Abstract
Operating system data provenance has a range of applications, such as security monitoring, debugging heterogeneous runtime environments, and profiling complex applications. However, fine-grained collection of provenance over extended periods of time can result in large amounts of metadata. Xie et al. describe an algorithm that leverages the subgraph similarity and locality of reference in provenance graphs to perform batch compression. We build on their effort to construct an online version that can perform streaming compression in SPADE. Our optimizations provide both performance and compression improvements over their baseline.
Cite
CITATION STYLE
Ahmad, R., Bru, M., & Gehani, A. (2018). Streaming provenance compression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11017 LNCS, pp. 236–240). Springer Verlag. https://doi.org/10.1007/978-3-319-98379-0_27
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