Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

[EN] Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.Funding This work has been developed under the framework of the COSIBAS project (funded by CDTI research and innovation programme under grant agreement No.EXP 00110912/INNO-20181033) and the EIFFEL project (funded by European Unions Horizon 2020 research and innovation programme under grant agreement No 101003518).Valero López, CI.; Ivancos Pla, E.; Vañó García, R.; Garro, E.; Boronat, F.; Palau Salvador, CE. (2021). Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution. Sensors. 21(23):1-15. https://doi.org/10.3390/s21238133115212

Facilitating and Enhancing the Performance of Model Selection for Energy Time Series Forecasting in Cluster Computing Environments

Applying Machine Learning (ML) manually to a given problem setting is a tedious and time-consuming process which brings many challenges with it, especially in the context of Big Data. In such a context, gaining insightful information, finding patterns, and extracting knowledge from large datasets are quite complex tasks. Additionally, the configurations of the underlying Big Data infrastructure introduce more complexity for configuring and running ML tasks. With the growing interest in ML the last few years, particularly people without extensive ML expertise have a high demand for frameworks assisting people in applying the right ML algorithm to their problem setting. This is especially true in the field of smart energy system applications where more and more ML algorithms are used e.g. for time series forecasting. Generally, two groups of non-expert users are distinguished to perform energy time series forecasting. The first one includes the users who are familiar with statistics and ML but are not able to write the necessary programming code for training and evaluating ML models using the well-known trial-and-error approach. Such an approach is time consuming and wastes resources for constructing multiple models. The second group is even more inexperienced in programming and not knowledgeable in statistics and ML but wants to apply given ML solutions to their problem settings. The goal of this thesis is to scientifically explore, in the context of more concrete use cases in the energy domain, how such non-expert users can be optimally supported in creating and performing ML tasks in practice on cluster computing environments. To support the first group of non-expert users, an easy-to-use modular extendable microservice-based ML solution for instrumenting and evaluating ML algorithms on top of a Big Data technology stack is conceptualized and evaluated. Our proposed solution facilitates applying trial-and-error approach by hiding the low level complexities from the users and introduces the best conditions to efficiently perform ML tasks in cluster computing environments. To support the second group of non-expert users, the first solution is extended to realize meta learning approaches for automated model selection. We evaluate how meta learning technology can be efficiently applied to the problem space of data analytics for smart energy systems to assist energy system experts which are not data analytics experts in applying the right ML algorithms to their data analytics problems. To enhance the predictive performance of meta learning, an efficient characterization of energy time series datasets is required. To this end, Descriptive Statistics Time based Meta Features (DSTMF), a new kind of meta features, is designed to accurately capture the deep characteristics of energy time series datasets. We find that DSTMF outperforms the other state-of-the-art meta feature sets introduced in the literature to characterize energy time series datasets in terms of the accuracy of meta learning models and the time needed to extract them. Further enhancement in the predictive performance of the meta learning classification model is achieved by training the meta learner on new efficient meta examples. To this end, we proposed two new approaches to generate new energy time series datasets to be used as training meta examples by the meta learner depending on the type of time series dataset (i.e. generation or energy consumption time series). We find that extending the original training sets with new meta examples generated by our approaches outperformed the case in which the original is extended by new simulated energy time series datasets

From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows.

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Vance, T. C., Wengren, M., Burger, E., Hernandez, D., Kearns, T., Medina-Lopez, E., Merati, N., O'Brien, K., O'Neil, J., Potemrag, J. T., Signell, R. P., & Wilcox, K. From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows. Frontiers in Marine Science, 6(211), (2019), doi:10.3389/fmars.2019.00211.Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.This is PMEL contribution 4873

Analisis Perbandingan Akurasi Metode Moving Average dan Metode Exponensial Smoothing dalam Memprediksi Kapasitas Produksi Padi Nasional

