The role of event-time order in data streaming analysis
Paper in proceedings, 2020

The data streaming paradigm was introduced around the year 2000 to overcome the limitations of traditional store-then-process paradigms found in relational databases (DBs). Opposite to DBs' "first-the-data-then-the-query" approach, data streaming applications build on the "first-the-query-then-the-data" alternative. More concretely, data streaming applications do not rely on storage to initially persist data and later query it, but rather build on continuous single-pass analysis in which incoming streams of data are processed on the fly and result in continuous streams of outputs.

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

Author

Vincenzo Gulisano

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

Dimitrios Palyvos-Giannas

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

Bastian Havers

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

Marina Papatriantafilou

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

DEBS 2020 - Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems

214-217 3404088

14th ACM International Conference on Distributed and Event-Based Systems, DEBS 2020
Montreal, Virtual, Canada,

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HARE: Self-deploying and Adaptive Data Streaming Analytics in Fog Architectures

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Subject Categories

Other Computer and Information Science

Computer Systems

Areas of Advance

Information and Communication Technology

DOI

10.1145/3401025.3404088

ISBN

9781450380287

Related datasets

DOI: 10.1145/3401025.3404088 URI: https://dl.acm.org/doi/abs/10.1145/3401025.3404088

More information

Latest update

9/1/2020 2