Aurora: a new model and architecture for data stream management

By Daniel J. Abadi, Donald Carney, Ugur Çetintemel, Mitch Cherniack, Christian Convey, Sangdon Lee, M. Stonebraker, Nesime Tatbul, S. Zdonik. The VLDB journal, 2003

650 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/395832041fab91e241f7c65c2a4f301058a6ad08

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In Computer Networks and Communications

In Signal Processing

Citations in the corpus

The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing (2015, background): “We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted,…”
Distributed data stream processing and edge computing: A survey on resource elasticity and future directions (2017, methodology): “rning query response time and memory utilisation. STREAM uses a chain scheduling technique that aims to minimise memory usage and adapt its execution to variations in data arrival rate [49]. 6 Aurora [50] was designed for managing data streams generated by monitoring applications. Similar to STREAM,”
Online Machine Learning in Big Data Streams (2018, methodology): “varying, possibly unpredictable, and unbounded streams appears to yield some fundamentally new research problems. Examples of such applications include ﬁnancial applications [241], network monitoring [18, 1], security, sensor networks [66], Twitter analysis [25], and more [83]. Traditional data proc”
A catalog of stream processing optimizations (2014, methodology): “Instead of specifying the stream graph textually in a language, some systems, such as Aurora, provide a visual environment for that [Abadi et al. 2003].”