標(biāo)題: Titlebook: Big Data in Bioeconomy; Results from the Eur Caj S?derg?rd,Tomas Mildorf,Christian Zinke-Wehlma Book‘‘‘‘‘‘‘‘ 2021 The Editor(s) (if applica [打印本頁(yè)] 作者: decoction 時(shí)間: 2025-3-21 19:16
書目名稱Big Data in Bioeconomy影響因子(影響力)
書目名稱Big Data in Bioeconomy影響因子(影響力)學(xué)科排名
書目名稱Big Data in Bioeconomy網(wǎng)絡(luò)公開度
書目名稱Big Data in Bioeconomy網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data in Bioeconomy被引頻次
書目名稱Big Data in Bioeconomy被引頻次學(xué)科排名
書目名稱Big Data in Bioeconomy年度引用
書目名稱Big Data in Bioeconomy年度引用學(xué)科排名
書目名稱Big Data in Bioeconomy讀者反饋
書目名稱Big Data in Bioeconomy讀者反饋學(xué)科排名
作者: antiandrogen 時(shí)間: 2025-3-21 20:25
ng more efficient and sustainable.Shows how common software .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 a作者: pus840 時(shí)間: 2025-3-22 02:48
Kleene Languages and Finite Automata which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.作者: uncertain 時(shí)間: 2025-3-22 07:44 作者: 嚴(yán)重傷害 時(shí)間: 2025-3-22 12:16
Lenore E. A. Walker,David L. Shapiro. Next, we present an overview of representative software technologiesfor efficiently describing, storing, querying and accessing Earth Observation datasets. The chapter concludes with a summary of the Earth Observation datasets used in each DataBio pilot. 作者: 柏樹 時(shí)間: 2025-3-22 16:32
Clinical Assessment in Forensic Settingssualize SensLog data. SensLog data model builds on the Observations & Measurements conceptual model from ISO 19156 and includes additional sections, e.g., for user authentication or volunteered geographic information (VGI) collection. It uses PostgreSQL database with PostGIS for data storage and several API endpoints.作者: auxiliary 時(shí)間: 2025-3-22 18:25
https://doi.org/10.1007/978-3-662-03809-3ese pipelines is to automate as much as possible the process to transform and publish different input datasets as Linked Data. In this chapter, we describe these pipelines and how they were applied to support different uses cases in the project, including the tools and methods used to implement them.作者: 離開可分裂 時(shí)間: 2025-3-22 21:32 作者: 和平主義者 時(shí)間: 2025-3-23 04:14
Remote Sensing. Next, we present an overview of representative software technologiesfor efficiently describing, storing, querying and accessing Earth Observation datasets. The chapter concludes with a summary of the Earth Observation datasets used in each DataBio pilot. 作者: Vsd168 時(shí)間: 2025-3-23 06:26
Crowdsourced Datasualize SensLog data. SensLog data model builds on the Observations & Measurements conceptual model from ISO 19156 and includes additional sections, e.g., for user authentication or volunteered geographic information (VGI) collection. It uses PostgreSQL database with PostGIS for data storage and several API endpoints.作者: 全部 時(shí)間: 2025-3-23 11:09 作者: 終止 時(shí)間: 2025-3-23 16:14 作者: gratify 時(shí)間: 2025-3-23 18:42
Lenore E. A. Walker,David L. Shapiro is essential when it comes to addressing real world complexities for any domain, as no single domain has sufficient data available within its own limits to tackle the major research challenges our world is facing.作者: 難聽的聲音 時(shí)間: 2025-3-23 23:08 作者: Duodenitis 時(shí)間: 2025-3-24 04:22
Caj S?derg?rd,Tomas Mildorf,Christian Zinke-WehlmaThis book is open access, which means that you have free and unlimited access.Explains how Big Data Technology can make raw material gathering more efficient and sustainable.Shows how common software 作者: 植物學(xué) 時(shí)間: 2025-3-24 10:05
http://image.papertrans.cn/b/image/185696.jpg作者: Precursor 時(shí)間: 2025-3-24 11:19 作者: 宇宙你 時(shí)間: 2025-3-24 16:20
Introduction to Formal PhilosophyBio project.??We start with a?short intdroduction of basic concepts. We then?describe how data analytics and machine learning markets have evolved. Next, we describe some basic technologies in the area. Finally, we describe how data analytics and machine learning were used in selected pilot cases of the DataBio project.作者: Interferons 時(shí)間: 2025-3-24 22:00 作者: 小母馬 時(shí)間: 2025-3-25 01:18 作者: delta-waves 時(shí)間: 2025-3-25 05:18 作者: 分解 時(shí)間: 2025-3-25 08:11
Introduction to Forensic Psychologyn the evolution of the Internet of Things (IoT). The chapter outlines how IoT technologies have affected bioeconomy sectors over the years. The last part outlines key examples of sensing devices and IoT data that are exploited in the context of the DataBio project.作者: absolve 時(shí)間: 2025-3-25 15:33 作者: 憲法沒有 時(shí)間: 2025-3-25 18:37 作者: Malaise 時(shí)間: 2025-3-25 20:38 作者: GREG 時(shí)間: 2025-3-26 02:22 作者: Carbon-Monoxide 時(shí)間: 2025-3-26 07:59
