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Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon. The work in this article was partly funded by the EC H2020 projects euBusinessGraph (grant 732003), EW-Shopp (grant 732590), TheyBuyForYou (780247), CIMA (grant BG16RFOP002-1.005-0168-C01)

This study deals with fault tree analysis algorithms based on an ontology-based approach in a fuzzy environment. We extend fuzzy fault tree analysis by embedding ontology-based fault tree structures. The ontology-based approach allows conducting advanced analyses grounded on rich domain knowledge. Two approaches as the conventional approach and rule-based method are applied for the calculations of failure probability and analysis of the fault tree. Collision and grounding of an autonomous ship are investigated in two scenarios. The structural design, probability calculations, and sensitivity analyses prove that the proposed system is applicable, robust, accurate and reliable. Thus, the propounded ontology-based fault tree algorithms fill a gap in safety engineering by its capability of comprehensiveness.

The emergence of NoSQL multi-model databases, natively supporting scalable and unified storage and querying of various data models, presents new opportunities for storing and managing RDF data. In this paper, we propose an approach to store RDF data in multi-model databases. We identify various aspects of representing the RDF data structure into a multi-model data structure and discuss their advantages and disadvantages. Furthermore, we implement and evaluate the proposed approach in a prototype using ArangoDB—a popular multi-model database.

The development of the Edge computing paradigm shifts data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructure. Such a paradigm requires data processing solutions that consider data locality in order to reduce the performance penalties from data transfers between remote (in network terms) data centres. However, existing Big Data processing solutions have limited support for handling data locality and are inefficient in processing small and frequent events specific to Edge environments. This paper proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. Our solution considers any available data locality information by default, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare our system with Argo workflow and show significant performance improvements in terms of speed of execution for processing units of data using our data locality aware Big Data workflow approach. This work was partly funded by the EC H2020 project “DataCloud” (Grant nr. 101016835) and the NFR project “BigDataMine” (Grant nr. 309691).

Big Data processing involves handling large and complex data sets, incorporating different tools and frameworks as well as other processes that help organisations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, require taking advantage of the elasticity of cloud infrastructures for scalability. In this paper, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies and message-oriented middleware (MOM) to enable highly scalable workflow execution. The approach is demonstrated in a use case together with a set of experiments that demonstrate the practical applicability of the proposed approach for the scalable execution of Big Data workflows. Furthermore, we present a scalability comparison of our proposed approach with that of Argo Workflows - one of the most prominent tools in the area of Big Data workflows.

The release of a growing amount of open procurement data means that we are increasingly able, and even have the obligation, to scrutinize and analyse public spending for delivering better quality of public services, optimizing costs, preventing fraud and corruption, and building healthy and sustainable economies. The TheyBuyForYou project addresses this challenge by developing an integrated technology platform, with a cross-lingual and cross-border procurement knowledge graph, core services, open APIs, and online tools, and validating them in several business cases in public/corporate procurement in Slovenia, Spain and Italy. This paper gives an overview about the project’s goals and challenges.

Public procurement is a large market affecting almost every organisation and individual. Governments need to ensure efficiency, transparency, and accountability, while creating healthy, competitive, and vibrant economies. In this context, we built a platform, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed end-user tools on top of the knowledge graph for anomaly detection and cross-lingual document search. This paper describes our experiences and challenges faced in creating such a platform and knowledge graph and demonstrates the usefulness of Semantic Web technologies for enhancing public procurement.

The concepts of Hybrid and Cognitive Digital Twin are introduced as elements of the next level of process control and automation in the process and manufacturing industry. We propose an architecture for the implementation of Hybrid and Cognitive Twins as part of the COGNITWIN software toolbox. The toolbox is designed to cover cognitive capabilities for optimal operations and maintenance of process equipment and assets, thereby minimizing production overheads and increasing efficiencies for the process industry. Furthermore, we identify a set of relevant use cases in the process industry and discuss the possible applicability and use of the toolbox.

Digital marketing is a domain where data analytics are a key factor to gaining competitive advantage and return of investment for companies running and monetizing digital marketing campaigns on, e.g., search engines and social media. In this paper, we propose an end-to-end approach to enrich marketing campaigns performance data with third-party event data (e.g., weather events data) and to analyze the enriched data in order to predict the effect of such events on campaigns’ performance, with the final goal of enabling advanced optimization of the impact of digital marketing campaigns. The use of semantic technologies is central to the proposed approach: event data are made available in a format more amenable to enrichment and analytics, and the actual data enrichment technique is based on semantic data reconciliation. The enriched data are represented as Linked Data and managed in a NoSQL database to enable processing of large amounts of data. We report on the development of a pilot to build a weather-aware digital marketing campaign scheduler for JOT Internet Media—a world leading company in the digital marketing domain that has amassed a huge amount of data on campaigns performance over the years—which predicts the best date and region to launch a marketing campaign within a seven-day timespan. Additionally, we discuss benefits and limitations of applying semantic technologies to deliver better optimization strategies and competitive advantage.

