Paper Info
Title
Visualizing large-scale streaming applications
Abstract
Stream processing is a new and important computing paradigm. Innovative streaming applications are being developed in areas ranging from scientific applications (for example, environment monitoring), to business intelligence (for example, fraud detection and trend analysis), to financial markets (for example, algorithmic trading systems). In this paper we describe Streamsight, a new visualization tool built to examine, monitor and help understand the dynamic behavior of streaming applications. Streamsight can handle the complex, distributed and large-scale nature of stream processing applications by using hierarchical graphs, multi-perspective visualizations, and de-cluttering strategies. To address the dynamic and adaptive nature of these applications, Streamsight also provides real-time visualization as well as the capability to record and replay. All these features are used for debugging, for performance optimization, and for management of resources, including capacity planning. More than 100 developers, both inside and outside IBM, have been using Streamsight to help design and implement large-scale stream processing applications.
Year
DOI
Venue
2009
10.1057/ivs.2009.5
Information Visualization
Keywords
Field
DocType
multi-perspective visualization,stream processing,dynamic behavior,large-scale stream processing application,algorithmic trading system,stream processing application,new visualization tool,large-scale nature,real-time visualization,adaptive nature,business intelligence,financial market,trend analysis,distributed processing
Data science,IBM,Simulation,Computer science,Visualization,Capacity planning,Ranging,Business intelligence,Stream processing,Algorithmic trading,Debugging,Distributed computing
Journal
Volume
Issue
ISSN
8
2
1473-8716
Citations 
PageRank 
References 
9
0.54
24
Authors
2
Name
Order
Citations
PageRank
Wim De Pauw140431.73
Henrique Andrade290.54