Peter Wetz

YABench: A Comprehensive Framework for RDF Stream Processor Correctness and Performance Assessment

RDF stream processing (RSP) has become a vibrant area of research in the semantic web community. Recent advances have resulted in the development of several RSP engines that leverage semantics to facilitate reasoning over flows of incoming data. These engines vary greatly in terms of implemented query syntax, their evaluation and operational semantics, and in various performance dimensions. Existing benchmarks tackle particular aspects such as functional coverage, result correctness, or performance. None of them, however, assess RSP engine behavior comprehensively with respect to all these dimensions. In this paper, we introduce YABench, a novel benchmarking framework for RSP engines. YABench extends the concept of correctness checking and provides a flexible and comprehensive tool set to analyze and evaluate RSP engine behavior. It is highly configurable and provides quantifiable and reproducible results on correctness and performance characteristics. To validate our approach, we replicate results of the existing CSRBench benchmark with YABench. We then assess two well-established RSP engines, CQELS and C-SPARQL, through more comprehensive experiments. In particular, we measure precision, recall, performance, and scalability characteristics while varying throughput and query complexity. Finally, we discuss implications on the development of future stream processing engines and benchmarks.

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.

Stream Reasoning for the Internet of Things: Challenges and Gap Analysis

The Internet of Things (IoT) is not only about interconnecting embedded devices to the Internet, but also about providing knowledge on such devices and what they sense from the physical world. One focus of IoT is put on extracting actionable knowledge and providing value-added services by means of reasoning techniques. Stream reasoning techniques offer a promising solution for processing dynamic, heterogeneous, and volume data for IoT. In this article, we identify the challenges for utilizing stream reasoners from the IoT point of view, review the landscape of stream reasoning techniques, and examine their capabilities to meet the challenges of IoT. Moreover, we present an experimental IoT system implementing stream reasoning and perform a gap analysis to evaluate stream reasoners. Finally, based on the analysis, we suggest several recommendations for future development of stream reasoners in order to overcome the identified gaps.

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.

Leveraging the web of data via linked widgets

Machine-readable datasets that have increasingly become available in open formats in recent years have great potential as a foundation for innovative applications and services. Linked Data in particular-a set of best practices for publishing and connecting structured data on the Web-has facilitated significant progress in evolving the Web of documents into a Web of Data. However, although this concept has opened up many opportunities for data sharing and collaboration, integrating data is still a challenging task that requires considerable technical expertise and a profound understanding of the underlying datasets. In this paper, we introduce a novel approach to provide knowledge workers with the necessary tools to leverage the fast growing Linked Data Cloud by creating semantic-aware dataflow processes. To this end, we introduce the “Linked Widget” concept as an enhancement of standard Web Widgets. These widgets are based on a semantic data model that facilitates powerful mechanisms for gathering, processing, integration, and visualization of data in a user-friendly Mashup environment. By allowing knowledge workers to easily create complex Linked Data applications in an adhoc manner, our approach should contribute towards reducing existing barriers of Linked Data adoption.

Ontology-based Data Integration for Corporate Sustainability Information Systems

Nowadays, the term sustainability gains more and more importance in corporate risk management and decision making. Corporate Sustainability Information Systems should support companies to analyse sustainability risks and provide relevant data in a practical manner. A major challenge in the domain of sustainability is to integrate environmental, social and economic data from different sources. This paper presents an approach for ontology-based data integration in the Corporate Sustainability domain preserving hierarchical relations to allow e.g. improved querying. Based on an existing sustainability framework defined in an XBRL-taxonomy, first a mechanism for the automatic generation of a domain-specific ontology is proposed. Finally, special data is queried and linked to the proposed ontology, serving as a working example for the suggested approach.