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Deep Learning for Enterprise Systems Implementation Lifecycle Challenges: Research Directions

Hassanien, Hossam El-Din, Elragal, Ahmed·2021·Informatics
DOI 10.3390/informatics8010011
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Abstract

Transforming the state-of-the-art definition and anatomy of enterprise systems (ESs) seems to some academics and practitioners as an unavoidable destiny. Value depletion lead by early retirement and/or replacement of ESs solutions has been a constant throughout the past decade. That did drive an enormous amount of research that works on addressing the problems leading to the resource drain. The resource waste had persisted throughout the ESs implementation lifecycle phases and dimensions especially post-live phases; leading to depleting the value of the social and technical dimensions of the lifecycle. Parallel to this research stream, the momentum gained by deep learning (DL) algorithms and platforms has been exponentially growing to fuel the advancements toward artificial intelligence and automated augmentation. Correspondingly, this paper is set out to present five key research directions through which DL would take part as a contributor towards the transformation of the ESs state-of-the-art. The paper reviews the ESs implementation lifecycle challenges and the intersection with DL research conducted on ESs by analyzing and synthesizing key basket journals (list of the Association of Information Systems). The paper also presents results from several experiments showcasing the effectiveness of DL in adding a level of augmentation to ESs by analyzing a large set of data extracted from the Atlassian Jira Software Issue Tracking System across different ecosystems. The paper then concludes by presenting the research directions and discussing socio-technical research courses that work on key frontiers identified within this scholarly work.

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Figure 1 Deep belief networks (DBNs) architecture [14].
Figure 1 Deep belief networks (DBNs) architecture [14].CC-BY · From the PDF
Figure 2 Deep belief networks architecture [14].
Figure 2 Deep belief networks architecture [14].CC-BY · From the PDF
Figure 3 Graphical representation of the highest adoption change (sorted by adoption change %) of deep learning platform as conducted by KDnuggets poll 2018-2019 [39].
Figure 3 Graphical representation of the highest adoption change (sorted by adoption change %) of deep learning platform as conducted by KDnuggets poll 2018-2019 [39].CC-BY · From the PDF
Figure 4 Graphical representation of the lowest adoption change (sorted by adoption change %) of deep learning platform as conducted by KDnuggets poll 2018-2019 [39].
Figure 4 Graphical representation of the lowest adoption change (sorted by adoption change %) of deep learning platform as conducted by KDnuggets poll 2018-2019 [39].CC-BY · From the PDF
Figure 5 The ESs implementation lifecycle [5].
Figure 5 The ESs implementation lifecycle [5].CC-BY · From the PDF
Figure 7 Infrastructure setup.
Figure 7 Infrastructure setup.CC-BY · From the PDF
Figure 8 Model results from the deep learning model (Siamese LSTM) used as part of the conducted experiment across the four JIRA issue tracking repositories. Please see Appendix B for the detailed graphs produced by the model upon training for 50 epochs. Full model accuracy and loss versus the four handled issue tracking system (ITS) repositories are also illustrated on Figure A1 of Appendix B.
Figure 8 Model results from the deep learning model (Siamese LSTM) used as part of the conducted experiment across the four JIRA issue tracking repositories. Please see Appendix B for the detailed graphs produced by the model upon training for 50 epochs. Full model accuracy and loss versus the four handled issue tracking system (ITS) repositories are also illustrated on Figure A1 of Appendix B.CC-BY · From the PDF
Image 8
CC-BY · From the PDF
Image 9
CC-BY · From the PDF
Image 10
CC-BY · From the PDF
Image 11
CC-BY · From the PDF
Figure A2. Python-based Keras libraries and prerequisites used for the experiment.
Figure A2. Python-based Keras libraries and prerequisites used for the experiment.CC-BY · From the PDF

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Cited by

Metadata

Authors
Hassanien, H. E.-D., & Elragal, A.
Affiliations
Department of Computer Science, Electrical and Space Engineering/Faculty of Information Systems, Luleå University of Technology, SE-971 87 Luleå, Sweden
ORCID
Venue
Informatics
Publisher
MDPI AG
Published
2021-02-20
Volume
8
Issue
1
Pages
11
ISSN
2227-9709
DOI
10.3390/informatics8010011
Cited by
5
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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