Hybrid Storage in Big Data Environments

Abstract

Provenance is a type of metadata that records the creation and transformation of data objects. It has been applied to a wide variety of areas such as security, search, and experimental documentation. However, provenance usually has a vast amount of data with its rapid growth rate which hinders the effective extraction and application of provenance. This paper proposes an efficient provenance management system via clustering and hybrid storage. Specifically, we propose a Provenance-Based Label Propagation Algorithm which is able to regularize and cluster a large number of irregular provenance. Then, we use separate physical storage mediums, such as SSD and HDD, to store hot and cold data separately, and implement a hot/cold scheduling scheme which can update and schedule data between them automatically. Besides, we implement a feedback mechanism which can locate and compress the rarely used cold data according to the query request. The experimental test shows that the system can significantly improve provenance query performance with a small run-time overhead.

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