A Quality Assessment Framework for Large Datasets of Container-Trips Information
| Autor: | Michail Makridis, Enrico Checchi, José-Antonio Cotelo-Lema, Raul Fidalgo-Merino, Aris Tsois |
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| Přispěvatelé: | European Commission - Joint Research Centre [Ispra] (JRC), Khalid Saeed, Władysław Homenda, TC 8 |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Quality assessment
Computer science Supply chain Knowledge validation [SHS.INFO]Humanities and Social Sciences/Library and information sciences 02 engineering and technology Container status Data science Qualitative indicators Robustness (computer science) 020204 information systems 0202 electrical engineering electronic engineering information engineering TRIPS architecture 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Risk assessment |
| Zdroj: | Lecture Notes in Computer Science 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM) 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. pp.729-740, ⟨10.1007/978-3-319-45378-1_63⟩ Computer Information Systems and Industrial Management ISBN: 9783319453774 CISIM |
| DOI: | 10.1007/978-3-319-45378-1_63⟩ |
| Popis: | Customs worldwide are facing the challenge of supervising huge volumes of containerized trade arriving to their country with resources allowing them to inspect only a minimal fraction of it. Risk assessment procedures can support them on the selection of the containers to inspect. The Container-Trip information (CTI) is an important element for that evaluation, but is usually not available with the needed quality. Therefore, the quality of the computed CTI records from any data sources that may use (e.g. Container Status Messages), needs to be assessed. This paper presents a quality assessment framework that combines quantitative and qualitative domain specific metrics to evaluate the quality of large datasets of CTI records and to provide a more complete feedback on which aspects need to be revised to improve the quality of the output data. The experimental results show the robustness of the framework in highlighting the weak points on the datasets and in identifying efficiently cases of potentially wrong CTI records. |
| Databáze: | OpenAIRE |
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