With the emergence of data markets, data have become an asset that is used as part of transactions. Current data markets rely on trusted third parties to manage the data, creating single points of failure with possibly disastrous consequences on data privacy and security. The lack of technical solutions to enforce strong privacy and security guarantees leaves the data markets’ stakeholders (e.g., buyers and sellers of data) vulnerable when they transact data. Smart Contracts and Multi-Party Computation represent examples of emerging technologies that have the potential to guarantee the desired levels of data privacy and security. In this paper, we propose an architecture for data markets based on Smart Contracts and Multi-Party Computation and present a proof of concept prototype developed to demonstrate the feasibility of the proposed architecture.

The risk and vulnerability analysis of buildings identifies buildings with high exposure to natural hazards. It is crucial to prevent or reduce damage and economic loss for the construction and property management industry. The government, as well as private landowners, benefit from risk and vulnerability assessment of their real estate portfolio. In this paper, we propose a semantic-enhanced method to simplify the risk and vulnerability assessment process and improve interoperability of real estate and natural hazards data. The method relies on the publication and integration of data from various sources as linked data. Visualization of the integrated data is also presented, together with application scenarios.

Linked Data exploration is an essential task in the process of understanding, assessing, and using datasets made available in the Resource Description Framework (RDF) format. Current solutions for exploration of RDF data are mainly targeted at Semantic Web experts, require non-trivial deployments, and do not scale to the increasing amounts of data published in RDF. The lack of simple, intuitive, and efficient solutions for exploring RDF data, especially for lay users, is the main motivation behind the work presented in this paper. We propose RDF Surveyor, an easy-to-use and lightweight tool for exploring RDF datasets. Its visual interface hides the intricacies of Semantic Web technologies from the user, while providing intuitive overviews of datasets, class navigation, and visualization of class instances. Furthermore, RDF Surveyor does not require any installation and can handle large datasets such as DBpedia. We provide a detailed overview of RDF Surveyor and illustrate its capabilities in two different scenarios. We also analyze the uptake, performance and usability of RDF Surveyor, showing its suitability for exploring Linked Data at scale.

Data preprocessing is a crucial step in data analysis. A substantial amount of time is spent on data transformation tasks such as data formatting, modification, extraction, and enrichment, typically making it more convenient for users to work with systems that can recommend most relevant transformations for a given dataset. In this paper, we propose an approach for generating relevant data transformation suggestions for tabular data preprocessing using machine learning (specifically, the Random Forest algorithm). The approach is implemented for Grafterizer, a Web-based framework for tabular data cleaning and transformation, and evaluated through a usability study.

The task of computing composite indicators to define and analyze complex social, economic, political, or environmental phenomena has traditionally been the exclusive competence of statistical offices. Nowadays, the availability of increasing volumes of data and the emergence of the open data movement have enabled individuals and businesses affordable access to all kinds of datasets that can be used as valuable input to compute indicators. OpenStreetMap (OSM) is a good example of this. It has been used as a baseline to compute indicators in areas where official data is scarce or difficult to access. Although the extraction and application of OSM data to compute indicators is an attractive proposition, this practice is by no means hassle-free. The use of OSM reveals a number of challenges that are usually addressed with ad-hoc and often overlapping solutions. In this context, this paper proposes MethOSM-a systematic methodology for computing indicators derived from OSM data. By applying MethOSM, the computation task is divided into four steps, with each step having a clear goal and a set of guidelines to apply. In this way, the methodology contributes to an effective and efficient use of OSM data for the purpose of computing indicators. To demonstrate its use, we apply MethOSM to a number of indicators used for real estate valuation of properties in Italy.

Part 3: Open Science and Open Data; International audience; The release of a growing amount of open procurement data led to various initiatives for harmonising the data being provided. Among others, the Open Contracting Data Standard (OCDS) is highly relevant due to its high practical value and increasing traction. OCDS defines a common data model for publishing structured data throughout most of the stages of a contracting process. OCDS is document-oriented and focuses on packaging and delivering relevant data in an iterative and event-driven manner through a series of releases. Ontologies, beyond providing uniform access to heterogeneous procurement data, could enable integration with related data sets such as with supplier data for advanced analytics and insight extraction. Therefore, we developed an ontology, the “OCDS ontology”, by using OCDS’ main domain perspective and vocabulary, since it is an essential source of domain knowledge. In this paper, we provide an overview of the developed ontology.

