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Peer-Review Record

Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model

Appl. Sci. 2019, 9(12), 2407; https://doi.org/10.3390/app9122407
by Hajo Wiemer *, Lucas Drowatzky and Steffen Ihlenfeldt
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Appl. Sci. 2019, 9(12), 2407; https://doi.org/10.3390/app9122407
Submission received: 17 April 2019 / Revised: 10 May 2019 / Accepted: 8 June 2019 / Published: 13 June 2019
(This article belongs to the Section Mechanical Engineering)

Round 1

Reviewer 1 Report

This draft presented DMME as an extension to the CRISP-DM methodology, specifically for engineering applications. The author provided a detailed description of the DMME procedure as well as an engineering example. The draft is well written and structured. However, in my opinion, the draft seems to be more likely a "technical report" rather than a scientific paper. The reviewer suggested that the authors should think about how to improve/highlight the novelty/originality and scientific contribution. For example, is there any other way to overcome this challenge associated with the CRISP-DM model without data mining method? Is that necessary to consider data mining methodology?   

Author Response

Dear reviewer,

First of all, thank you for your effort to evaluate our paper. Thank you for your valuable feedback. We have taken a few days to discuss how we can fulfil the suggestions.

In the attached document I answered each point individually.
Many greetings from Dresden.

Hajo Wiemer

Author Response File: Author Response.docx

Reviewer 2 Report

Manuscript Identification #: applsci-497659

 

Title: Data mining methodology for engineering applications (DMME) – a holistic extension to the CRISP-DM model

This paper aims to proposes DMME as an extension to the CRISP-DM methodology specifically tailored for engineering applications. It provides a communication and planning foundation for data analytics within the manufacturing domain, including the design and evaluation of the infrastructure for process-integrated data acquisition. In addition, the methodology includes functions of Design of Experiments capabilities to systematically and efficiently identify relevant interactions. Several suggestions should be considered to revise this manuscript:


1.      In the abstract section, the results and contributions of case study should brief described.

2.      In the introduction section, the motivation of this study is clear to meet the objectives of this study. However, it is necessary to identify the debates of previous studies to identify the gaps to link the objectives of this study.

3.      In the introduction section, please identify the reason for research aims. Is there any other methodology or theory? What is the pros and cons?

4.      I would suggest the author to enhance your theoretical discussion and arrives your debate or argument. Then answer several questions: Why is the topic important (or why do you study on it)? What are research questions? What has been studied? What are your contributions? Debates with proper citation support.

5.      In the Hesitant fuzzy set and fuzzy cognitive maps section, there is no recent work to describe your statement. It is not appropriate in the manuscript.

6.      The Requirements for the new methodology section looks more like descriptive summary rather than critical evaluation of previous studies. All of the requirements were mentioned in related reports. Any novel needs in industry?

7.      SEMMA and CRISP-DM have been proposed for several years but none of related studies have been mentioned.

8.      There is no Discussions and Implications section, please address it with research aims to maintain the consistency of this study.

9.      In the Conclusion section, the authors have mentioned what have been done in the work, but just sporadically mentioned in few contents about what's the main output from the work. The authors should add some brief instruction of the outputs in sentences in both section.


Author Response

Dear reviewer,


First of all, thank you for your effort to evaluate our paper. Thank you for your valuable feedback. We have taken a few days to discuss how we can fulfil the suggestions. One criticism in particular, "Science isn't good enough" or "Research design isn't clear.", is causing us headaches. Since we are not computer scientists or business computer scientists, the methods of research design are not common here. Since we have not worked according to them. Depending on how profound your criticism is meant, the changes to our paper will result. We are uncertain about this. That's why we first wrote down our opinion about your feedback. We ask them to comment on our thoughts again so that we don't have misunderstandings and the document is changed in the wrong direction.


In the attached document I answered each point individually.


Many greetings from Dresden.

Hajo Wiemer

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors have answered and revised the manuscript based on the given comments. The paper has novel approach and significant results to fit this journal’s aims. I am pleasure to see this paper publish in this journal.


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