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MapReduce is a popular framework for large-scale data analysis. As data access is critical for MapReduce’s performance, some recent work has applied different storage models, such as column-store or PAX-store, to MapReduce platforms. However, the data access patterns of different queries are very different. No storage model is able to achieve the optimal performance alone. In this paper, we study...
MapReduce has shown vigorous vitality and penetrated both academia and industry in recent years. MapReduce not only can be used as an ETL tool, it can do even much more. The technique has been applied to SQL summation, OLAP, data mining, machine learning, information retrieval, multimedia data processing, science data processing etc. Basically MapReduce is a general purpose parallel computing framework...
In algorithm trading, computer algorithms are used to make the decision on the time, quantity, and direction of operations (buy, sell, or hold) automatically. To create a useful algorithm, the parameters of the algorithm should be optimized based on historical data. However, Parameter optimization is a time consuming task, due to the large search space. We propose to search the parameter combination...
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