dc.citation.conferencePlace |
US |
- |
dc.citation.conferencePlace |
Washington |
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dc.citation.endPage |
1029 |
- |
dc.citation.startPage |
1024 |
- |
dc.citation.title |
2014 IEEE International Conference on Big Data, IEEE Big Data 2014 |
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dc.contributor.author |
Song, Minseok |
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dc.contributor.author |
Yang, Hanna |
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dc.contributor.author |
Park, Minjeong |
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dc.contributor.author |
Cho, Minsu |
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dc.contributor.author |
Kim, Seongjoo |
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dc.date.accessioned |
2023-12-19T23:10:12Z |
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dc.date.available |
2023-12-19T23:10:12Z |
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dc.date.created |
2015-02-26 |
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dc.date.issued |
2014-10-27 |
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dc.description.abstract |
Interests in manufacturing process management and analysis are increasing, but it is difficult to conduct process analysis due to the increase of manufacturing data. Therefore, we suggest a manufacturing data analysis system that collects event logs from so-called big data and analyzes the collected logs with process mining. There are two kinds of big data generated from manufacturing processes, structured data and unstructured data. Usually, manufacturing process analysis is conducted by using only structured data, however the proposed system uses both structured and unstructured data for enhancing the process analysis results. The system automatically discovers a process model and conducts various performance analysis on the manufacturing processes. |
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dc.identifier.bibliographicCitation |
2014 IEEE International Conference on Big Data, IEEE Big Data 2014, pp.1024 - 1029 |
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dc.identifier.doi |
10.1109/BigData.2014.7004336 |
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dc.identifier.scopusid |
2-s2.0-84988240403 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/46702 |
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dc.identifier.url |
http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?arnumber=7004336 |
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dc.language |
영어 |
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dc.publisher |
Institute of Electrical and Electronics Engineers Inc. |
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dc.title |
A system architecture for manufacturing process analysis based on big data and process mining techniques |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2014-10-27 |
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