Architecture, Methodology and Quality – an Overview

Welcome to the European Statistical System Collaborative Network (ESSnet) Web Intelligence Network project, which began in April 2020 and will run until March 2025. This is a continuation of the previous ESSnet Big Data and ESSnet on Big Data II projects, which commenced in 2016. The aim of the network is to contribute to National Statistical Institutes (NSI) understanding the need to change and advance the production of national statistics. New technologies and data sources have tremendous potential to improve statistical production. They offer a way to generate statistics in an effective, accurate, and cost-effective manner.

ESSnet Web Intelligence Network Project

This webinar will provide the first insights of important quality aspects of web data and methodological solutions .

Trusted Smart Statistics – Web Intelligence NetworkBackgroundThe <a href="https://ec.europa.eu/eurostat/cros/WIN" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Web Intelligence Network </a>(WIN) project is creating an environment where an array of non-traditional data sources can be accessed by members of the European Statistical System (ESS) and beyond. This project works hand in hand with the Web Intelligence Hub (WIH), a platform where data is accessed. The WIH is designed to provide National Statistical Institutes (NSI) exposure to new data sets, enabling data sharing within a trusted, secure environment. New data sets collated from modern technologies can help produce statistics more cost-effectively by harnessing the power of the digital era. The WIH will harness technology and, in turn, allows collaboration to benefit the modernisation of official statistics. The WIH will create a community of collaboration to: <ul><li>Learn together</li><li>Share knowledge, data, and methods</li><li>Build a partnership between NSIs</li><li>Enable cooperation and sharing of statistical solutions for NSIs </li></ul>This webinar will provide the first insights into essential quality aspects of web data and methodological solutions for some quality issues.Starting from the architectural framework BREAL, the main benefits and challenges of dealing with web data will be analysed.For further information about the WIN project or the webinar, please contact Essnet.project@ons.gov.uk. Who will benefit from this webinar?This webinar is aimed at newcomers to projects or NSIs starting new projects in the Web Intelligence field:By the end of the course, you will have an overview of the following:<ul><li>The web data pipeline and related official statistical standards</li><li>Main architectural components of a modern data platform</li><li>Web data-specific quality aspects</li><li>Important methodological tasks</li></ul>Duration: 1 hour and 30 minsPresenters :Alexander Kowarik, Statistics Austria Piet Daas, Statistics NetherlandsMagdalena Six, Statistics AustriaGiuseppina Ruocco, Italian National Institute of StatisticsMauro Bruno, Italian National Institute of Statistics

Presenters BiographiesDr Alexander Kowarik is head of the methods unit at Statistics Austria with more than 10 years of experience working at an NSI. He is an active contributor to the R open-source community with a focus on official statistics application.Prof. Dr Piet Daas is a senior methodologist at Statistics Netherlands where he leads the research on Big Data and Artificial Intelligence. He is also a professor by special appointment on ‘Big Data in Official Statistics’ at the Eindhoven University of Technology where his work focuses on developing Big Data methodology Magdalena Six studied Applied Mathematics at the University of Salzburg and holds a PHD in Management from the University of Vienna, Faculty of Business, Economics and Statistics. She joined Statistics Austria in 2014 and has worked in the department for Quality Management and Methods since then.Giuseppina Ruocco is a technologist working at the Italian Statistical Institute, in the Methodology directorate. Currently she is responsible of a team dealing with process standardisation and analysis of statistical processes according to official statistical standards. Her main research interests are reuse of software solutions, process reproducibility and auditability. Mauro Bruno, Ph. D., Mauro Bruno is a researcher at the Italian Statistical Institute, currently he is leading the Data Scientists team in the Methodology directorate. The main research areas of the team are: i) innovative methods for accessing and analyzing Big Data sources (e.g., social media, satellite images, mobile devices sensors); ii) application of modern machine learning algorithms (e.g., Deep Learning, Word Embeddings, Natural language processing) to Big Data sources. He is involved in several research projects funded by EUROSTAT in the context of Big Data applied to Official Statistics (ESSNet Trusted Smart Surveys, ESSNet Web Intelligence Network). His main research interests are natural language processing, machine learning and official statistics standards.Privacy noticeThe data you provide will be used by the Office for National Statistics to contact you in connection with the above event and to understand the attendance level and interest in the event. ONS will store your data securely on our IT infrastructure and your data will not be shared with other organisations. Personal data will be kept until the end of the project (March 2025) and then deleted. From time to time, we will contact you to keep you up to date with new events we have planned.If you have a question about how we process your personal data and you can’t find the answer on our website, you can contact our Data Protection Officer at DPO@statistics.gov.uk or 0845 6013034. To find out more about your rights under data protection legislation, or how to raise a concern with the Information Commissioner, see our website www.ons.gov.uk/dataprotection or the Information Commissioners Office at https://ico.org.uk.Your data will be processed by Eventbrite – please read their privacy policy.

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Architecture, Methodology and Quality – an Overview

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