https://doi.org/10.1007/978-3-662-03809-3large variety of data generated and collected through various applications, services and devices. The?DataBio approach to deliver such capabilities was based on the use of Linked Data as a federated layer to provide an integrated view over (initially) disconnected and heterogeneous datasets. The lar作者: 松馳 時(shí)間: 2025-3-26 12:16
Introduction to Formal Hardware Verification of the data modeling regarding the readability and the comprehensibility of the models. We start with explaining the challenges surrounding the?DataBio project that led to the adoption of data pipelines modeling using the?Enterprise Architecture language ArchiMate. Then we present the data modeling作者: –LOUS 時(shí)間: 2025-3-26 15:55 作者: Amplify 時(shí)間: 2025-3-26 17:15 作者: Landlocked 時(shí)間: 2025-3-27 00:45 作者: 終端 時(shí)間: 2025-3-27 01:15
Introduction to Fractal Manufacturingent techniquesto address these challenges.We then provide examples from the DataBio project of? visualisation?solutions. These examples show that there are many technologies and software components available for? big data visualisation, but they also point to limitations and the need for further res作者: 解決 時(shí)間: 2025-3-27 07:59
Fractional Differential Equations,Smart agriculture is a rising area bringing the benefits of digitalization through big data, artificial intelligence and?linked?data into the agricultural domain. This chapter motivates the use and describes the rise of smart agriculture. 作者: Ergots 時(shí)間: 2025-3-27 12:22
Introduction of Smart AgricultureSmart agriculture is a rising area bringing the benefits of digitalization through big data, artificial intelligence and?linked?data into the agricultural domain. This chapter motivates the use and describes the rise of smart agriculture. 作者: insurgent 時(shí)間: 2025-3-27 16:27 作者: Conflagration 時(shí)間: 2025-3-27 18:24 作者: chemoprevention 時(shí)間: 2025-3-27 23:12
Sensor Datan the evolution of the Internet of Things (IoT). The chapter outlines how IoT technologies have affected bioeconomy sectors over the years. The last part outlines key examples of sensing devices and IoT data that are exploited in the context of the DataBio project.作者: 耕種 時(shí)間: 2025-3-28 04:26 作者: 大氣層 時(shí)間: 2025-3-28 09:18 作者: 牲畜欄 時(shí)間: 2025-3-28 11:18
Big Data Technologies in DataBiog Data including the main characteristics volume, velocity and variety. Thereafter, we discuss data pipelines and the Big Data Value (BDV) Reference Model that is referred to repeatedly in the book. The layered reference model ranges from data acquisition from sensors up to visualization and user in作者: 無(wú)情 時(shí)間: 2025-3-28 15:24 作者: 血友病 時(shí)間: 2025-3-28 19:04 作者: 美色花錢 時(shí)間: 2025-3-29 00:57
Remote Sensing velocity) make us consider Earth Observation as Big Data. Thereafter, we discuss the most commonly open data formats used to store and share?the data. The main sources of Earth Observation?dataare also described, with particular focus on the constellation of Sentinel satellites, Copernicus Hub and 作者: 公社 時(shí)間: 2025-3-29 06:00
Crowdsourced Datanformation on existing geospatial data oris a?part of data collection from geolocated devices.They enable?opening parts of scientific work to the general public.DataBio?Crowdsourcing Solution is a combination of the SensLog server platform and HSLayersweb and mobile applications. SensLog is a server作者: burnish 時(shí)間: 2025-3-29 07:32
Genomics Datanalysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high 作者: facilitate 時(shí)間: 2025-3-29 12:51 作者: GUISE 時(shí)間: 2025-3-29 17:36 作者: 清真寺 時(shí)間: 2025-3-29 20:50 作者: Affectation 時(shí)間: 2025-3-30 02:39
Data Analytics and Machine LearningBio project.??We start with a?short intdroduction of basic concepts. We then?describe how data analytics and machine learning markets have evolved. Next, we describe some basic technologies in the area. Finally, we describe how data analytics and machine learning were used in selected pilot cases of作者: 奇怪 時(shí)間: 2025-3-30 06:15 作者: Leaven 時(shí)間: 2025-3-30 11:38 作者: 類似思想 時(shí)間: 2025-3-30 14:24 作者: Favorable 時(shí)間: 2025-3-30 16:38
Book‘‘‘‘‘‘‘‘ 2021e technologies into perspective, by showing useable applications from farming, forestry andfishery. 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 a作者: Sad570 時(shí)間: 2025-3-30 23:32 作者: Projection 時(shí)間: 2025-3-31 03:58
Genomics Data breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which aredesignedto store, manage and analyze large-scale data, offer appeal作者: 拖債 時(shí)間: 2025-3-31 05:50
Real-Time Data Processingfor IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide r
