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A Tool of Benchmarking Realtime Analysis for Massive Behavior Data

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10367))

Abstract

With the increasing development of platforms for massive users, the amount of data generated from these platforms is rapidly increasing. A large number of big data analysis frameworks have been designed to analyze data generated from these platforms. This however requires a specific benchmark to evaluate the system performance. Today, realtime analysis (or streaming analysis) becomes a hot research topic of big data research. However, there is no special benchmark designed for such streaming analysis systems. This paper introduces a tool for evaluating the performance of such streaming analysis systems. Based on the scenario of e-commerce platforms, the benchmark tool is designed using a data generator with certain user models based on the user’s habits in e-commerce platforms. A test suite is developed to be responsible for simulated mixed workloads for streaming analysis.

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References

  1. storm.apache.org

  2. Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., Tzoumas, K.: Apache flink™: stream and batch processing in a single engine. IEEE Data Eng. Bull. 38(4), 28–38 (2015)

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Acknowledgements

This work is supported by Science and Technology Project of the State Grid Corporation of China (SGBJDK00KJJS1500180) and the State Grid Information & Telecommunication Group CO., LTD. (SGITG-KJ-JSKF[2015]0010).

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Correspondence to Xiongpai Qin .

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© 2017 Springer International Publishing AG

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Teng, M., Sun, Q., Deng, B., Sun, L., Qin, X. (2017). A Tool of Benchmarking Realtime Analysis for Massive Behavior Data. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_33

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  • DOI: https://doi.org/10.1007/978-3-319-63564-4_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63563-7

  • Online ISBN: 978-3-319-63564-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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