Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Access to Marine Genetic Resources (MGR): Raising Awareness of Best-Practice Through a New Agreement for Biodiversity Beyond National Jurisdiction (BBNJ)

Better scientific knowledge of the poorly-known deep sea and areas beyond national jurisdiction (ABNJ) is key to its conservation, an urgent need in light of increasing environmental pressures. Access to marine genetic resources (MGR) for the biodiversity research community is essential to allow these environments to be better characterised. Negotiations have commenced under the auspices of the United Nations Convention on the Law of the Sea (UNCLOS) to develop a new treaty to further the conservation and sustainable use of marine biological diversity in ABNJ. It is timely to consider the relevant issues with the development of the treaty underway. Currently uncertainties surround the legal definition of MGR and scope of related benefit-sharing, against a background of regional and global governance gaps in ABNJ. These complications are mirrored in science, with recent major advances in the field of genomics, but variability in handling of the resulting increasing volumes of data. Here, we attempt to define the concept of MGR from a scientific perspective, review current practices for the generation of and access to MGR from ABNJ in the context of relevant regulations, and illustrate the utility of best-practice with a case study. We contribute recommendations with a view to strengthen best-practice in accessibility of MGR, including: funder recognition of the central importance of taxonomy/biodiversity research; support of museums/collections for long-term sample curation; open access to data; usage and further development of globally recognised data standards and platforms; publishing of datasets via open-access, quality controlled and standardised data systems and open access journals; commitment to best-practice workflows; a global registry of cruises; and lastly development of a clearing house to further centralised access to the above. We argue that commitment to best-practice would allow greater sharing of MGR for research and extensive secondary use including conservation and environmental monitoring, and provide an exemplar for access and benefit-sharing (ABS) to inform the biodiversity beyond national jurisdiction (BBNJ) process.Copyright © 2019 Rabone, Harden-Davies, Collins, Zajderman, Appeltans, Droege, Brandt, Pardo-Lopez, Dahlgren, Glover and Horton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms

The science-policy interfaces of the European network for observing our changing planet : From Earth Observation data to policy-oriented decisions

This paper reports on major outcomes of the ERA-PLANET (The European network for observing our changing planet) project, which was funded under Horizon 2020 ERA-net co-funding scheme. ERA-PLANET strengthened the European Research Area in the domain of Earth Observation (EO) in coherence with the European partici-pation to Group on Earth Observation and the Copernicus European Union's Earth Observation programme. ERA -PLANET was implemented through four projects focused on smart cities and resilient societies (SMURBS), resource efficiency and environmental management (GEOEssential), global changes and environmental treaties (iGOSP) and polar areas and natural resources (iCUPE). These projects developed specific science-policy workflows and interfaces to address selected environmental policy issues and design cost-effective strategies aiming to achieve targeted objectives. Key Enabling Technologies were implemented to enhancing 'data to knowledge' transition for supporting environmental policy making. Data cube technologies, the Virtual Earth Laboratory, Earth Observation ontologies and Knowledge Platforms were developed and used for such applications.SMURBS brought a substantial contribution to resilient cities and human settlements topics that were adopted by GEO as its 4th engagement priority, bringing the urban resilience topic in the GEO agenda on par with climate change, sustainable development and disaster risk reduction linked to environmental policies. GEOEssential is contributing to the development of Essential Variables (EVs) concept, which is encouraging and should allow the EO community to complete the description of the Earth System with EVs in a close future. This will clearly improve our capacity to address intertwined environmental and development policies as a Nexus.iGOSP supports the implementation of the GEO Flagship on Mercury (GOS4M) and the GEO Initiative on POPs (GOS4POPs) by developing a new integrated approach for global real-time monitoring of environmental quality with respect to air, water and human matrices contamination by toxic substances, like mercury and persistent organic pollutants. iGOSP developed end-user-oriented Knowledge Hubs that provide data repository systems integrated with data management consoles and knowledge information systems.The main outcomes from iCUPE are the novel and comprehensive data sets and a modelling activity that contributed to delivering science-based insights for the Arctic region. Applications enable defining and moni-toring of Arctic Essential Variables and sets up processes towards UN2030 SDGs that include health (SDG 3), clean water resources and sanitation (SDGs 6 and 14).Peer reviewe

Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Integrating Data Science and Earth Science

This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows

Integrating Data Science and Earth Science

This open access book presents the results of three years collaboration between earth scientists and data scientists, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows

The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development