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020 _a9783030783068
020 _a9783030783075
_q(electronic bk.)
024 7 _a10.1007/978-3-030-78307-5
_2doi
035 9 _a(OCLCCM-CC)1313606104
035 _a(OCoLC)1313606104
_z(OCoLC)1313903832
_z(OCoLC)1324248657
_z(OCoLC)1330968359
_z(OCoLC)1336588875
_z(OCoLC)1374855040
_z(OCoLC)1380485672
_z(OCoLC)1395022430
_z(OCoLC)1401734378
_z(OCoLC)1417804632
_z(OCoLC)1419879379
_z(OCoLC)1440248388
_z(OCoLC)1452383331
_z(OCoLC)1457667366
_z(OCoLC)1470867430
041 _aeng
050 4 _aQA76.9.B45
072 7 _aUNF
_2bicssc
072 7 _aUNF
_2thema
082 0 0 _223
_a006.312
245 0 0 _aTechnologies and applications for big data value /
_cEdward Curry, Sören Auer, Arne J. Berre, Andreas Metzger, Maria S. Perez, Sonja Zillner, editors.
264 1 _aCham :
_bSpringer,
_c[2022]
264 4 _c©2022
300 _a1 online resource :
_billustrations (chiefly color)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
347 _bPDF
505 0 _aTechnologies and Applications for Big Data Value -- Part I: Technologies and Methods -- Trade-Offs and Challenges of Serverless Data Analytics -- Big Data and AI Pipeline Framework: Technology Analysis from a Benchmarking Perspective -- An Elastic Software Architecture for Extreme-Scale Big Data Analytics -- Privacy-Preserving Technologies for Trusted Data Spaces -- Leveraging Data-Driven Infrastructure Management to Facilitate AIOps for Big Data Applications and Operations -- Leveraging High-Performance Computing and Cloud Computing with Unified Big-DataWorkflows: The LEXIS Project -- Part II: Processes and Applications -- The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures -- Applying AI to Manage Acute and Chronic Clinical Condition -- 3D Human Big Data Exchange Between the Healthcare and Garment Sectors -- Using a Legal Knowledge Graph for Multilingual Compliance Services in Labor Law, Contract Management, and Geothermal Energy -- Big Data Analytics in the Banking Sector: Guidelines and Lessons Learned from the CaixaBank Case -- Data-Driven Artificial Intelligence and Predictive Analytics for the Maintenance of Industrial Machinery with Hybrid and Cognitive Digital Twins -- Big Data Analytics in the Manufacturing Sector: Guidelines and Lessons Learned Through the Centro Ricerche FIAT (CRF) Case -- Next-Generation Big Data-Driven Factory 4.0 Operations and Optimization: The Boost 4.0 Experience -- Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials: The Boost 4.0 Experience -- Model-Based Engineering and Semantic Interoperability for Trusted Digital Twins Big Data Connection Across the Product Lifecycle -- A Data Science Pipeline for Big Linked Earth Observation Data -- Towards Cognitive Ports of the Futures -- Distributed Big Data Analytics in a Smart City -- Processing Big Data in Motion: Core Components and System Architectures with Applications to the Maritime Domain -- Knowledge Modeling and Incident Analysis for Special Cargo.
520 _aThis open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part "Technologies and Methods" contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part "Processes and Applications" details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.
540 _aCreative Commons Attribution 4.0 International
_fCC BY 4.0
_uhttp://creativecommons.org/licenses/by/4.0/
546 _aEnglish.
588 0 _aOnline resource; title from PDF title page (SpringerLink, viewed May 6, 2022).
650 0 _aBig data.
_0http://id.loc.gov/authorities/subjects/sh2012003227
650 0 _aArtificial intelligence.
_0http://id.loc.gov/authorities/subjects/sh85008180
655 7 _aElectronic books.
700 1 _aCurry, Edward,
_eeditor.
700 1 _aAuer, Sören,
_d1975-
_eeditor.
_1https://id.oclc.org/worldcat/entity/E39PBJpptWRgw9frGTgrg9G4MP
_0http://id.loc.gov/authorities/names/n2009016885
700 1 _aBerre, Arne J.,
_eeditor.
700 1 _aMetzger, Andreas,
_eeditor.
700 1 _aPérez, María S.,
_eeditor.
700 1 _aZillner, Sonja,
_eeditor.
856 4 0 _uhttps://directory.doabooks.org/handle/20.500.12854/81694
_yFull text is available at the Directory of Open Access Books. Click here to view.
942 _2ddc
_cOA