Missing the logic of the text: Lord Butler’s report on intelligence on Iraqi weapons of mass destruction

This article focuses on the final report of Lord Butler’s review of British intelligence on weapons of mass destruction (WMD), specifically on its treatment of the accuracy of the use of intelligence on Iraqi WMD in a government dossier published in September 2002 ahead of the 2003 Iraq war. In the report, the demonstration of the accuracy of the “September Dossier” hinges on the insertion of tables that compare side-by-side quotations from this document and from intelligence assessments. The analysis of the textual and visual methods by which the report is written reveals how the logic of the comparative tables is missed in the Butler report: the logic of these tables requires that the comparison between quotations from the two documents should be performed at the level of their details but the Butler report performs its comparison only at a broad and general level.

Improving aircraft maintenance, repair, and overhaul: A novel text mining approach

Aircraft Maintenance, Repair and Overhaul (MRO) feedback commonly includes an engineer’s complex text-based inspection report. Capturing and normalizing the content of these textual descriptions is vital to cost and quality benchmarking, and provides information to facilitate continuous improvement of MRO process and analytics. As data analysis and mining tools requires highly normalized data, raw textual data is inadequate. This paper offers a textual-mining solution to efficiently analyse bulk textual feedback data. Despite replacement of the same parts and/or sub-parts, the actual service cost for the same repair is often distinctly different from similar previously jobs. Regular expression algorithms were incorporated with an aircraft MRO glossary dictionary in order to help provide additional information concerning the reason for cost variation. Professional terms and conventions were included within the dictionary to avoid ambiguity and improve the outcome of the result. Testing results show that most descriptive inspection reports can be appropriately interpreted, allowing extraction of highly normalized data. This additional normalized data strongly supports data analysis and data mining, whilst also increasing the accuracy of future quotation costing. This solution has been effectively used by a large aircraft MRO agency with positive results.