
Proceedings Paper
Beyond visualization of big data: a multi-stage data exploration approach using visualization, sonification, and storificationFormat | Member Price | Non-Member Price |
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Paper Abstract
As the sheer volume of data grows exponentially, it becomes increasingly difficult for existing visualization techniques
to keep pace. The sonification field attempts to address this issue by enlisting our auditory senses to detect anomalies or
complex events that are difficult to detect via visualization alone. Storification attempts to improve analyst
understanding by converting data streams into organized narratives describing the data at a higher level of abstraction
than the input stream that they area derived from.
While these techniques hold a great deal of promise, they also each have a unique set of challenges that must be
overcome. Sonification techniques must represent a broad variety of distributed heterogeneous data and present it to the
analyst/listener in a manner that doesn’t require extended listening – as visual “snapshots” are useful but auditory sounds
only exist over time. Storification still faces many human-computer interface (HCI) challenges as well as technical
hurdles related to automatically generating a logical narrative from lower-level data streams.
This paper proposes a novel approach that utilizes a service oriented architecture (SOA)-based hybrid visualization/
sonification / storification framework to enable distributed human-in-the-loop processing of data in a manner that makes
optimized usage of both visual and auditory processing pathways while also leveraging the value of narrative explication
of data streams. It addresses the benefits and shortcomings of each processing modality and discusses information
infrastructure and data representation concerns required with their utilization in a distributed environment. We present a
generalizable approach with a broad range of applications including cyber security, medical informatics, facilitation of
energy savings in “smart” buildings, and detection of natural and man-made disasters.
Paper Details
Date Published: 28 May 2013
PDF: 9 pages
Proc. SPIE 8758, Next-Generation Analyst, 87580K (28 May 2013); doi: 10.1117/12.2016019
Published in SPIE Proceedings Vol. 8758:
Next-Generation Analyst
Barbara D. Broome; David L. Hall; James Llinas, Editor(s)
PDF: 9 pages
Proc. SPIE 8758, Next-Generation Analyst, 87580K (28 May 2013); doi: 10.1117/12.2016019
Show Author Affiliations
Jeffrey Rimland, The Pennsylvania State Univ. (United States)
Mark Ballora, The Pennsylvania State Univ. (United States)
Mark Ballora, The Pennsylvania State Univ. (United States)
Wade Shumaker, The Pennsylvania State Univ. (United States)
Published in SPIE Proceedings Vol. 8758:
Next-Generation Analyst
Barbara D. Broome; David L. Hall; James Llinas, Editor(s)
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