Abstract
Reliable development of next-generation data analytics toolboxes (N-GDATs) requires robust underpinning theories, which cannot necessarily be inductively generated. Based on the axiomatic theories fusion (ATF) methodology we deductively developed a comprehensive theory supporting the development of a N-GDAT for white goods design based on middle-of-life data. Accordingly, theories about designer’s needs, advanced technologies, data analytics, creative problem-solving, decision-making, and interoperability were fused following the ATF steps: (i) selection of component theories, (ii) axiomatic discretization of foundational theories, (iii) establishing relationships among axioms and postulates, (iv) transcription of system of axiomatic propositions into a textual format, and (v) validation of explanatory theory. The obtained new theory provides a robust basis for the targeted knowledge platform. It provides (i) decision- making, (ii) algorithmic concepts, (iii) learning, (iv) data management, (v) interfacing, (vi) reasoning, (vii) data types and characteristics, (viii) design issues, (ix) analytics techniques and methods, and (x) outputs requirements to develop N- GDATs.
Original language | English |
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Title of host publication | 2024 63rd Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) |
Publisher | SICE |
Pages | 432 |
Number of pages | 6 |
Publication status | Published - 2024 |
Event | Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) - Kochi City, Japan Duration: 27 Aug 2024 → 30 Aug 2024 Conference number: 63 https://sice.jp/siceac/sice2024/ |
Conference
Conference | Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) |
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Country/Territory | Japan |
City | Kochi City |
Period | 27/08/24 → 30/08/24 |
Internet address |
Keywords
- data analytics toolbox
- axiomatization
- theories fusion
- middle-of-life data
- Data analytics