Paper Info
Title
Scalable Visualization and Interactive Analysis Using Massive Data Streams.
Abstract
Historically, data creation and storage has always outpaced the infrastructure for its movement and utilization. This trend is increasing now more than ever, with the ever growing size of scientific simulations, increased resolution of sensors, and large mosaic images. Effective exploration of massive scientific models demands the combination of data management, analysis, and visualization techniques, working together in an interactive setting. The ViSUS application framework has been designed as an environment that allows the interactive exploration and analysis of massive scientific models in a cache-oblivious, hardware-agnostic manner, enabling processing and visualization of possibly geographically distributed data using many kinds of devices and platforms. For general purpose feature segmentation and exploration we discuss a new paradigm based on topological analysis. This approach enables the extraction of summaries of features present in the data through abstract models that are orders of magnitude smaller than the raw data, providing enough information to support general queries and perform a wide range of analyses without access to the original data.
Year
DOI
Venue
2012
10.3233/978-1-61499-322-3-212
CLOUD COMPUTING AND BIG DATA
Keywords
Field
DocType
Visualization,data analysis,topological data analysis,Parallel I/O
Topological data analysis,Data stream mining,Interactive analysis,Computer graphics (images),Visualization,Computer science,Parallel I/O,Database,Distributed computing,Scalability
Conference
Volume
ISSN
Citations 
23
0927-5452
2
PageRank 
References 
Authors
0.38
0
7
Name
Order
Citations
PageRank
Valerio Pascucci13241192.33
Peer-Timo Bremer2144682.47
Attila Gyulassy345323.11
Giorgio Scorzelli41377.38
Cameron Christensen5193.50
Brian Summa61126.84
Sidharth Kumar7346.79