Semantics all the way down: the Semantic Web and open science in big earth data

Narock, T. W.; Shepherd, Adam

doi:10.1080/20964471.2017.1397408

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…If the planned activities include database construction, this step will also include the work of data structures, such as conceptual model, logical model, physical model, as well as controlled vocabularies for data standardization. Researchers of cyberinfrastructure have recognized that consideration and action on data semantics in the early stage will help improve data interoperability when data are generated, collected, integrated and shared in a later stage (Reitsma et al, 2009;Narock and Shepherd, 2017).…”

Section: Business Understanding and Conceptmentioning

confidence: 99%

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Ma¹

2021

Preprint

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Data science receives increasing attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions have begun to increase in many geoscience conferences. Across academia, industry, and governmental sectors, there is a strong interest to know more about the current progress as well as the potential of data science for geoscience. To address that need, this article provides a review from the perspective of a data life cycle. The key steps in the data life cycle includes concept, collection, preprocessing, analysis, archive, distribution, discovery, and repurpose. Those subjects are intuitive and easy to follow even for geoscientists with very limited experience of cyberinfrastructure, statistics, and machine learning. The review includes two key parts. The first is about the fundamental concepts and theoretical foundation of data science, and the second is the summary of highlights and sharable experience from existing publications centered on each step in the data life cycle. At the end, a vision about the future trends of data science applications in geoscience is discussed, including topics on open science, smart data, and science of team science. We hope this review will be useful to data science practitioners in the geoscience community, and will lead to more discussions on the best practices and future trends of data science for geoscience.

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Section: Business Understanding and Conceptmentioning

confidence: 99%

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Ma¹

2021

Preprint

View full text Add to dashboard Cite

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“…In this context, Boulton ( 2018 ) argues that effects of big Earth data can be compared to the invention of the microscope, allowing to see complex but previously concealed patterns. Others argue that biased data require biased methods and advocate for including semantics in data analysis, especially when data from multiple sources are combined (Narock and Shepherd 2017 ; Scheider, Ostermann, and Adams 2017 ). Scientific discussion aside, in practical applications, user communities of big Earth data will be further broadened to non-expert user groups seeking practical answers in the data.…”

Section: Gaps and Challenges: Technology Policy Science And Societymentioning

confidence: 99%

Big Earth data: disruptive changes in Earth observation data management and analysis?

Sudmanns

Tiede

Lang

et al. 2019

International Journal of Digital Earth

131

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Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term 'big Earth data' emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

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“…Moreover, van den Brink et al [29] proposed the best practice of delivering geospatial linked data, and they bridged the OGC web services and the Semantic Web. In the Earth Science domain, there have also been several discussions about how to utilize linked data for data integration and discovery (e.g., [30]).…”

Section: Geospatial Semantic Web and Linked Datamentioning

confidence: 99%

Assessment and Benchmarking of Spatially Enabled RDF Stores for the Next Generation of Spatial Data Infrastructure

Huang

Raza

Mirzov

et al. 2019

IJGI

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Geospatial information is indispensable for various real-world applications and is thus a prominent part of today’s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). However, current SDIs are facing challenges in terms of data integration and semantic heterogeneity because of their partially siloed data organization. In this context, linked data provides a promising means to unravel these challenges, and it is seen as one of the key factors moving SDIs toward the next generation. In this study, we investigate the technical environment of the support for geospatial linked data by assessing and benchmarking some popular and well-known spatially enabled RDF stores (RDF4J, GeoSPARQL-Jena, Virtuoso, Stardog, and GraphDB), with a focus on GeoSPARQL compliance and query performance. The tests were performed in two different scenarios. In the first scenario, geospatial data forms a part of a large-scale data infrastructure and is integrated with other types of data. In this scenario, we used ICOS Carbon Portal’s metadata—a real-world Earth Science linked data infrastructure. In the second scenario, we benchmarked the RDF stores in a dedicated SDI environment that contains purely geospatial data, and we used geospatial datasets with both crowd-sourced and authoritative data (the same test data used in a previous benchmark study, the Geographica benchmark). The assessment and benchmarking results demonstrate that the GeoSPARQL compliance of the RDF stores has encouragingly advanced in the last several years. The query performances are generally acceptable, and spatial indexing is imperative when handling a large number of geospatial objects. Nevertheless, query correctness remains a challenge for cross-database interoperability. In conclusion, the results indicate that the spatial capacity of the RDF stores has become increasingly mature, which could benefit the development of future SDIs.

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Semantics all the way down: the Semantic Web and open science in big earth data

Cited by 4 publications

References 19 publications

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Big Earth data: disruptive changes in Earth observation data management and analysis?

Assessment and Benchmarking of Spatially Enabled RDF Stores for the Next Generation of Spatial Data Infrastructure

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