DataGraft: Simplifying Open Data Publishing

Roman, Dumitru; Dimitrov, Marin; Nikolov, Nikolay; Putlier, Antoine; Sukhobok, Dina; Elvesæter, Brian; Berre, Arne J.; Ye, Xianglin; Simov, Alex; Petkov, Yavor

doi:10.1007/978-3-319-47602-5_21

Cited by 13 publications

(7 citation statements)

References 1 publication

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“…Furthermore, the resulting data from the processing algorithms and case studies reported in this paper are publicly available and can be visualized via the Common Agriculture Policy Funds Assignments Service (CAPAS) viewer [41] and accessed in Linked Data form [42] via SPARQL on the DataGraft platform [43][44][45]. …”

Section: Discussion and Outlookmentioning

confidence: 99%

Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment

Estrada

Sánchez

Hernanz

et al. 2017

IJGI

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Abstract: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.

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Section: Discussion and Outlookmentioning

confidence: 99%

Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment

Estrada

Sánchez

Hernanz

et al. 2017

IJGI

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“…3]. The second case corresponds to the integration of RDF Surveyor in a larger platform -DataGraft 8 [28], [29], a cloud-based platform for data transformation and publishing. RDF Surveyor has been successfully integrated in DataGraft and is offered as an exploration tool for DataGraft datasets.…”

Section: Sample Usage Scenarios For Rdf Surveyormentioning

confidence: 99%

Linked Data Exploration With RDF Surveyor

et al. 2019

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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.

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“…The resulted set of data cleaning and transformation operations was implemented in Grafterizer [28], [36] -a web-based framework for data cleaning and transformations, that is a part of DataGraft 1 [29], [30] platform for data transformation and publishing. This section describes both the library for data cleaning and transformation operations (targeting software developers to facilitate data cleaning tasks in their applications) and the graphical user interface of Grafterizer that facilitates interactive data cleaning for data workers.…”

Section: Reference Implementation: Grafterizermentioning

confidence: 99%

Tabular Data Anomaly Patterns

Sukhobok

Nikolov

Roman

2017

2017 International Conference on Big Data Innovations and Applications (Innovate-Data)

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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.

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DataGraft: Simplifying Open Data Publishing

Cited by 13 publications

References 1 publication

Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment

Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment

Linked Data Exploration With RDF Surveyor

Tabular Data Anomaly Patterns

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