Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

Crawled Data Analysis on Baidu API Website for Improving SaaS Platform (Short Paper)

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  • @INPROCEEDINGS{10.1007/978-3-030-12981-1_49,
        author={Lei Yu and Shanshan Liang and Shiping Chen and Yaoyao Wen},
        title={Crawled Data Analysis on Baidu API Website for Improving SaaS Platform (Short Paper)},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={2},
        keywords={SaaS (Software-as-a-Service) Baidu API Data analysis Regression Micro service},
        doi={10.1007/978-3-030-12981-1_49}
    }
    
  • Lei Yu
    Shanshan Liang
    Shiping Chen
    Yaoyao Wen
    Year: 2019
    Crawled Data Analysis on Baidu API Website for Improving SaaS Platform (Short Paper)
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-12981-1_49
Lei Yu1,*, Shanshan Liang1, Shiping Chen2, Yaoyao Wen1
  • 1: Inner Mongolia University
  • 2: Commonwealth Scientific and Industrial Research Organization
*Contact email: yuleiimu@sohu.com

Abstract

SaaS (Software-as-a-Service) is a cloud computing model, which is sometimes referred to as “on-demand software”. Existing SaaS platforms are investigated before building new distributed SaaS platform. The service data mining and evaluation on existing SaaS platforms improve our new SaaS platform. For SaaS that provide various APIs, we analysis their website data in this paper by our data mining method and related software. We wrote a crawler program to obtain data from these websites. The websites include Baidu API and ProgrammableWeb API. After ETL (Extract-Transform-Load), the obtained and processed data is ready to be analyzed. Statistical methods including non-linear regression and outlier detection are used to evaluate the websites performance, and give suggestions to improve the design and development of our API website. All figures and tables in this paper are generated from IBM SPSS statistical software. The work helps us improve our own API website by comprehensively analyzing other successful API websites.