Title: A quality assessment framework for large datasets of container-Trips information
Citation: Lecture Notes in Computer Science vol. 9842 p. 729-740
Publisher: Springer Verlag
Publication Year: 2016
JRC N°: JRC101430
ISSN: 0302-9743
URI: http://link.springer.com/chapter/10.1007%2F978-3-319-45378-1_63
DOI: 10.1007/978-3-319-45378-1_63
Type: Articles in periodicals and books
Abstract: 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.
JRC Directorate:Space, Security and Migration

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