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Applications Realtime medical analysis (swine flu outbreak) [2]

Real-time MultiAgent Data Mining Cuong Tong M InfSci Supervisors: Prof Dharmendra Sharma, Dr Fariba Shadabi. Approach: MultiAgent + Data Mining [1]. Introduction. Applications Realtime medical analysis (swine flu outbreak) [2]

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Applications Realtime medical analysis (swine flu outbreak) [2]

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  1. Real-time MultiAgent Data MiningCuong Tong M InfSci Supervisors: Prof Dharmendra Sharma, Dr Fariba Shadabi Approach: MultiAgent + Data Mining [1] Introduction • Applications • Realtime medical analysis (swine flu outbreak) [2] • Stream Mining in Sensor Networks (imagine millions of nodes generating data 24/7) • Astronomical and geophysical sensor data analysis • E-Commerce: weblog and click stream analysis Large Central Data Source • Huge volume of data • Building model takes long time (weeks, months) or not even feasible. • Data grows exponentially • Rebuilding model can be impractical. • MultiAgent + Data Mining • Emerging research area • Complements each other Research Challenges • Memory constraint • Time bound • Data Mining is challenging • Mining Data Stream is even more challenging • Concept drift • High Rate of data arrival • Efficiency/Accuracy • Conclusion • We look at how MultiAgent can • mine large dataset. • synthesise distributed knowledge. • mine data stream in realtime with adaptive sliding windows size. • collaborate to carry out common goal. • learn from each other. Distributed Data Sources References : [1] Cao, Long Bing 2007: Agent-Mining Interaction and Integration Tutorial WI/AIT 07 [2] Realtime data vital in swine flu fight http://www.australianit.news.com.au/story/0,25197,25429057-15306,00.html Faculty of ISE Research Student Colloquium 2009

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