Poster: Twins, a Middleware for Adaptive Streaming Provenance at the Edge
Paper in 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

Author

Mikael Gordani Shahri

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

Andréas Erlandsson

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

Dimitrios Palyvos-Giannas

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

Vincenzo Massimiliano Gulisano

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

ACM International Conference Proceeding Series

235-236
9781450389334 (ISBN)

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

Subject Categories

Computer Engineering

Biomedical Laboratory Science/Technology

Computer Systems

DOI

10.1145/3427796.3433931

More information

Latest update

2/11/2021