Ba-Lam Do

Linked Widgets: An Approach to Exploit Open Government Data

More and more governments publish Open Government Data (OGD) for their citizens, which receives great interest; because they are not only covering many domains, but also are meaningful and trustful. Since the data are not ready to be linked to other sources, some governments have started to follow the Linked Open Data paradigm as a mean to improve data quality. However, exploiting Linked Data is still a challenging task, which requires a profound understanding of underlying datasets, as well as considerable technical expertise. In this paper, we present an approach to remove the barrier between end users and Linked Data generally, and Open Government Linked Data particularly.

Distributed mashups: a collaborative approach to data integration

Purpose – This paper aims to present a collaborative mashup platform for dynamic integration of heterogeneous data sources. The platform encourages sharing and connects data publishers, integrators, developers and end users. Design/methodology/approach – This approach is based on a visual programming paradigm and follows three fundamental principles: openness, connectedness and reusability. The platform is based on semantic Web technologies and the concept of linked widgets, i.e. semantic modules that allow users to access, integrate and visualize data in a creative and collaborative manner. Findings – The platform can effectively tackle data integration challenges by allowing users to explore relevant data sources for different contexts, tackling the data heterogeneity problem and facilitating automatic data integration, easing data integration via simple operations and fostering reusability of data processing tasks. Research limitations/implications – This research has focused exclusively on conceptual ...

Widget-based Exploration of Linked Statistical Data Spaces

Today, public statistical data plays an increasingly important role both in public policy formation and as a facilitator for informed decision-making in the private sector. In line with to the increasing adoption of open data policies, the amount of data published by governments and organizations on the web is growing rapidly. To make such data more useful, the W3C has developed the RDF Data Cube Vocabulary to facilitate the publication of data in a more structured and interlinked manner. Although important first steps toward building a web of statistical linked datasets have been made, however, providing adequate facilities for end users to interactively explore and make use of the published data largely remains an unresolved challenge. This paper presents a widget-based approach to deal with this issue. In particular, we introduce a mashup platform that allows users without advanced skills and knowledge of Semantic Web technologies to interactively analyze datasets through widget compositions and visualizations. Furthermore, we provide mechanisms for the interconnection of datasets to support sophisticated knowledge extraction.

Toward a statistical data integration environment: the role of semantic metadata

In most government and business organizations alike, statistical data provides the foundation for strategic planning and for the management of operations. In this context, the use of increasingly abundant statistical data available on the web creates new opportunities for interesting applications and facilitates more informed decision-making. For the majority of end users, however, viable means to explore statistical data sets available on the web are still scarce. Gathering and relating statistical data from multiple sources is hence typically a tedious manual process that requires significant technical expertise. Data that is being published with associated semantics, using standards such as the W3C RDF Data Cube Vocabulary, lays the foundation to overcome such limitations. In this paper, we develop a semantic metadata repository that describes each statistical data set and develop mechanisms for the interconnection of data sets based on their metadata. Finally, we support users in exploring data sets through interactive mashups that facilitate data integration, comparisons, and visualization.

A Web-based Platform for Dynamic Integration of Heterogeneous Data

Whereas the number of open and accessible data sources on the web is growing rapidly, data becomes more heterogeneous and is difficult to use or reuse. Even though many national and international organizations have published their data according to the Linked Data principles, a considerable number of data sources is still available in traditional formats, e.g., HTML, XML, CSV, JSON. This results in a challenge of data aggregation and integration. To address this issue, we apply the visual programming paradigm to develop an open web platform. The platform is based on Semantic Web technologies and aims at encouraging and facilitating use of heterogeneous Open Data sources. We define Linked Widgets as user-driven modules which support users in accessing, processing, integrating, and visualizing different kinds of data. By connecting Linked Widgets from different developers, users without programming skills can compose and share ad-hoc applications that combine Open Data sources in a creative manner.

