GeneaLog: Fine-Grained Data Streaming Provenance at the Edge
Paper i proceeding, 2018

Fine-grained data provenance in data streaming allows linking each result tuple back to the source data that contributed to it, something beneficial for many applications (e.g., to find the conditions triggering a security- or safety-related alert). Further, when data transmission or storage has to be minimized, as in edge computing and cyber-physical systems, it can help in identifying the source data to be prioritized.
The memory and processing costs of fine-grained data provenance, possibly afforded by high-end servers, can be prohibitive for the resource-constrained devices deployed in edge computing and cyber-physical systems. Motivated by this challenge, we present GeneaLog, a novel fine-grained data provenance technique for data streaming applications. Leveraging the logical dependencies of the data, GeneaLog takes advantage of cross-layer properties of the software stack and incurs a minimal, constant size per-tuple overhead. Furthermore, it allows for a modular and efficient algorithmic implementation using only standard data streaming operators. This is particularly useful for distributed streaming applications since the provenance processing can be executed at separate nodes, orthogonal to the data processing. We evaluate an implementation of GeneaLog using vehicular and smart grid applications, confirming it efficiently captures fine-grained provenance data with minimal overhead.

Data streaming

Fine-grained data provenance

Edge architectures

Författare

Dimitrios Palyvos-Giannas

Chalmers, Data- och informationsteknik, Nätverk och system

Vincenzo Massimiliano Gulisano

Chalmers, Data- och informationsteknik, Nätverk och system

Marina Papatriantafilou

Chalmers, Data- och informationsteknik, Nätverk och system

Proceedings of the 19th International Middleware Conference

227-238

19th International Middleware Conference
Rennes, France,

STAMINA - GE

Göteborg Energi, Forskningsstiftelsen, 2017-01-01 -- 2021-12-31.

HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman

Vetenskapsrådet (VR), 2017-01-01 -- 2020-12-31.

Molnbaserade produkter och produktion (FiC)

Stiftelsen för Strategisk forskning (SSF), 2016-01-01 -- 2020-12-31.

INDEED

Chalmers, 2016-01-01 -- 2020-12-31.

Ämneskategorier

Datorteknik

Datavetenskap (datalogi)

Datorsystem

Styrkeområden

Informations- och kommunikationsteknik

Energi

DOI

10.1145/3274808.3274826

Mer information

Senast uppdaterat

2019-01-14