Abstract.A big data benchmark suite is needed eagerly by customers, industry and academia recently. A number of prominent works in last several years are reviewed, their characteristics are introduced and shortcomings are analyzed. The authors also provide some suggestions on building the expected benchmark, including: component based benchmarks as well as end-to-end benchmarks should be used together to test distinct tools and test the system as a whole; workloads should be enriched with complex analytics to encompass different application scenarios; metrics other than performance metrics should also be considered.
Algorithm trading techniques are adopted by institutional and individual investors with the expectation of making profit. Not only the algorithms are getting more complex, but also the data on which algorithms are running is becoming bigger, and various data are exploited to make more accurate prediction. Next generation of algorithm trading will be big data driven. This paper presents the big data processing platform for algorithm trading strategy evaluation, which will facilitate the back testing process so that algorithms could be put into production use with a high degree of confidence. Various data processing techniques should be integrated in a unified system. Some data accessing optimization techniques are also discussed.
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