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Streamsight: a visualization tool for large-scale streaming applications
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Software Visualization archive
Proceedings of the 4th ACM symposium on Software visualization table of contents
Ammersee, Germany
SESSION: Joint paper session with VL/HCC table of contents
Pages 125-134  
Year of Publication: 2008
ISBN:978-1-60558-112-5
Authors
Wim De Pauw  IBM T.J. Watson Research Center, Hawthorne, NY
Henrique Andrade  IBM T.J. Watson Research Center, Hawthorne, NY
Lisa Amini  IBM T.J. Watson Research Center, Hawthorne, NY
Sponsors
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGSOFT: ACM Special Interest Group on Software Engineering
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGCHI : Specialist Interest Group in Computer-Human Interaction of the ACM
Publisher
ACM  New York, NY, USA
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ABSTRACT

Stream processing is becoming a new and important computing paradigm. Innovative streaming applications are being developed in areas ranging from scientific applications (e.g., environment monitoring), to business intelligence (e.g., fraud detection and trend analysis), to financial markets (e.g., algorithmic trading strategies). Developing, understanding, debugging, and optimizing streaming applications is non-trivial because of the adaptive and dynamic nature of these applications. The sheer complexity and the distributed character of a large number of cooperating components hosted on a distributed environment further complicate matters. In this paper we describe Streamsight, a new visualization tool built to examine, monitor, and help understand the dynamic behavior of streaming applications. Previously developed stream processing visualization tools focus solely on composition of dataflow graphs. Streamsight's novelty hinges on a wide range of capabilities, including the ability to manage the dynamics of large and evolving topologies comprising multiple streaming applications with thousands of nodes and interconnections. From rendering live performance counters using different perspectives to allowing recordings and replays of the execution process, Streamsight provides the mechanisms that permit a better understanding of the evolving and adaptive behavior of streaming applications. These capabilities are used for debugging purposes, for performance optimization, and management of resources, including capacity planning. More than 50 developers, both inside and outside IBM, have been using Streamsight.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Wim De Pauw: colleagues
Henrique Andrade: colleagues
Lisa Amini: colleagues