What is it about?
Data Stream Processing has emerged as the reference paradigm for the analysis of continuous information flows, which have often to be processed with low-latency requirements. Dealing with unbounded data flows, Data Stream Processing applications are typically long-running and, thus, likely experience varying working conditions over time. We review the most relevant strategies for run-time adaptation of Data Stream Processing systems and applications. Our analysis identifies current research trends as well as open challenges that will motivate further investigations in this field.
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Why is it important?
Our analysis identifies current research trends as well as open challenges on the adaptation of Data Stream Processing systems and applications.
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This page is a summary of: Runtime Adaptation of Data Stream Processing Systems: The State of the Art, ACM Computing Surveys, January 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3514496.
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