A General Framework for Data Uncertainty and Quality Classification
| Autor: | Vanessa Simard, Nadia Lehoux, Mikael Rönnqvist, Luc LeBel |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Process (engineering) Computer science media_common.quotation_subject Supply chain 020208 electrical & electronic engineering Context (language use) 02 engineering and technology Plan (drawing) computer.software_genre 020901 industrial engineering & automation Control and Systems Engineering Order (exchange) Data quality 0202 electrical engineering electronic engineering information engineering Quality (business) Data mining computer media_common |
| Zdroj: | IFAC-PapersOnLine. 52:277-282 |
| ISSN: | 2405-8963 |
| Popis: | It is often assumed that data used to plan operations and supply chain activities is accurate. But in the presence of uncertainty, this assumption is known not to be entirely true. In this context, it becomes relevant to evaluate if a planning decision is appropriate in light of partially accurate data. This paper proposes a general framework for data analysis in order to provide a quality evaluation of the information used in the decision-making process. To this end we propose a process to quantify data quality by comparing “measured” data to “real” data. We use a hybrid approach combining multiple data quality assessment techniques as well as different alternative sources of historic data. A classification phase then rates and «tags» data for proper consideration for decision-making. Such classification provides insights into the level of uncertainty associated with the data. This paper demonstrates the approach developed using a case study from the forest sector. The approach can be adapted to other industrial sectors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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