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,

 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 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 MB per minute, 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

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, December, 2006.