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1

Demchenko, Yuri, Juan J. Cuadrado-Gallego, Oleg Chertov, and Marharyta Aleksandrova. Big Data Infrastructure Technologies for Data Analytics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69366-3.

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Sharma, Rohit, Gwanggil Jeon, and Yan Zhang, eds. Data Analytics for Internet of Things Infrastructure. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33808-3.

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Jang, Jinwoo. Development of Data Analytics and Modeling Tools for Civil Infrastructure Condition Monitoring Applications. [publisher not identified], 2016.

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Data Analytics for Advanced Metering Infrastructure. World Bank, Washington, DC, 2018. http://dx.doi.org/10.1596/32549.

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Data Analytics for Internet of Things Infrastructure. Springer, 2023.

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6

Big Data analytics with R and Hadoop: Set up an integrated infrastructure of R and Hadoop to turn your data analytics into Big Data analytics. Packt Publishing, 2013.

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7

Data-Driven Analytics for the Geological Storage of Co2. Taylor & Francis Group, 2018.

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8

Mohaghegh, Shahab. Data-Driven Analytics for the Geological Storage of CO2. Taylor & Francis Group, 2018.

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Mohaghegh, Shahab. Data-Driven Analytics for the Geological Storage of CO2. Taylor & Francis Group, 2018.

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10

Mohaghegh, Shahab. Data-Driven Analytics for the Geological Storage of CO2. Taylor & Francis Group, 2018.

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11

Serverless Analytics with Amazon Athena: Query Structured, Unstructured, or Semi-Structured Data in Seconds Without Setting up Any Infrastructure. de Gruyter GmbH, Walter, 2021.

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12

Virtuoso, Anthony, Mert Turkay Hocanin, Aaron Wishnick, and Rahul Pathak. Serverless Analytics with Amazon Athena: Query structured, unstructured, or semi-structured data in seconds without setting up any infrastructure. Packt Publishing, 2021.

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13

Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastructure, edge computing, analytics, and security. Packt Publishing, 2018.

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14

Cloud Infrastructures for Big Data Analytics. IGI Global, 2014.

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15

Giudici, Paolo, and Giulio Mignola. Big Data & Advanced Analytics per il Risk Management. AIFIRM, 2022. http://dx.doi.org/10.47473/2016ppa00035.

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One of the main consequences of the digital revolution, which for the last few years has been transforming almost every economic activity, has been an unprecedented availability of big data. At the same time, recent technological breakthroughs have provided tools (technological infrastructures and analytical methodologies) capable of processing these large amounts of data in a very short timeframe. Against this backdrop, the introduction of machine-learning models has been spreading. Even the Banking and Insurance sectors, despite their long-standing tradition of using statistical models, have
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16

Trepulė, Elena, Airina Volungevičienė, Margarita Teresevičienė, et al. Guidelines for open and online learning assessment and recognition with reference to the National and European qualification framework: micro-credentials as a proposal for tuning and transparency. Vytauto Didžiojo universitetas, 2021. http://dx.doi.org/10.7220/9786094674792.

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These Guidelines are one of the results of the four-year research project “Open Online Learning for Digital and Networked Society” (2017-2021). The project objective was to enable university teachers to design open and online learning through open and online learning curriculum and environment applying learning analytics as a metacognitive tool and creating open and online learning assessment and recognition practices, responding to the needs of digital and networked society. The research of the project resulted in 10 scientific publications and 2 studies prepared by Vytautas Magnus university
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17

van, José. Healthcare and Health Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190889760.003.0006.

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This chapter focuses on the platformization of the health sector. A burgeoning field of online health platforms has emerged, ranging from personal fitness apps to medical platforms that are used by patients, doctors, and researchers. A global industry of health-related platforms is being stacked onto, and interwoven with, the infrastructural core of the ecosystem; developing sectoral health platforms is a potentially lucrative and data-rich area that major operators are keen to invest in. Examining health platforms, the chapter unravels a peculiar double-edged logic in how their benefit is arg
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18

Ślusarski, Marek. Metody i modele oceny jakości danych przestrzennych. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-30-4.

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The quality of data collected in official spatial databases is crucial in making strategic decisions as well as in the implementation of planning and design works. Awareness of the level of the quality of these data is also important for individual users of official spatial data. The author presents methods and models of description and evaluation of the quality of spatial data collected in public registers. Data describing the space in the highest degree of detail, which are collected in three databases: land and buildings registry (EGiB), geodetic registry of the land infrastructure network
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19

Clifton, Judith, Daniel Díaz Fuentes, and David Howarth, eds. Regional Development Banks in the World Economy. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198861089.001.0001.

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Regional development banks (RDB) have become increasingly important in the world economy, but have also been relatively under-researched to date. This timely volume addresses this lack of attention by providing a comprehensive, comparative, and empirically informed analysis of their origins, evolution, and contemporary role in the world economy through to the second decade of the twenty-first century. The editors provide an analytical framework that includes a revised categorization of RDB by geographic operation and function. In part one, the chapter authors offer detailed analyses of the ori
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