Tina Chun‐Fong Chiao, Kamal Nayan Agarwal, Kuang‐Fu Ma and Albert Y. Chi
This paper aims to resolve the phenomenon of the gaps between plentiful data and useful information for many manufacturing companies.
Abstract
Purpose
This paper aims to resolve the phenomenon of the gaps between plentiful data and useful information for many manufacturing companies.
Design/methodology/approach
By introducing Bayesian dynamic sampling processes, a quality process control cycle will be achieved through the sequential linkages between the involved entities; namely, user's requirement statement, target specification document, database management, and data collection.
Findings
For commercial product‐development processes, the requirement for continuous product improvement can be done by adopting a Bayesian sequential procedure. As such, industry practitioners can be able to justify whether the manufactured products have been done successfully in fulfilling target specification documents on the nano‐scale granularity level. The unsatisfactory manufacturing cases may indicate the situations of the necessity for remedy treatments of component‐wise re‐engineering processes incrementally, or for radical innovation of creating new markets through new functional capacity, based on new technical generation of the target system.
Originality/value
This paper proposes managerial quality strategic decision guidelines which may be served as an incentive for stimulating further practical industrial case studies, especially on the area of quality products assurance.
Details
Keywords
Jun Deng, Chuyi Zhong, Shaodan Sun and Ruan Wang
This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining…
Abstract
Purpose
This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method.
Design/methodology/approach
The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities.
Findings
The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations.
Originality/value
The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.