Implementing machine learning in an enterprise involves tackling a wide range of complexities with respect to requirements elicitation, design, development, and deployment of such solutions. Despite the necessity and relevance of requirements engineering approaches to the process, not much research has been done in this area. This paper employs a case study method to evaluate the expressiveness and usefulness of GR4ML, a conceptual modeling framework for requirements elicitation, design, and development of machine learning solutions. Our results confirm that the framework includes an adequate set of concepts for expressing machine learning requirements and solution design. The case study also demonstrates that the framework can be useful in machine learning projects by revealing new requirements that would have been missed without using the framework, as well as, by facilitating communication among project team members of different roles and backgrounds. Feedback from study participants and areas of improvement to the framework are also discussed.
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Interestingly, recent research in the healthcare domain also supports the idea that enrolling the wrong doctors into government programs can be a contributing factor toward failure of such programs. These research publications were unknown to the modelers and project team during the Business View modeling.
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We wish to thank the anonymous reviewer #1 for her/his valuable comments, especially for suggesting to highlight the centrality of the Insight modeling elements as a link between the three modeling views.
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Appendix A : List of prompting questions for constructing models in the framework
|Constructing Business View models|
|•What are the key business strategies in your domain of interest?|
|•Who is responsible for/aim to achieve those goals?|
|•How are they achieving this? How else can we achieve this?|
|•Why are they doing this?|
|•What are the key performance indicators in this context?|
|•How would you measure how well you are achieving those goals?|
|•What are the business decision(s) that need analytics (or data-driven) support? Who are those decision makers?|
|•Why would they need to make such decisions? Which business goal is each decision part of? Which business (routine) process is this decision part of?|
|•What is the frequency of each decision (how often)?|
|•What would the decision maker(s) need to know during the decision processes?|
|•What are the questions that come to their mind (and they need to have an answer for) during their decision making activities?|
|•For each question, if it is too broad, can you break it into sub-questions?|
|•Specify the tense (past, present, or future), and frequency (how often) of the questions|
|•From the given list, specify what kinds of answers are needed for each of the business questions? Predictive model, groupings of the data (segments), probability model, diagram (visualization), or logical rules|
|•For each of the above, specify the Input, Output, Usage Frequency, Update Frequency, and Learning Period of the machine learning model|
|Constructing Analytics View models|
|•What kind of analytics (descriptive, predictive, or prescriptive) would be appropriate to generate required insights?|
|•What algorithm(s) exist for fulfilling the analytics goal at hand?|
|•What are the quality attributes or non-functional requirements (NFRs) are critical for users?|
|•What numeric metrics would be used to compare/evaluate the algorithms?|
|•Define the threshold (upper or lower) values for indicators (e.g., minimum required accuracy for predictive models)|
|•How are the critical NFRs influenced by alternative algorithms?|
|Constructing Data Preparation View models|
|•What kind of data would be relevant for generating the insights and answering the business question at hand?|
|•What data attributes (i.e., features), in what format, and aggregation level are needed for the question goals under consideration?|
|•Where is the data stored, and what is data schema (i.e., entities and relationships)?|
|•Explain, to best of your understanding, the attributes, format, and size of the dataset at hand|
|•For each attributes, what is the data types, aggregation level, and selection of records (filtering)?|
|•What (sequence of) integration, cleaning, aggregation, filtering, and other data preparations are needed for transforming the raw data tables into the prepared data tables?|
|•Are there any data quality concerns?|
Appendix B: Questionnaire used for collecting feedback in post-modeling interviews
[Q1] At the end of modeling sessions, were the modelers able to arrive at a characterization of your existing analytics solution/product?
If your answer is NO, please explain what aspects/parts/components of your product/solution were not identified at the end of modeling sessions.
If your answer is YES, please provide 2–3 sentences on which area of the graphical models correspond to which part of your product.
[Q2] Through the course of this collaboration, were there any instances of understandings or findings that you and your team were not able to arrive at that prior to the modeling activities? Please provide 2–3 examples.
[Q3] What did you find useful about the framework? (Write 3–4 sentences or bullet points). This can include specific modeling language features or methodological steps, as well as the general approach.
[Q4] What do you think is most lacking in the framework? Are there additions to or variations on the framework that you would like to see?
[Q5] Provide 2–3 examples of features that are not part of current your product/solution, but after the modeling sessions, you think that they can be fruitful additions.
[Q6] What are the aspects or features of the framework that you consider least useful? (This can include modeling language features as well as methodological steps.)
[Q7] In arriving at your current analytics solution/product, you had evolved the product conception and design through one or more iterations in the past. Retrospectively, do you think using the modeling framework would have enabled you to arrive at a viable product more easily or sooner?, e.g., in uncovering pain points and analyzing failure stories and scenarios, and in providing guidance and focus in the search for solutions.
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Nalchigar, S., Yu, E. & Keshavjee, K. Modeling machine learning requirements from three perspectives: a case report from the healthcare domain. Requirements Eng (2021). https://doi.org/10.1007/s00766-020-00343-z
- Conceptual modeling
- Requirements engineering
- Machine learning
- Data analytics
- Health care