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

Authors :
Lei Yu
Yaoyao Wen
Shiping Chen
Shanshan Liang
Source :
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783030129804, CollaborateCom
Publication Year :
2019
Publisher :
Springer International Publishing, 2019.

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.

Details

ISBN :
978-3-030-12980-4
ISBNs :
9783030129804
Database :
OpenAIRE
Journal :
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783030129804, CollaborateCom
Accession number :
edsair.doi...........019082cebc43635ab2a56f8b66e11036
Full Text :
https://doi.org/10.1007/978-3-030-12981-1_49