Performance linked Workflow Composition for

Video Processing – An Ecological

Inspiration

Jessica Chen-BurgerJessica Chen-BurgerUniversity of EdinburghUniversity 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 High-performance Computing).

Source: NCHC

Sensor Grid in Taiwan

福山

關刀溪

鴛鴦湖

南仁山

塔塔加

Ken-Ting coral reef at Third Nuclear Power Station Adapted from Source: NCHC

墾丁 Ken-Ting National Park

Under-water surveillance

Objectives and Scopeof 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 un-

predictable 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

Images from Ken Ting National Park

Thank you for listening

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.