Workflow Enactment in the EcoGrid

From several informal scholar exchanges, National Center for High-performance Computing (NCHC), Taiwan, and Artificial Intelligence Applications Institute (AIAI), the University of Edinburgh have forged an unique and ambitious research collaboration and becoming the first British-Taiwanese collaboration in Grid Computing.

Recognising the needs for long-term ecological monitoring and planning, NCHC has installed and managed Wireless Sensor Nets in several national parks in Taiwan. The information collected are stored in and made available through EcoGrid for access. The information collected includes surveillance videos in the Fu-Shan National Park covering the entire area for observing natural lives and protecting them from potential poachers; audio recording of frogs of rare species; under-sea coral reef and marine life observation stations and more. Due to the continuous and non-intrusive methods deployed, such monitoring and recording efforts have already made ecological discoveries of significant importance that traditional methods otherwise could not have made.

Continuous data collection in the EcoGrid, however, poses a great challenge as how this data may be transformed into useable information for the ecologists and in a timely fashion. For instance, one minute of video clip typically takes 1829 frames and is stored in 3.72 Mbytes. That translates into 223.2 MB per minute, 5356.8MB per day and 1.86 Terabytes per year for one operational camera, and due to the unpredictability of nature, one may not easily skip frames as they may contain vital information. Based on our own experience, one minute's clip will on average cost manual processing time of 15 minutes. This means that one year's recording of a camera would cost human experts 15 years' effort just to perform basic analysing and classifying tasks. Currently there are three under water cameras in operation and this will cost a human expert 45 years' time just to do basic processing task. This is clearly a hopeless situation and more appropriate automation methods must be deployed.

Based on its expertise in workflow and planning, AIAI provides a semantic-based workflow method that enables a self-generative automation for carrying integration and analytical tasks. This method not only allows us to describe what the initial execution steps in the workflow, it also allows us to dynamically monitor and modify workflow at run-time. Our initial work will be published in the conference of CNNA in Taiwan in May 2005 [1].

AIAI's next aim is to provide a hybrid method that enables a self-learned workflow enactment. Planning techniques are used as a basis to determine which procedural steps is to take next according to the environment and problems encountered, it would also learn from past experiences and self-adapt when a chosen method does not work.


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Last updated: Mon Oct 22 22:53:31 2007