Ariadne: Managing Fine-Grained Provenance on Data Streams

Provided by: Association for Computing Machinery
Topic: Big Data
Format: PDF
Managing fine-grained provenance is a critical requirement for Data Stream Management Systems (DSMS), not only to address complex applications that require diagnostic capabilities and assurance, but also for providing advanced functionality such as revision processing or query debugging. This paper introduces a novel approach that uses operator instrumentation, i.e., modifying the behavior of operators, to generate and propagate fine-grained provenance through several operators of a query network. In addition to applying this technique to compute provenance eagerly during query execution, the authors also study how to decouple provenance computation from query processing to reduce run-time overhead and avoid unnecessary provenance retrieval.

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