Requirements Engineering for Machine Learning: A Review and Reflection
Pei, Zhongyi, Liu, Lin, Wang, Chen, Wang, Jianmin
–arXiv.org Artificial Intelligence
Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. We first review the literature on requirements engineering for machine learning, and then go through the collaborative requirements analysis process step-by-step. An example case of industrial data-driven intelligence applications is also discussed in relation to the aforementioned steps.
arXiv.org Artificial Intelligence
Oct-3-2022
- Country:
- South America > Brazil (0.04)
- North America
- United States
- District of Columbia > Washington (0.04)
- Texas > Travis County
- Austin (0.04)
- New York > New York County
- New York City (0.06)
- California > San Francisco County
- San Francisco (0.14)
- Canada
- Quebec > Montreal (0.04)
- British Columbia (0.04)
- Alberta > Census Division No. 15
- Improvement District No. 9 > Banff (0.04)
- United States
- Europe
- Denmark (0.04)
- Spain > Castile and León
- Salamanca Province > Salamanca (0.04)
- Netherlands > South Holland
- Delft (0.04)
- Latvia > Riga Municipality
- Riga (0.04)
- Italy > Emilia-Romagna
- Metropolitan City of Bologna > Bologna (0.04)
- Germany > Baden-Württemberg
- Stuttgart Region > Stuttgart (0.04)
- France > Île-de-France
- Belgium > Flanders
- Flemish Brabant > Leuven (0.04)
- Asia
- Genre:
- Research Report (1.00)
- Overview (1.00)
- Industry:
- Information Technology (1.00)
- Health & Medicine (1.00)
- Energy (1.00)
- Education (1.00)
- Technology: