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Zebra: A novel method for optimizing text classification query in overload scenario

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Abstract

Text classification is a crucial task in the text mining field, and it can be included in queries with user-defined functions(UDF). In many web applications, such as Twitter mining or Weibo real-time processing, when the amount of text data to be processed is enormous, there will be many overload phenomena. At the same time, when the system is overloaded, the delays in the query process can negatively affect the user experience in a streaming scenario. This paper focuses on the query with text classification on streaming data. We propose a novel method called Zebra with progressive pipelines to optimize the overload query situations. The core module of Zebra is the probabilistic filter which can reduce an incredible amount of text data based on semantic information of the query predicate. We train weak classifiers as filters using data with labels from brute-force pipelines. Next, we use a parameter search method to choose a suitable filter with the best settings and apply it to progressive pipelines. Experiments with several text workloads on real-world datasets show that Zebra can achieve higher accuracy stably while answering the query in time.

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  1. https://blog.hootsuite.com/twitter-statistics/

  2. AllenNLP library: https://allennlp.org/

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Acknowledgements

This work was mainly supported by National Natural Science Foundation of China under Grant Nos. 61732004, 62072113. This work was also supported by the Research Projects of Zhejiang Lab (No. 2021PE0AC01).

We are also grateful for the insightful comments offered by the anonymous reviewers. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors.

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Authors and Affiliations

  1. School of Software, Fudan University, 2005, Songhu Road, Shanghai, 200438, China

    Tianhuan Yu

  2. School of Computer Science, Fudan University, 2005, Songhu Road, Shanghai, 200438, China

    Zhenying He, Fei Ye, Yuankai Fan, Yinan Jing, Kai Zhang & X. Sean Wang

  3. Research Institute of Intelligent Computing, Zhejiang Lab, Yuhang District, Hangzhou, 311100, China

    Zhihui Yang

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  1. Tianhuan Yu
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Yu, T., He, Z., Yang, Z. et al. Zebra: A novel method for optimizing text classification query in overload scenario. World Wide Web 26, 905–931 (2023). https://doi.org/10.1007/s11280-022-01061-y

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