Linked Open Statistical Data Research Articles

Open Spatiotemporal Data Warehouse for Agriculture Production Analytics

Business Intelligence (BI) technology with Extract, Transform, and Loading process, Data Warehouse, and OLAP have demonstrated the ability of information and knowledge generation for supporting decision making. In the last decade, the advancement of the Web 2.0 technology is improving the accessibility of web of data across the cloud. Linked Open Data, Linked Open Statistical Data, and Open Government Data is increasing massively, creating a more significant computer-recognizable data available for sharing. In agricultural production analytics, data resources with high availability and accessibility is a primary requirement. However, today’s data accessibility for production analytics is limited in the 2 or 3-stars open data format and rarely has attributes for spatiotemporal analytics. The new data warehouse concept has a new approach to combine the openness of data resources with mobility or spatiotemporal data in nature. This new approach could help the decision-makers to use external data to make a crucial decision more intuitive and flexible. This paper proposed the development of a spatiotemporal data warehouse with an integration process using service-oriented architecture and open data sources. The data sources are originating from the Village and Rural Area Information System (SIDeKa) that capture the agricultural production transaction in a daily manner. This paper also describes the way to spatiotemporal analytics for agricultural production using a new spatiotemporal data warehouse approach. The experiment results, by executing six relevant spatiotemporal query samples on DW with fact table contains 324096 tuples with temporal integer/float for each tuple, 4495 tuples of field dimension with geographic data as polygons, 80 tuples of village dimension, dozens of tuples of the district, subdistrict, province dimensions. The DW time dimension contains 3653 tuples representing a date for ten years, proved that this new approach has a convenient, simple model, and expressive performance for supporting executive to make decisions on agriculture production analytics based on spatiotemporal data. This research also underlines the prospects for scaling and nurturing the spatiotemporal data warehouse initiative.

Linked Open Statistical Data: Relevance and Prospects

After a detailed argumentation of the study’s relevance, this article discusses the prospects for introducing the concept of linked open statistics produced within the framework of a single information environment that ensures efficient production, dissemination, and reuse of statistical and administrative data. The implementation of this qualitatively new concept based on technological innovations and aimed to meet rapidly growing user demands is a key task of digital transformation, defined by the Government of the Russian Federation in the field of official statistics. The major part of open data concerns statistics such as demographic, economic and social indicators. Describing and presenting them in the form of linked open statistics sets an important background for accelerating socio-economic development by introducing new socially significant state, municipal, non-commercial and commercial services/products.Linked Open Statistical Data (LOSD) allows performing analysis based on a coordinated, integrated information environment as an alternative to using disparate and often controversial data sets. National statistical institutes and government bodies in many countries, together with international organizations, have already chosen the paradigm of linked open statistics. The authors discuss the advantages of this approach, as well as its practical application in international projects.The article presents the examples and best practices of linked open statistics in a number of publications and strategic documents within the European Statistical System. It also shows the constraints of the linked open statistics development due to the lack of accessible ontologies and standards - the extensions necessary to meet the requirements for classification and management of various concepts in statistics domain. The analysis of projects and initiatives carried out in the article reflects the possibilities and prospects of solving this problem in the field of state statistics. The authors formulate a set of recommendations based both on the analysis of international practice and on the results of their own development experience within the research project «Center of Semantic Integration».

Interoperability Conflicts in Linked Open Statistical Data

An important part of Open Data is of a statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analyzing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that could result in valuable services and data products. However, it is still difficult to discover and combine Open Statistical Data that reside in different data portals. Although Linked Open Statistical Data (LOSD) provide standards and approaches to facilitate combining statistics on the Web, various interoperability challenges still exist. In this paper, we propose an Interoperability Framework for LOSD, comprising definitions of LOSD interoperability conflicts as well as modelling practices currently used by six official open government data portals. Towards this end, we combine a top-down approach that studies interoperability conflicts in the literature with a bottom-up approach that studies the modelling practices of data portals. We define two types of LOSD schema-level conflicts, namely naming conflicts and structural conflicts. Naming conflicts result from using different URIs. Structural conflicts result from different practices of modelling the structure of data cubes. Only two out of the 19 conflicts are currently resolved and 11 can be resolved according to literature.

On modeling linked open statistical data

A major part of Open Data concerns statistics such as economic and social indicators. Statistical data are structured in a multidimensional manner creating data cubes. Recently, National Statistical Institutes and public authorities adopted the Linked Data paradigm to publish their statistical data on the Web. Many vocabularies have been created to enable modeling data cubes as RDF graphs, and thus creating Linked Open Statistical Data (LOSD). However, the creation of LOSD remains a demanding task mainly because of modeling challenges related either to the conceptual definition of the cube, or to the way of modeling cubes as linked data. The aim of this paper is to identify and clarify (a) modeling challenges related to the creation of LOSD and (b) approaches to address them. Towards this end, nine LOSD experts were involved in an interactive feedback collection and consensus-building process that was based on Delphi method. We anticipate that the results of this paper will contribute towards the formulation of best practices for creating LOSD, and thus facilitate combining and analyzing statistical data from diverse sources on the Web.

On Modeling Linked Open Statistical Data

A major part of Open Data concerns statistics such as economic and social indicators. Statistical data are structured in a multidimensional manner creating data cubes. Recently, National Statistical Institutes and public authorities adopted the Linked Data paradigm to publish their statistical data on the Web. Many vocabularies have been created to enable modeling data cubes as RDF graphs, and thus creating Linked Open Statistical Data (LOSD). However, the creation of LOSD remains a demanding task mainly because of modeling challenges related either to the conceptual definition of the cube, or to the way of modeling cubes as linked data. The aim of this paper is to identify and clarify (a) modeling challenges related to the creation of LOSD and (b) approaches to address them. Towards this end, nine LOSD experts were involved in an interactive feedback collection and consensus-building process that was based on Delphi method. We anticipate that the results of this paper will contribute towards the formulation of best practices for creating LOSD, and thus facilitate combining and analysing statistical data from diverse sources on the Web.

Towards a Methodology for Publishing Linked Open Statistical Data

The number of open government initiatives and directives around the globe with focused interest on publishing large amounts of data on the Web as “open” is increasing rapidly in the recent years. Opening up data aims for citizens, scientists and organizations to easily access, discover and exploit the data and consequently to benefit out of them. As a result, there has been an emerging need of integrating and representing those data in transparent and reusable ways, with high degree of interoperability which will further facilitate the discovery of new connections and insights by linking data coming from disperse sources. Statistical data published either by government bodies or by national statistical authorities are used for policy and decision making purposes, as they present important socioeconomic indicators. In this paper, we present a generic methodology describing the basic steps and overall model to publish statistical data coming from tabular data sources or relational databases in the form of Linked Open Data.

Towards Linked Open Statistical Data: Dissemination Formats and Major Statistic Data Sets of the AEC Countries

Official statistics can be disseminated via different media including the Internet, paper publications, CD-ROM, fax, public speeches, and press releases. Dissemination formats such as HTML pages, PDF documents, and Excel sheets are widely deployed in national and international statistics agencies in releasing their data. However, with the recent worldwide movement that calls for opening up government or public data, more governments and institutions are increasingly publishing their data in more open formats such as SDMX, CSV, RDF, and RDFa that allow users to freely use and reuse or even link their data. The examination of dissemination formats of statistics of the AEC countries in this study has shown that data still needs to be disseminated in a more open format in cases of national statistical organizations in almost all AEC states as well as ASEAN.