Poster: Twins, a Middleware for Adaptive Streaming Provenance at the Edge
Paper i proceeding, 2021

Data streaming applications process continuous flows of data to detect unusual/critical events. When it is beneficial to further analyze the source data leading to such events, fine-grained streaming provenance can be used to link each event back to its contributing data. Existing provenance tools, though, (i) can be computationally heavy, especially for applications deployed on resource-constrained devices at the edge of Cyber-Physical Systems, and (ii) cannot activate/deactivate provenance recording based on user-defined rules. To cover such gaps, we present Twins, a new adaptive provenance tool that leverages APIs found in state-of-the-art streaming frameworks to allow for custom conditions to activate/deactivate provenance recording. Our preliminary results, based on an implementation on top of Apache Flink and GeneaLog show that Twins can match, during the periods in which provenance is inactive, the performance of queries that do not record provenance at all.

Fine-grained data provenance

Stream processing

Middleware

Författare

Mikael Gordani Shahri

Chalmers, Data- och informationsteknik, Nätverk och system

Andréas Erlandsson

Chalmers, Data- och informationsteknik, Nätverk och system

Dimitrios Palyvos-Giannas

Chalmers, Data- och informationsteknik, Nätverk och system

Vincenzo Massimiliano Gulisano

Chalmers, Data- och informationsteknik, Nätverk och system

ACM International Conference Proceeding Series

235-236

22nd International Conference on Distributed Computing and Networking, ICDCN 2021
Online, Japan,

Ämneskategorier

Datorteknik

Biomedicinsk laboratorievetenskap/teknologi

Datorsystem

DOI

10.1145/3427796.3433931

Mer information

Senast uppdaterat

2021-02-11