Influence-based provenance for dataflow applications with taint propagation-Reference-Cited by-同舟云学术

Influence-based provenance for dataflow applications with taint propagation

Published:2020-10-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 11th ACM Symposium on Cloud Computing
language:
Short-container-title:

Author:

Teoh Jason¹,Gulzar Muhammad Ali²,Kim Miryung¹

Affiliation:

1. UCLA

2. Virginia Tech

Funder

Office of Naval Research

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3419111.3421292

Reference43 articles.

1. [n.d.]. AggregateByKey. https://spark.apache.org/docs/2.1.1/api/java/org/apache/spark/rdd/PairRDDFunctions.html. [n.d.]. AggregateByKey. https://spark.apache.org/docs/2.1.1/api/java/org/apache/spark/rdd/PairRDDFunctions.html.

2. [n.d.]. Hadoop. http://hadoop.apache.org/. [n.d.]. Hadoop. http://hadoop.apache.org/.

3. [n.d.]. Spark. https://spark.apache.org/. [n.d.]. Spark. https://spark.apache.org/.

4. Dynamic program slicing

5. Techniques for efficiently querying scientific workflow provenance graphs

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