Ananke: A Streaming Framework for Live Forward Provenance
Journal article, 2020

Data streaming enables online monitoring of large and continuous event streams in Cyber-Physical Systems (CPSs). In such scenarios, fine-grained backward provenance tools can connect streaming query results to the source data producing them, allowing analysts to study the dependency/causality of CPS events. While CPS monitoring commonly produces many events, backward provenance does not help prioritize event inspection since it does not specify if an event’s provenance could still contribute to future results. To cover this gap, we introduce Ananke, a framework to extend any fine-grained backward provenance tool and deliver a live bi-partite graph of fine-grained forward provenance. With Ananke, analysts can prioritize the analysis of provenance data based on whether such data is still potentially being processed by the monitoring queries. We prove our solution is correct, discuss multiple implementations, including one leveraging streaming APIs for parallel analysis, and show Ananke results in small overheads, close to those of existing tools for fine-grained backward provenance.

data streaming

big data

provenance

Author

Dimitrios Palyvos-Giannas

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Bastian Havers

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Marina Papatriantafilou

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Vincenzo Massimiliano Gulisano

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Proceedings of the VLDB Endowment

2150-8097 (ISSN)

Vol. 14 3 391-403

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Subject Categories

Computer Science

DOI

10.14778/3430915.3430928

Related datasets

Ananke Online Repository [dataset]

URI: https://github.com/dmpalyvos/ananke

More information

Latest update

12/18/2020