Ananke: A Streaming Framework for Live Forward Provenance
Artikel i vetenskaplig tidskrift, 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

Författare

Dimitrios Palyvos-Giannas

Chalmers, Data- och informationsteknik, Nätverk och system

Bastian Havers

Chalmers, Data- och informationsteknik, Nätverk och system

Marina Papatriantafilou

Chalmers, Data- och informationsteknik, Nätverk och system

Vincenzo Massimiliano Gulisano

Chalmers, Data- och informationsteknik, Nätverk och system

Proceedings of the VLDB Endowment

2150-8097 (ISSN)

Vol. 14 3 391-403

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Ämneskategorier

Datavetenskap (datalogi)

DOI

10.14778/3430915.3430928

Relaterade dataset

Ananke Online Repository [dataset]

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

Mer information

Senast uppdaterat

2020-12-18