Theory and practice of linked open statistical data

Lecture Notes in Computer Science

Demographic, economic, social and other datasets are often used in policy-making processes. These types of statistical data are opened more and more by governments, which enables the use of these datasets by the public. However, statistical data needs often to combine different datasets. Data cubes can be used to combine datasets and are a multi-dimensional array of values typically used to describe time series of geographical areas. While Linked Open Statistical Data (LOSD) cube software is still in an initial stage of maturity, there is a need for evaluation the software platforms used to process this open data. Yet there is a lack of evaluation methods. The objective of this ongoing research paper is to identify functional requirements for open data cubes infrastructures. Eight main processes are identified and a list of 23 functional requirements are used to evaluate the OpenCube platform. The evaluation results of a LOSD platform show that many functions are not automated and need to be manually executed. We recommend the further integration of the building blocks in the platform to reduce the barriers for the use of datasets by the public.

2013

In the last few years, with the rise of the open data movement, a large and increasing number of governments and organizations have started to make information freely available and easily accessible online. Additionally, in order to increase transparency and improve interoperability and interaction with citizens and society as a whole, but also create new businesses and job opportunities, national governments publish their data in a machine-readable and future-proof format. In this paper we present the LOD2 Statistical Workbench, an integrated set of professional tools for accessing, manipulating, exploring and publishing statistical data. The data representation and processing is based on the W3C standard vocabularies (RDF Data Cube as a main model) and open source components delivered by the LOD2 consortium. The system meets the needs of both publishers and consumers of statistical data and directs the potential of the LOD2 tools to the specific domain of the statistical office. U...

Information, 2019

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.

Semantic Technology, 2018

In 2016, the Japanese Statistics Center published a large-scale statistical linked open data (LOD) site consisting of approximately 300 million triples. The LOD simplify processing data, such as filtering, aggregation, and integration of data. The aim of this action is to promote domestic and international utilization of the statistics. In this paper, we introduce publishing processes and a use case of statistical LOD. Afterwards, we show our approach to speed up the SPARQL search for the vast number of LOD.

2013

The access information law approved by the Brazilian government regulates the provision of open government data in the Web. However, they are heterogeneous, unstructured and derived from independent sources, making it difficult to interconnect. This paper presents a process of identifying sources, ontology generation, mapping and publishing statistical linked data in the form of multidimensional cubes, represented by the RDF Data Cube Vocabulary. In this process, data are transformed and assigned semantic meaning through its connection with domain ontologies. Through a web application, the publication of these data is automated, allowing for future analysis operations with the use of Online Analytical Processing (OLAP). As a result, the approach is expected to increase the scale in the publication of statistical linked data, and therefore, increasing the potential for analysis.

Web Semantics: Science, Services and Agents on the World Wide Web, 2016

Publishing and sharing open government data in Linked Data format provides many opportunities in terms of data aggregation/integration and creation of information mashups. Statistical data, that contains various performance indicators and their evolution through time, is an example of data that can be used as the foundation for policy prediction, planning and adjustments, and can be re-used in different applications. However, due to Linked Data being relatively a new field, currently there is a lack of tools that enable efficient exploration and analysis of linked geospatial statistical datasets. Therefore, ESTA-LD (Exploratory Spatio-Temporal Analysis) tool was developed to address some of the Linked statistical Data management issues, such as crossing the statistical and the geographical dimensions, producing statistical maps, visualizing different measures, and comparing statistical indicators of different regions through time. This paper discusses the modeling approach that was adopted so that the published data conform to the established standards for representing statistical, spatial and temporal data in Linked Data format. The main contribution is related to the delivery of state-of-the-art open-source tools for retrieving, quality assessment, exploration and analysis of statistical Linked Data that is made available through a SPARQL endpoint.

In the recent years the Linked Open Data phenomenon has gained a substantial traction. This has lead to a vast amount of data being available on the Web in what is known as the LOD cloud. While the potential of this linked data space is huge, it fails to reach the non-expert users so far. At the same time there is even larger amount of data that is so far not open yet, often because its owners are not convinced of its usefulness. In this paper we refine our Linked Data Visualization Model (LDVM) and show its application via its implementation Payola. On a real-world scenario built on real-world Linked Open Data created from Czech open data sources we show how end-user friendly visualizations can be easily achieved. Our first goal is to show that using Payola, existing Linked Open Data can be easily mashed up and visualized using an extensible library of analyzers, transformers and visualizers. Our second goal is to give potential publishers of (Linked) Open Data a proof that simply by publishing their data in a right way can bring them powerful visualizations at virtually no additional cost.

Many national and international organizations today leverage semantic web technologies to make statistical datasets available as Linked Open Data (LOD). A key advantages of this approach is that the data not only becomes publicly available, but also machine-readable and hence suitable for automated discovery and exploration. Whereas this has great potential to support interesting use cases, it remains difficult for end users today to utilize and combine these statistical Linked Data. Three challenges are: (i) directing users to relevant data sources based on a specified location; (ii) facilitating data integration despite a lack of outgoing links between datasets; and (iii) offering flexible means to integrate and aggregate data from various sources. As time and location are highly relevant dimensions in most statistical data, we address the identified challenges by first constructing geographical metadata for statistical sources. Following a mashup approach, we introduce mechanisms to recommend interesting datasets to end users and automatically enable data integration, visualization, and comparisons based on userdefined criteria.

2014

syntax to make statements about resources, and thanks to LOD principles, construct links to available external datasets in a simple manner, making them accessible and queryable through the Internet and promoting data reusability. As the amount of information at hand is greater every day, the need to handle it efficiently becomes a key requirement for anybody interested in working with it. This situation settles a great scenario for the Information Visualization field (or InfoVis, as it is known by academics and industry), taking advantage of humans capacity to identify patterns and gain insights from visual representations of abstract data. InfoVis positions itself in the intersection of other data-related fields: Statistics, Analytics, Dissemination and so on. One of the biggest issues concerning mass adoption of LOD outside the Semantic Web (SW) community, is the technical and conceptual knowledge required to take full advantage of the benefits provided by this type of data publis...

2016

The StatDCAT Application Profile is an extension of the DCAT Application Profile for Data Portals in Europe, version 1.1 (DCAT-AP). Its purpose is to provide a specification that is fully conformant with DCAT-AP version 1.1 as it meets all obligations of the DCAT-AP Conformance Statement. Its basic use case is to facilitate a better integration of the existing statistical data portals with the Open Data Portals, improving the discoverability of statistical datasets. StatDCAT-AP defines a small number of additions to the DCAT-AP model that are particularly relevant for statistical datasets. This will be beneficial for the general data portals, enabling enhanced services for the discovery of statistical data.