Linked Widgets Platform: Lowering the Barrier for Open Data Exploration

Despite a drastic increase in available Open and Linked Data, unmediated utilization of these data by end users is still relatively uncommon. Applications built on top of Open Data are typically domain-specific and discovering appropriate solutions that fit users’ rapidly shifting needs is a cumbersome process. In line with the Linked Data paradigm, end user tools should be based on openness, foster reusability, and be flexible enough to handle arbitrary data sources. We develop an open platform based on Semantic Web technologies that encourages developers and users to access, process, integrate, and visualize Open Data sources. To help users overcome technological barriers of adoption and get in touch with Open Data, we introduce the concept of Linked Widgets. By connecting Linked Widgets from different developers, users without programming skills can compose and share ad-hoc applications that combine Open Data sources in a creative manner.

StatSpace: A Unified Platform for Statistical Data Exploration

In recent years, the amount of statistical data available on the web has been growing fast. Numerous organizations and governments publish data sets in a multitude of formats and encodings, using different scales, and providing access through a wide range of mechanisms. Due to such inconsistent publishing practices, integrated analysis of statistical data is challenging. StatSpace tackles this problem through semantic integration and provides uniform access to disparate statistical data. At present, it incorporates more than 1,800 data sets published by a variety of data providers including the World Bank, the European Union, and the European Environment Agency. StatSpace transparently lifts data from raw sources, maps geographical and temporal dimensions, aligns value ranges, and allows users to explore and integrate the previously isolated data sets. This paper introduces the constituent elements of the StatSpace architecture – i.e., a metadata repository, URI design patterns, and supporting services – and demonstrates the usefulness of the resulting Linked Data infrastructure by means of use case examples.

Towards an Environmental Decision-Making System: A Vocabulary to Enrich Stream Data

The future of the earth’s environmental systems will, to a major extent, be determined in cities, where already more than 50 % of the human population is concentrated. Pervasively available sensors and the data they generate can help to address pressing environmental challenges in urban areas by making crucial information available to researchers and decision-makers. However, environmental data is at present typically stored in disparate systems and formats, which inhibits reuse and integration. Furthermore, the large amounts of environmental data that stream in continuously require novel processing approaches. So far, research at the intersection of environmental sciences and urban data infrastructures has been scarce. To address these issues, we develop a novel framework based on semantic web technologies. We apply data modeling and semantic stream processing technologies in order to facilitate integration, comparison, and visualization of heterogeneous data from various sources. This paper presents the concept of a platform for environmental data stream analysis, and focuses on the design of a new vocabulary to semantically enrich the processed streams. The implemented architecture shall be capable of informing and supporting decision-making by non-expert users. We propose and discuss a three-step framework, present a vocabulary to model environmental data streams, and outline initial results.

Linked Widgets Platform for Rapid Collaborative Semantic Mashup Development

In recent years, data has become vital in supporting our everyday lives. Along with large volumes of open data available on the web, various types of public, private, and enterprise data are stored in the cloud or distributed over multiple devices. The value of this data would increase drastically if we were able to integrate it. This would enable more sophisticated presentation and analysis of previously disparate data. So far, however, it is challenging for non-expert users to efficiently make use of such data because (i) data heterogeneity hampers integration of different kinds of data that are stored in various formats and spread among storage infrastructures; (ii) manual data integration processes are typically neither reproducible, nor reusable; and (iii) the lack of support for exploration does not allow for the integration of arbitrary data sources. This paper tackles these challenges by introducing a mashup platform that combines semantic web and mashup concepts to help users obtain insights and make informed decisions. To this end, we leverage a semantic model of mashup components for automated techniques that support the user in exploring available data. Moreover, we introduce a collaborative and distributed model to create and execute mashups. This facilitates distributed ad-hoc integration of heterogeneous data contributed by multiple stakeholders.

Exploring linked statistical data using linked widgets

The Open Data movement has gained momentum among governments, in the business world, and in the public sector in recent years. This movement has resulted in a growing number of open and accessible datasets that have established a solid basis for enhanced service offerings and improved experiences for citizens and businesses. Statistical data, which embodies a big portion of Open Data, comprises a wide range of domains including finance, demographics, transportation, employment, etc. Statistical data plays an important role in public policy formation and as a facilitator for informed decision-making in the private sector. Linked Statistical Data is an evolving concept that combines the richness of Linked Data (a set of best practices for publishing and connecting structured data on the Web) with the descriptiveness of statistical data to integrate data from multiple sources and put it in a semantic context. In this short paper, Linked Statistical Data limitations and challenges are explored before introducing Linked Widgets as an innovative approach.