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.
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This material is based upon work supported by the National Science Foundation under Grant ACI-1547467. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Ahmad, R., Bru, M., Gehani, A. (2018). Streaming Provenance Compression. In: Belhajjame, K., Gehani, A., Alper, P. (eds) Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science(), vol 11017. Springer, Cham. https://doi.org/10.1007/978-3-319-98379-0_27
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DOI: https://doi.org/10.1007/978-3-319-98379-0_27
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