Performance linked Workflow Composition for Video Processing – An Ecological Inspiration

1. Performance linked Workflow Composition for Video Processing – An Ecological Inspiration Jessica Chen-Burger University of Edinburgh

2. 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

3. 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 !!

4. 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).

5. Source: NCHC

6. Ken-Ting National Park • Under-water surveillance Sensor Grid in Taiwan 福山 鴛鴦湖 關刀溪 塔塔加 南仁山 墾丁 Ken-Ting coral reef at Third Nuclear Power Station Adapted from Source: NCHC

7. 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.

8. 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.

9. 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 situationand 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.

10. Dynamic nature of collected video Target information is variable and un-predictable Limited expertise Untrained Grid/workflow tool users Challenges

11. 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

12. Challenges • Spectrum of quality in data • Lack of uniformity in data • Diverse user requirements

13. Opportunities • Rich processing opportunity • Long-term ecological documentary and analysis • Flexible practice that is incrementally improved over time • Semantic based annotation

14. A Workflow Design

15. Thank you for listening Images from Ken Ting National Park

16. 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.