Procurement affects virtually all sectors and organizations particularly in times of slow economic recovery and enhanced transparency. Public spending alone will soon exceed EUR 2 trillion per annum in the EU. Therefore, there is a pressing need for better insight into, and management of government spending. In the absence of data and tools to analyse and oversee this complex process, too little consideration is given to the development of vibrant, competitive economies when buying decisions are made. To this end, in this short paper, we report our ongoing work for enabling procurement data value chains through a knowledge graph based platform with data management, analytics, and interaction.

Part 4: Cloud Resources; International audience; Data preparation is the process of collecting, cleaning and consolidating raw datasets into cleaned data of certain quality. It is an important aspect in almost every data analysis process, and yet it remains tedious and time-consuming. The complexity of the process is further increased by the recent tendency to derive knowledge from very large datasets. Existing data preparation tools provide limited capabilities to effectively process such large volumes of data. On the other hand, frameworks and software libraries that do address the requirements of big data, require expert knowledge in various technical areas. In this paper, we propose a dynamic, service-based, scalable data preparation approach that aims to solve the challenges in data preparation on a large scale, while retaining the accessibility and flexibility provided by data preparation tools. Furthermore, we describe its implementation and integration with an existing framework for data preparation – Grafterizer. Our solution is based on Apache Spark, and exposes application programming interfaces (APIs) to integrate with external tools. Finally, we present experimental results that demonstrate the improvements to the scalability of Grafterizer.

This paper presents the Norwegian State of Estate (SoE) dataset containing data about real estates owned by the central government in Norway. The dataset is produced by integrating cross-domain government datasets including data from sources such as the Norwegian business entity register, cadastral system, building accessibility register and the previous SoE report. The dataset is made available as Linked Data. The Linked Data generation process includes data acquisition, cleaning, transformation, annotation, publishing, augmentation and interlinking the annotated data as well as quality assessment of the interlinked datasets. The dataset is published under the Norwegian License for Open Government Data (NLOD) and serves as a reference point for applications using data on central government real estates, such as generation of the SoE report, searching properties suitable for asylum reception centres, risk assessment for state-owned buildings or a public building application for visitors.

One essential and challenging task in data science is data cleaning - the process of identifying and eliminating data anomalies. Different data types, data domains, data acquisition methods, and final purposes of data cleaning have resulted in different approaches in defining data anomalies in the literature. This paper proposes and describes a set of basic data anomalies in the form of anomaly patterns commonly encountered in tabular data, independently of the data domain, data acquisition technique, or the purpose of data cleaning. This set of anomalies can serve as a valuable basis for developing and enhancing software products that provide general-purpose data cleaning facilities and can provide a basis for comparing different tools aimed to support tabular data cleaning capabilities. Furthermore, this paper introduces a set of corresponding data operations suitable for addressing the identified anomaly patterns and introduces Grafterizer - a software framework that implements those data operations.

Earthquakes, landslides, and other natural hazard events have severe negative socio-economic impacts. Among other consequences, those events can cause damage to infrastructure networks such as roads and railways. Novel methodologies and tools are needed to analyse the potential impacts of extreme natural hazard events and aid in the decision-making process regarding the protection of existing critical road and rail infrastructure as well as the development of new infrastructure. Enabling uniform, integrated, and reliable access to data on historical failures of critical transport infrastructure can help infrastructure managers and scientist from various related areas to better understand, prevent, and mitigate the impact of natural hazards on critical infrastructures. This paper describes the construction of the InfraRisk ontology for representing relevant information about natural hazard events and their impact on infrastructure components. Furthermore, we present a software prototype that visualizes data published using the proposed ontology.

This paper proposes an approach for using visual data profiling in tabular data cleaning and transformation processes. Visual data profiling is the statistical assessment of datasets to identify and visualize potential quality issues. The proposed approach was implemented in a software prototype and empirically validated in a usability study to determine to what extent visual data profiling is useful and how easy it is to use by data scientists. The study involved 24 users in a comparative usability test and 4 expert reviewers in cognitive walkthroughs. The evaluation results show that users find visual data profiling capabilities to be useful and easy to use in the process of data cleaning and transformation.

In this demonstrator we introduce DataGraft – a platform for Open Data management. DataGraft provides data transformation, publishing and hosting capabilities that aim to simplify the data publishing lifecycle for data workers (i.e., Open Data publishers, Linked Data developers, data scientists). This demonstrator highlights the key features of DataGraft by exemplifying a data transformation and publishing use case with property-related data.

