Merono Penuela, Albert

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VU Research PortalRefining Statistical Data on the WebMerono Penuela, Albert2016document versionPublisher's PDF, also known as Version of recordLink to publication in VU Research Portalcitation for published version (APA)Merono Penuela, A. (2016). Refining Statistical Data on the Web.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 27. May. 2021R E F I N I N G  S TAT I S T I C A L  D ATA O N  T H E  W E BREFINING STATISTICAL DATA ON THE WEBA L B E R T M E R O Ñ O  P E Ñ U E L AALBERT MEROÑO PEÑUELAThe Web has grown into a heterogeneous open data space of interlinked documents, tables, and databases. Analyzing these datasets in knowledge discovery processes requires careful data preparation, which usually takes 60% of the total time spent. Statistical data, which are data subject to analysis by statistical methods, show a number of problems making their preparation difficult: legacy and hardly accessible tabular formats, disturbing data errors, and non-reusable data curation procedures are some examples. In addition, historical statistical data are only partially preserved, and their harmonization is very poor. Today, these issues are only addressed with inefficient and non-reproducible data munging.This thesis contributes solutions to these problems, taking advantage of Semantic Web technologies, and using the domain of Social and Economic History as a case study. Concretely, its contributions are: · A thorough study of data preparation and integration requirements in Social Science and History · A semi-automatic approach that combines human expertise with scalable automation to convert legacy statistical collections to 5-star Linked Data · A novel metric to quantify the predictability of diachronic Web schemas · A Web-friendly way of expressing statistical data validation procedures (or “edit rules”) in SPARQL, the RDF query language · An easy way of extending the functionality of SPARQL by leveraging federationThese contributions are a basis for bringing a more efficient data preparation and refinement to the Web in quantitative History, the Digital Humanities, and Science.

Refining Statistical Data on the Web

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Refining Statistical Data on the Web

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