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Streamsight: a Visualization Tool for Large-scale Streaming Applications by Wim De Pauw; Henrique Andrade; Lisa Amini is a scholarly article available to read on EtoBox.

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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

Author

Wim De Pauw; Henrique Andrade; Lisa Amini

Publisher

ACM

Published

2008

Language

EN