Over the past several years the amount of published open data has increased significantly. The majority of this is tabular data, that requires powerful and flexible approaches for data cleaning and preparation in order to convert it into Linked Data. This paper introduces Grafterizer – a software framework developed to support data workers and data developers in the process of converting raw tabular data into linked data. Its main components include Grafter, a powerful software library and DSL for data cleaning and RDF-ization, and Grafterizer, a user interface for interactive specification of data transformations along with a back-end for management and execution of data transformations. The proposed demonstration will focus on Grafterizer’s powerful features for data cleaning and RDF-ization in a scenario using data about the risk of failure of transport infrastructure components due to natural hazards.

A comprehensive strategy combining remote sensing and field data can be helpful for more effective agriculture management. Satellite data are suitable for monitoring large areas over time, while LiDAR provides specific and accurate data on height and relief. Both types of data can be used for calibration and validation purposes, avoiding field visits and saving useful resources. In this paper, we propose a process for objective and automated identification of agricultural parcel features based on processing and combining Sentinel-2 data (to sense different types of irrigation patterns) and LiDAR data (to detect landscape elements). The proposed process was validated in several use cases in Spain, yielding high accuracy rates in the identification of irrigated areas and landscape elements. An important application example of the work reported in this paper is the European Union (EU) Common Agriculture Policy (CAP) funds assignment service, which would significantly benefit from a more objective and automated process for the identification of irrigated areas and landscape elements, thereby enabling the possibility for the EU to save significant amounts of money yearly.

Part 9: Workshop ENVIP 2013, Environmental Information Systems and Services - Infrastructures and Platforms 2013; International audience; A number of past and current research and development projects aim to improve the sharing and use of environmental information. In 2010, the Environmental Infrastructures and Platforms (ENVIP) initiative was introduced as a means to identify the European potentials and specify common building blocks (‘services and enablers’) related to these projects. Work began with a set of projects supporting the Shared Environmental Information System (SEIS). This paper briefly summarizes the ongoing result collection and introduces the next wave of activities, which will follow two newly emerging trends: Citizens Observatories and Linked Open Data. We invite interested parties and project consortia to provide their contributions for future analysis and synergies through the CEN/TC287 TR 15449-2 Best practices registry and the ENVIP initiative.

We present our ideas of an open Information and Communication Technology (ICT) platform for monitoring, mapping and managing our environment. The envisioned solution bridges the gap between the Internet of Things, Content and Services, and highly specific applications, such as oil spill detection or marine monitoring. On the one hand, this environmental platform should be open to new technologies; on the other hand, it has to provide open standard interfaces to various application domains. We identify core components, standards, and needs for new standard development in ICT for environment. We briefly outline how our past and present activities contribute to the development of the desired open environmental platform. Future implementations shall contribute to sustainable developments in the environmental domain., JRC.DDG.H.6-Spatial data infrastructures

Part 3: Environmental Information Systems and Services – Infrastructures and Platforms; International audience; The ENVISION Portal is a Web-enabled infrastructure for the discovery, annotation, and composition of environmental services. It is a tool to create Web sites dedicated to particular domain-specific scenarios such as oil spill drift modeling or landslide risk assessment. The underlying architecture based on pluggable user interface components is briefly discussed, followed by a presentation of the components resulting from the first iteration of the implementation. A walkthrough explains how to create a scenario website and populate it with the user interface components required for one specific scenario. The paper concludes with a discussion of open challenges identified during the implementation.

PurposeCitizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.Design/methodology/approachThe authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.FindingsThe results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.Originality/valueIn the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

In the business environment, knowledge of company data is essential for a variety of tasks. The European funded project euBusinessGraph enables the establishment of a company data platform where data providers and consumers can publish and access company data. The core of the platform is the semantic data model that is the conceptual representation of company data in a common way so that it is easier to share and interlink company data. In this paper we show how the unified model and Grafterizer, a tool for manipulating and transforming raw data into Linked Data, support the linking challenge proposed in FEIII 2019. Results show that geographical enrichment of RDF data supports the interlinking process between company entities in different datasets.

RuleML 2014 held in conjunction with ECAI 2014, the 21st biennial European Conference on Artificial Intelligence; International audience; In this paper, we motivate and develop the linguistic characteristics of requirement compounds which are major types of business rules. The discourse structures that further refine or elaborate requirements are also analyzed. An implementation is then presented. It is carried out in Dislog on the platform. Dislog allows high level specifications in logic that allow fast and easy prototyping at a high level of linguistic adequacy. Elements of an indicative evaluation are provided.