The role of event-time order in data streaming analysis
Paper i proceeding, 2020
In contrast with traditional batch processing, data streaming applications require the user to reason about an additional dimension in the data: event-time. Numerous models have been proposed in the literature to reason about event-time, each with different guarantees and trade-offs. Since it is not always clear which of these models is appropriate for a particular application, this tutorial studies the relevant concepts and compares the available options. This study can be highly relevant for people working with data streaming applications, both researchers and industrial practitioners.
stream processing engines
data streaming
event-time
Författare
Vincenzo Gulisano
Chalmers, Data- och informationsteknik, Nätverk och system
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
DEBS 2020 - Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems
214-217 3404088
978-145038028-7 (ISBN)
Montreal, Virtual, Canada,
Molnbaserade produkter och produktion (FiC)
Stiftelsen för Strategisk forskning (SSF) (GMT14-0032), 2016-01-01 -- 2020-12-31.
AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2
VINNOVA (2019-05884), 2020-03-12 -- 2022-12-31.
HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman
Vetenskapsrådet (VR) (2016-03800), 2017-01-01 -- 2020-12-31.
Ämneskategorier
Annan data- och informationsvetenskap
Datorsystem
Styrkeområden
Informations- och kommunikationsteknik
DOI
10.1145/3401025.3404088
ISBN
9781450380287
Relaterade dataset
DOI: 10.1145/3401025.3404088 URI: https://dl.acm.org/doi/abs/10.1145/3401025.3404088