Extracting Understanding From Big Data to Support Supply Chain innovation
This dissertation is concerned with build an analytic infrastructure to support Big Data analytic. It describes the development and testing of an analytic infrastructure. This method is a combination of connectance concept, deduction graph and data mining technique. This model supports vividly alte...
| Main Author: | |
|---|---|
| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2013
|
| Online Access: | https://eprints.nottingham.ac.uk/26626/ |
| _version_ | 1848793212972433408 |
|---|---|
| author | Chen, Fan |
| author_facet | Chen, Fan |
| author_sort | Chen, Fan |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This dissertation is concerned with build an analytic infrastructure to support Big Data analytic. It describes the development and testing of an analytic infrastructure. This method is a combination of connectance concept, deduction graph and data mining technique. This model supports vividly alternative developing processes by the deduction graph, so the decision-makers have the clear view about the expansion of the competences sets. The analytic infrastructure provides more knowledge, skills and technologies to enhance the capability through data mining technique.
The analytic infrastructure is efficient to support decision makers by offering more alternative choices and suggesting the optimal solution. This model provides more alternative developing processes option for decision makers. This model is suitable for solving manufacturing problems. |
| first_indexed | 2025-11-14T18:56:43Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-26626 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:56:43Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-266262017-10-19T13:35:38Z https://eprints.nottingham.ac.uk/26626/ Extracting Understanding From Big Data to Support Supply Chain innovation Chen, Fan This dissertation is concerned with build an analytic infrastructure to support Big Data analytic. It describes the development and testing of an analytic infrastructure. This method is a combination of connectance concept, deduction graph and data mining technique. This model supports vividly alternative developing processes by the deduction graph, so the decision-makers have the clear view about the expansion of the competences sets. The analytic infrastructure provides more knowledge, skills and technologies to enhance the capability through data mining technique. The analytic infrastructure is efficient to support decision makers by offering more alternative choices and suggesting the optimal solution. This model provides more alternative developing processes option for decision makers. This model is suitable for solving manufacturing problems. 2013-09-19 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/26626/1/Dissertation_-_chen_fan.pdf Chen, Fan (2013) Extracting Understanding From Big Data to Support Supply Chain innovation. [Dissertation (University of Nottingham only)] (Unpublished) |
| spellingShingle | Chen, Fan Extracting Understanding From Big Data to Support Supply Chain innovation |
| title | Extracting Understanding From Big Data to Support Supply Chain innovation |
| title_full | Extracting Understanding From Big Data to Support Supply Chain innovation |
| title_fullStr | Extracting Understanding From Big Data to Support Supply Chain innovation |
| title_full_unstemmed | Extracting Understanding From Big Data to Support Supply Chain innovation |
| title_short | Extracting Understanding From Big Data to Support Supply Chain innovation |
| title_sort | extracting understanding from big data to support supply chain innovation |
| url | https://eprints.nottingham.ac.uk/26626/ |