Complex Event Computation System and Platform for Uncertain Data
20160115 - 20170714
Dr James Zhibin Lei
1.1 Platform requirement and system architecture; 1.2 Complex event computation infrastructure and platform design 1.2.1 Event stream framework and processing module 1.2.2 System modules and tools – real-time database optimization, Java libraries (chart, multiple sorting), cross-language messaging, high-performance communication and caching module 1.3 Basic quantitative analysis and statistical tools 1.4 Extensible API for supporting outside applications 2.1 Hybrid CEC engine supporting explicit and implicit event modelers 2.1.1 Quantitative analysis and application modules 2.1.2 Statistical toolbox and supporting modules for hybrid model 2.2 Quantitative analysis, optimization, and estimation algorithms for Parametric Modeling 3.1 Built-in stream data infrastructure supporting efficient stream statistical package 3.2 Tools and modules for financial big data applications 3.3 General-purpose, distributed, scalable, fault-tolerant, pluggable platform for processing continuous unbounded streams of data
Dr Andrew Yiu-Wing Wat Dr Jerome Yen Mr Yu Liu Dr Kent Kang Heng Wu Mr Cheuk Lun, Calvin Cheung Dr Yang Liu Mr Xiaoyu Zhao Mr Amir Shiyu Liao Mr Mengte, Matt Miao Mr Carlos Chiu Mr Zuyao Wang Mr Wai Cheong Ku Mr Jiqi, Jacky Zhang Dr Man Yau, Edmond Chiu
Genius Union Limited (Licensing) [Sponsor] Nebula Technology Ltd (Licensing) [Sponsor] t.Axiom Solutions Ltd (Inkind) [Sponsor] t.Axiom Solutions Ltd (Licensing) [Sponsor]
Complex Event Computation System and Platform for Uncertain Data Project focuses on developing an efficient, scalable and high performance computational engine and its data infrastructure platform to enable new and innovative products, services, and applications that deal with big data with uncertainties. The project aims to build the core complex data platform with high performance data process and explicit/implicit event modeling, low latency stream data analysis, distributed statistical computation, optimal execution, and effective risk management to enable partners, e.g. quantitative talents in academics and industry to build, develop, and commercialize new products and applications on a common cost-effective platform.