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Data-driven Approximation

Approximate algorithms enable to solve NP problems. Traditional approximation algorithm theory blindly pursues an approximation algorithm to reduce complexity in design time. It may spent plenty of time while the effect is disappointing. We reach for a quantum leap from traditional approximate computing and heads strongly towards data-driven approximate algorithms and theories. The ultimate results are to provide small and medium-sized enterprises with accurate and efficient queries on big data despite limited hardware resources.
 

Research Areas

Based on the idea of ​​transforming big data into small data, we research the design method of data-driven approximation algorithm and design the approximation algorithm. Finally, we extend the data-driven approximation algorithm theory for optimization problems to different types of query languages/problems based on the query’s data-driven approximation algorithm theory. In the case of limited big data resources, real-time analysis can be provided.
 

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