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Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0. / Gong, Wenwen; Zhang, Wei; Bilal, Muhammad et al.
In: IEEE Transactions on Industrial Informatics, Vol. 18, No. 9, 01.09.2022, p. 6379-6387.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gong, W, Zhang, W, Bilal, M, Chen, Y, Xu, X & Wang, W 2022, 'Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0', IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 6379-6387. https://doi.org/10.1109/TII.2021.3133614

APA

Gong, W., Zhang, W., Bilal, M., Chen, Y., Xu, X., & Wang, W. (2022). Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0. IEEE Transactions on Industrial Informatics, 18(9), 6379-6387. https://doi.org/10.1109/TII.2021.3133614

Vancouver

Gong W, Zhang W, Bilal M, Chen Y, Xu X, Wang W. Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0. IEEE Transactions on Industrial Informatics. 2022 Sept 1;18(9):6379-6387. doi: 10.1109/TII.2021.3133614

Author

Gong, Wenwen ; Zhang, Wei ; Bilal, Muhammad et al. / Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0. In: IEEE Transactions on Industrial Informatics. 2022 ; Vol. 18, No. 9. pp. 6379-6387.

Bibtex

@article{f8c9e69c0b844ef0a1553e8c8b3645e3,
title = "Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0",
abstract = "Integrating lightweight web application programming interfaces (APIs) into mobile Apps is a promising way for quick and cost-effective development of mobile Apps with desired functions. Web APIs, on the other hand, are created by distinct enterprises or organizations, making it challenging to develop compatible and diverse mobile Apps by combining existing web APIs. It has been demonstrated that this process is an NP-hard problem. In mobile Apps development, it is often necessary to read confidential information, leading to the business privacy leakage of enterprises. Thus, we devise a novel efficient web APIs recommendation (E-WAR) approach based on locality-sensitive hashing for recommending desirable web APIs to developers. Through analyzing industrial enterprises' expected needs, E-WAR efficiently makes compatible and diverse web APIs recommendations while guaranteeing privacy protection. Finally, extensive experiments on real-world web APIs datasets are conducted. The results show that E-WAR can achieve significant performance improvements over the existing approaches.",
keywords = "App development, efficiency, Industry 4.0, privacy, web APIs recommendation",
author = "Wenwen Gong and Wei Zhang and Muhammad Bilal and Yifei Chen and Xiaolong Xu and Weizheng Wang",
year = "2022",
month = sep,
day = "1",
doi = "10.1109/TII.2021.3133614",
language = "English",
volume = "18",
pages = "6379--6387",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE Computer Society",
number = "9",

}

RIS

TY - JOUR

T1 - Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0

AU - Gong, Wenwen

AU - Zhang, Wei

AU - Bilal, Muhammad

AU - Chen, Yifei

AU - Xu, Xiaolong

AU - Wang, Weizheng

PY - 2022/9/1

Y1 - 2022/9/1

N2 - Integrating lightweight web application programming interfaces (APIs) into mobile Apps is a promising way for quick and cost-effective development of mobile Apps with desired functions. Web APIs, on the other hand, are created by distinct enterprises or organizations, making it challenging to develop compatible and diverse mobile Apps by combining existing web APIs. It has been demonstrated that this process is an NP-hard problem. In mobile Apps development, it is often necessary to read confidential information, leading to the business privacy leakage of enterprises. Thus, we devise a novel efficient web APIs recommendation (E-WAR) approach based on locality-sensitive hashing for recommending desirable web APIs to developers. Through analyzing industrial enterprises' expected needs, E-WAR efficiently makes compatible and diverse web APIs recommendations while guaranteeing privacy protection. Finally, extensive experiments on real-world web APIs datasets are conducted. The results show that E-WAR can achieve significant performance improvements over the existing approaches.

AB - Integrating lightweight web application programming interfaces (APIs) into mobile Apps is a promising way for quick and cost-effective development of mobile Apps with desired functions. Web APIs, on the other hand, are created by distinct enterprises or organizations, making it challenging to develop compatible and diverse mobile Apps by combining existing web APIs. It has been demonstrated that this process is an NP-hard problem. In mobile Apps development, it is often necessary to read confidential information, leading to the business privacy leakage of enterprises. Thus, we devise a novel efficient web APIs recommendation (E-WAR) approach based on locality-sensitive hashing for recommending desirable web APIs to developers. Through analyzing industrial enterprises' expected needs, E-WAR efficiently makes compatible and diverse web APIs recommendations while guaranteeing privacy protection. Finally, extensive experiments on real-world web APIs datasets are conducted. The results show that E-WAR can achieve significant performance improvements over the existing approaches.

KW - App development

KW - efficiency

KW - Industry 4.0

KW - privacy

KW - web APIs recommendation

U2 - 10.1109/TII.2021.3133614

DO - 10.1109/TII.2021.3133614

M3 - Journal article

AN - SCOPUS:85127878953

VL - 18

SP - 6379

EP - 6387

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

IS - 9

ER -