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A closed domain question answering (QA) dataset in statistical metadata is important to build an effective QA system about statistic. This dataset can be utilized to train or fine-tune the QA models in statistic. Further, it can also be exploited to evaluate the effectiveness of any QA methods in statistical domain. In this research, we build a new dataset of statistical metadata documents and question-answer pairs annotations of these documents in Indonesian language, called StatMetaQA (Statistical Metadata Question Answering). The collection of statistical metadata documents is used as the knowledge base of a QA system, while the collection of question-answer pairs annotations is used to train or fine-tune the QA models in statistic. The collection of statistical metadata documents, consisting of 861 statistical activity metadata documents and 1,231 statistical indicator metadata documents, was obtained from a website managed by the Statistics Indonesia (http://sirusa.bps.go.id). Next, the collection of question-answer pairs about statistical metadata, consisting of 28,863 question-answer pairs from 1,000 statistical metadata documents, was obtained using two strategies: human and automatic annotation. Here, 7353 question-answer pairs were manually annotated by human, and 21,510 question-answer pairs were automatically generated by machine using our predefined templates that were applied on some document fields of statistical metadata.

A closed domain question answering (QA) dataset in statistical metadata is important to build an effective QA system about statistic. This dataset can be utilized to train or fine-tune the QA models in statistic. Further, it can also be exploited to evaluate the effectiveness of any QA methods in statistical domain. In this research, we build a new dataset of statistical metadata documents and question-answer pairs annotations of these documents in Indonesian language, called StatMetaQA (Statistical Metadata Question Answering). The collection of statistical metadata documents is used as the knowledge base of a QA system, while the collection of question-answer pairs annotations is used to train or fine-tune the QA models in statistic. The collection of statistical metadata documents, consisting of 861 statistical activity metadata documents and 1,231 statistical indicator metadata documents, was obtained from a website managed by the Statistics Indonesia (http://sirusa.bps.go.id). Next, the collection of question-answer pairs about statistical metadata, consisting of 28,863 question-answer pairs from 1,000 statistical metadata documents, was obtained using two strategies: human and automatic annotation. Here, 7353 question-answer pairs were manually annotated by human, and 21,510 question-answer pairs were automatically generated by machine using our predefined templates that were applied on some document fields of statistical metadata., (© 2024 The Author(s).)

Metadata represents the semantic schema of the data collected over the years by an organization in order to apply the business intelligence approach. However, the metadata normally collected are not enough to facilitate knowledge discovery processes because they are conceived, primarily, for the interoperability between information systems. Research undertaken in this study confirmed the need to enrich data warehousing systems with structured meaningful metadata in order to increase the productivity and efficacy of any investigation, including data management and future business analytics. This need led us to adopt and extend the concept of “statistical metadata”. Thus, our proposed conceptual model of statistical metadata not only considers recognized standards, but also represents other additional properties. This means that our conceptual model allows increased levels of detail about the data and quality of the semantic contents.

Metadata represents the semantic schema of the data collected over the years by an organization in order to apply the business intelligence approach. However, the metadata normally collected are not enough to facilitate knowledge discovery processes because they are conceived, primarily, for the interoperability between information systems. Research undertaken in this study confirmed the need to enrich data warehousing systems with structured meaningful metadata in order to increase the productivity and efficacy of any investigation, including data management and future business analytics. This need led us to adopt and extend the concept of "statistical metadata". Thus, our proposed conceptual model of statistical metadata not only considers recognized standards, but also represents other additional properties. This means that our conceptual model allows increased levels of detail about the data and quality of the semantic contents. [ABSTRACT FROM AUTHOR]

This paper discusses a way to document the statistical editing process with process tables. Work on process tables for official economic statistics has so far focused on high level aggregates like Gross National Income (GNI), but here they are presented for every item of the Financial Accounts statistics, i.e. the financial part of the National Accounts. A more widespread use of process tables for official statistics may have been hampered by difficulties with their interpretation and by the costs of producing them. This paper first tries to give process tables a theoretical foundation by generalizing their concept to all statistics. Then it is shown how we have integrated them at the Luxembourg Central Bank (BCL) into our compilation system for the Financial Accounts statistics. However, some issues remain such as a certain arbitrariness in the definition of the starting point and the resulting limited comparability of process tables. It is argued that process tables provide both an important quality management tool for statistics compilers and a quality assessment tool for end-users. At present, more experience with process tables and feedback from users is needed to answer the question to what extent they could be disseminated to other potential users. [ABSTRACT FROM AUTHOR]

Metadata (data about data) is integral to many processes in the Statistical Knowledge Network (SKN). Recently, efforts such as the Semantic Web and others express the importance of metadata in supporting integration of information across multiple sources. Critical challenges in building the SKN are identifying what metadata is needed, and at what point in the cycle of production and use of statistical information it must be available, and establishing an architecture that supports metadata acquisition and use throughout the SKN. This article provides a research agenda in those areas, building on our current work in helping users to find the statistical information they need and understand what they find. [ABSTRACT FROM AUTHOR]

There is a growing demand for more cost-efficient production processes in Statistical Institutes. One way to address this need is to equip Statistical Information Systems (SIS) with the ability to automatically produce statistical data and metadata of high quality and deliver them to the user via the Internet. Current approaches, although provide for the storage of appropriate metadata, do not use process metadata for guiding the production process. In this paper we present an approach on creating SISs that permits metadata-guided statistical processing based on an object-based, statistical metadata model. The model is not domain specific and can accommodate both microdata and macrodata. We have equipped the model with a set of transformations that can be used to automatically manipulate data and metadata. We show the applicability of transformations with some examples using actual statistical data for R&D expenditures. Finally, we demonstrate how the presented framework can be exploited for the construction of a web site that offers ad hoc query capabilities to the users of statistical data. [ABSTRACT FROM AUTHOR]

Summary: This paper aims in summarising some of the latest results of research in the area of metadata. The modelling of metainformation using templates and object oriented models within the context of a metadata database is initially compared to the traditional way of using simple verbal footnotes. The possibility of further automating the procedures inside National Statistical Offices using metadata guided statistical processing is discussed and certain related aspects of man-machine interfaces are considered. Finally, selected topics concerning the quality of metadata and their integration inside large statistical information systems are examined. [ABSTRACT FROM AUTHOR]