Pengelolaan persediaan padi merupakan aspek penting yang perlu ditingkatkan oleh para pemangku kepentingan guna mencapai keseimbangan antara persediaan dan konsumsi beras. Bullwhip Effect (BE) telah menjadi perhatian khusus dalam rantai pasokan selama pandemi, terutama dengan adanya komponen permintaan musiman dan nonmusiman. Peramalan kebutuhan produksi padi diperlukan untuk mengatasi masalah dalam pengolahan data dan situasi di lapangan. Perangkat lunak seperti Production and Operations Management (POM) dapat digunakan untuk peramalan menggunakan logika fuzzy. Dalam era Industri 4.0, sustainable smart manufacturing menjadi hal yang penting. Proyeksi kebutuhan produksi beras nasional dilakukan dengan menggunakan metode moving average dan metode exponential smoothing. Pengujian akurasi dilakukan dengan peramalan menggunakan metode moving average dan exponential smoothing dengan data produksi padi tahun 2010-2019, kemudian hasil peramalan tahun 2020 dari kedua metode tersebut akan dibandingkan dengan data real dan akan diketahui metode mana yang paling mendekati data real. Tujuan utama penelitian ini adalah untuk membandingkan dua metode yaitu metode moving average dan metode exponential smoothing yang digunakan pada perangkat lunak berbasis fuzzy. Hasil pengujian akurasi peramalan produksi beras dengan menggunakan metode moving average dan exponential smoothing yang telah dilakukan menunjukkan bahwa metode moving average lebih akurat dengan selisih 1,0089% dari data sebenarnya, sedangkan metode exponential smoothing memiliki selisih 12,0051% dari data sebenarnya. &nbsp

Technologies for a FAIRer use of Ocean Best Practices

The publication and dissemination of best practices in ocean observing is pivotal for multiple aspects of modern marine science, including cross-disciplinary interoperability, improved reproducibility of observations and analyses, and training of new practitioners. Often, best practices are not published in a scientific journal and may not even be formally documented, residing solely within the minds of individuals who pass the information along through direct instruction. Naturally, documenting best practices is essential to accelerate high-quality marine science; however, documentation in a drawer has little impact. To enhance the application and development of best practices, we must leverage contemporary document handling technologies to make best practices discoverable, accessible, and interlinked, echoing the logic of the FAIR data principles [1]

The Nexus Between Security Sector Governance/Reform and Sustainable Development Goal-16

This Security Sector Reform (SSR) Paper offers a universal and analytical perspective on the linkages between Security Sector Governance (SSG)/SSR (SSG/R) and Sustainable Development Goal-16 (SDG-16), focusing on conflict and post-conflict settings as well as transitional and consolidated democracies. Against the background of development and security literatures traditionally maintaining separate and compartmentalized presence in both academic and policymaking circles, it maintains that the contemporary security- and development-related challenges are inextricably linked, requiring effective measures with an accurate understanding of the nature of these challenges. In that sense, SDG-16 is surely a good step in the right direction. After comparing and contrasting SSG/R and SDG-16, this SSR Paper argues that human security lies at the heart of the nexus between the 2030 Agenda of the United Nations (UN) and SSG/R. To do so, it first provides a brief overview of the scholarly and policymaking literature on the development-security nexus to set the background for the adoption of The Agenda 2030. Next, it reviews the literature on SSG/R and SDGs, and how each concept evolved over time. It then identifies the puzzle this study seeks to address by comparing and contrasting SSG/R with SDG-16. After making a case that human security lies at the heart of the nexus between the UN’s 2030 Agenda and SSG/R, this book analyses the strengths and weaknesses of human security as a bridge between SSG/R and SDG-16 and makes policy recommendations on how SSG/R, bolstered by human security, may help achieve better results on the SDG-16 targets. It specifically emphasizes the importance of transparency, oversight, and accountability on the one hand, and participative approach and local ownership on the other. It concludes by arguing that a simultaneous emphasis on security and development is sorely needed for addressing the issues under the purview of SDG-16

Big Data in Bioeconomy

This edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen. With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources

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

Destination Earth: Survey on “Digital Twins” technologies and activities, in the Green Deal area

Digital Twins have been around for decades, especially in industrial processes. However, with the recent advent of transformative digital technologies (i.e. IoT, AI, ML, Big Data analytics, and ubiquitous connectivity) Digital Twins are changing most of the society sectors, providing the most advance pattern to make the physical and the digital worlds interact. Naturally, this is also true for the scientific sector, and in particular those disciplines that are engaged in understanding and addressing the Global Change effects. Thanks to the Digital Twins growing development, for the first time, it is possible to envision a digital replica of important natural and social phenomena and processes, trying to anticipate their behaviour. There exist diverse definitions of Digital Twins, reflecting the diverse concerns of the industrial, scientific, and standardization sectors (in particular IEEE and ISO/IEC), which have been working on their description and realization. The main interaction features characterizing a Digital Twin are: - Interoperability; - Information Model; - Data Exchange; - Administration; - Synchronization; - Push mode (Publish Subscribe). According the scientific research, there is still the need to address the following challenges to push Digital Twins implementation and effective use: - Unify data and model standards; - Share data and models; - Innovate on services; - Establish forums. In industry, Digital Twins are well used in “vertical” sectors/application areas, including: manufacturing, energy, smart cities, farming, building, healthcare. For the applied scientific and research areas, this preliminary study recognized several areas.JRC.B.6-Digital Econom