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Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data

Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data

Bo Jiang, Junwu Chen, Ye Wang, Liping Zhao, Pengxiang Liu
Copyright: © 2020 |Volume: 1 |Issue: 1 |Pages: 22
ISSN: 2644-1675|EISSN: 2644-1683|EISBN13: 9781799809777|DOI: 10.4018/IJBDIA.2020010101
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MLA

Jiang, Bo, et al. "Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data." IJBDIA vol.1, no.1 2020: pp.1-22. http://doi.org/10.4018/IJBDIA.2020010101

APA

Jiang, B., Chen, J., Wang, Y., Zhao, L., & Liu, P. (2020). Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data. International Journal of Big Data Intelligence and Applications (IJBDIA), 1(1), 1-22. http://doi.org/10.4018/IJBDIA.2020010101

Chicago

Jiang, Bo, et al. "Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data," International Journal of Big Data Intelligence and Applications (IJBDIA) 1, no.1: 1-22. http://doi.org/10.4018/IJBDIA.2020010101

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Abstract

In recent years, business process re-engineering has played an important role in the development of large-scale web-based applications. To re-engineer business processes, business services are developed and coordinated by reusing a set of open APIs and services on the internet. Yet, the number of services on the internet has grown drastically, making it difficult for them to be discovered to support the changing business goals. One major challenge is therefore to search for a suitable service that matches a specific business goal from a large number of available services in an efficient and effective manner. To address this challenge, this paper proposes a deep learning approach for massive service discovery. The approach, thus called MassRAFF, employs a combination of the recurrent attention and feature fusion methods. This paper first presents the MassRAFF approach and then reports on an experiment for evaluating this approach. The experimental results show that the MassRAFF approach has performed reasonably well and has potential to be improved further in future work.

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