Performance linked
Workflow Composition for
Video Processing –
An Ecological
Inspiration
Jessica Chen-Burger
University of Edinburgh
An Ecological Motivation
 An oil spill occurred at Lungkeng near
Ken-Ting (墾丁龍坑生態區 )
 the head of the Environmental Protection
Administration (EPA), Lin Jun-yi vowed
to restore it to its former condition within
2 months.
 But it is unclear as how this may be
achieved –
 There was no prior survey on the area there isn’t a basis for referring to
Lungkeng's original ecosystem prior the
oil spill.
Source: Taiwan News, http://www.etaiwannews.com/Viewpoint/2001/02/14/982136471.htm
 In addition, if there was such research data
into the area's ecology before the spill, one
could have used it as a basis to seek
insurance compensation !!
In Response
 In 1992, TERN (Taiwan long-term
Ecological Research) project, a join effort
with US NSF long-term ecological research,
were formed.
 Sponsored by Taiwanese National Science
Council (NSC).
 Wireless Sensor Nets were constructed and
managed by NCHC.
 NCHC (National Center for Highperformance Computing).
Source: NCHC
Sensor Grid in Taiwan
福山
鴛鴦湖
關刀溪
塔塔加
南仁山
Ken-Ting National Park
墾丁
Under-water surveillance
Ken-Ting coral reef at
Third Nuclear Power Station
Adapted from Source: NCHC
Objectives and Scope
of EcoGrid
• To develop a scalable observational environment that is
capable to hierarchically connect local environmental
observatories into a global one via grid and web-service
technologies.
• To enable scientific and engineering applications in
long term ecological Research (LTER) as well as
environmental hazard mitigation.
• To provide an end-to-end process from automatic
information collection to automated analysis and
documentation.
• To provide a useful feedback loop for Ecologists.
• Relevant Technology and solution:
• Self-aware and adaptive workflow composition and
management.
Challenges
 The vast amount of data available to us is of
tremendous value.
 However, how to process them efficiently and
effectively is a big challenge:
– One minute of video clip takes 1829 frames and 3.72
Mbytes;
– That is 223.2 MB per minute, 5356.8 MB per day,
and
– 1.86 Terabytes per year for one operational camera;
– Currently there are 3 under-water operational
camera.
 Human Efforts:
– Assuming one minute’s clip will need one human expert
manual processing time of 15 minutes:
– This means that for one camera and one year’s recording
will cost a human expert 15 years’ efforts just to do some
basic annotation work;
– This is a hopeless situation and automation must be
deployed in order to carry out these tasks efficiently and
effectively.
 In addition, relevant clips need to be related,
organised, classified in a sensible structure, and so
that additional properties may be further derived,
however, this is again time consuming.
Challenges
 Dynamic nature of collected video
 Target information is variable and unpredictable
 Limited expertise
 Untrained Grid/workflow tool users
Challenges
 Effective and efficient workflow
automation
 Data co-relation identification,
management and retrieval
 Presentation of information
– Rendering of images
– annotation
– co-relation with other information/clips
Challenges
 Spectrum of quality in data
 Lack of uniformity in data
 Diverse user requirements
Opportunities
 Rich processing opportunity
 Long-term ecological documentary and
analysis
 Flexible practice that is incrementally
improved over time
 Semantic based annotation
A Workflow Design
Thank you for listening
Images from Ken Ting National Park
Thank you for listening
Gayathri Nadarajan, Yun-Heh Chen-Burger,
James Malone. "Semantic-Based Workflow
Composition for Video Processing in the Grid". The
2006 IEEE/WIC/ACM International Conference on
Web Intelligence, Hong Kong, 18-22 December,
2006.