LEADER 01566nlm0 2200313 450 001 000030447 005 20150428124715.0 100 $a20150428d2014----km-y0itay50------ba 101 0 $aeng 102 $aIT 105 $ay---m---001yy 200 1 $aService Quality Monitoring, Prediction And Refinement-Based Guarantee For Dynamic Service-Centric Systems$ftesi di dottorato di: Giuseppe Cicotti$gtutor: Luigi Coppolino$brisorsa elettronica 210 $aNapoli$d2014 215 $a1 disco ottico (CD-ROM)$d12 cm 230 $aDati testuali (1 file: 2,4 Mb) 304 $aTit. dell'etichetta 314 $aCoordinatore: Antonio Napolitano 328 $aTesi di Dottorato di ricerca in Ingegneria dell'Informazione. 2012-2014 (26. ciclo) : Università degli studi di Napoli "Parthenope". Dipartimento di Ingegneria 337 $aRequisiti minimi del sistema: Windows 500 10$aService Quality Monitoring, Prediction And Refinement-Based Guarantee For Dynamic Service-Centric Systems$940315 610 1 $aElaborazione di dati$aElaboratori 676 $a004.65$v21$9Elaborazione dei dati. Scienza degli elaboratori. Informatica. Architettura delle reti di comunicazione. 700 1$aCicotti,$bGiuseppe$0634523 712 02$aUniversità degli Studi di Napoli "Parthenope" 801 0$aIT$bUNIPARTHENOPE$c20150428$gRICA$2UNIMARC 912 $a000030447 951 $aTESI Dottorato CD-ROM/202$bs.i.$cNAVA1$d2015 996 $aService Quality Monitoring, Prediction And Refinement-Based Guarantee For Dynamic Service-Centric Systems$940315 997 $aUNIPARTHENOPE LEADER 04399 am 22007573u 450 001 9910293141703321 005 20230125183347.0 010 $a3-658-20540-7 024 7 $a10.1007/978-3-658-20540-9 035 $a(CKB)4100000001795004 035 $a(DE-He213)978-3-658-20540-9 035 $a(MiAaPQ)EBC5599496 035 $a(Au-PeEL)EBL5599496 035 $a(OCoLC)1076259214 035 $a(MiAaPQ)EBC6422736 035 $a(Au-PeEL)EBL6422736 035 $a(OCoLC)1231610904 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/34375 035 $a(PPN)223955019 035 $a(EXLCZ)994100000001795004 100 $a20180109d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProjection-Based Clustering through Self-Organization and Swarm Intelligence$b[electronic resource] $eCombining Cluster Analysis with the Visualization of High-Dimensional Data /$fby Michael Christoph Thrun 205 $a1st ed. 2018. 210 $aCham$cSpringer Nature$d2018 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Vieweg,$d2018. 215 $a1 online resource (XX, 201 p. 90 illus., 29 illus. in color.) 311 $a3-658-20539-3 327 $aApproaches to Unsupervised Machine Learning -- Methods of Visualization of High-Dimensional Data -- Quality Assessments of Visualizations -- Behavior-Based Systems in Data Science -- Databionic Swarm (DBS). 330 $aThis book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun?s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch. 606 $aPattern recognition 606 $aData structures (Computer science) 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017 610 $aCluster Analysis 610 $aDimensionality Reduction 610 $aSwarm Intelligence 610 $aVisualization 610 $aUnsupervised Machine Learning 610 $aData Science 610 $aKnowledge Discovery 610 $a3D Printing 610 $aSelf-Organization 610 $aEmergence 610 $aGame Theory 610 $aAdvanced Analytics 610 $aHigh-Dimensional Data 610 $aMultivariate Data 610 $aAnalysis of Structured Data 615 0$aPattern recognition. 615 0$aData structures (Computer science). 615 14$aPattern Recognition. 615 24$aData Structures. 676 $a006.4 700 $aThrun$b Michael Christoph$4aut$4http://id.loc.gov/vocabulary/relators/aut$0909471 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910293141703321 996 $aProjection-Based Clustering through Self-Organization and Swarm Intelligence$92035037 997 $aUNINA LEADER 03669nam 2200913z- 450 001 9910557522003321 005 20220111 035 $a(CKB)5400000000044359 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76491 035 $a(oapen)doab76491 035 $a(EXLCZ)995400000000044359 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSustainable Business Models in Tourism 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (286 p.) 311 08$a3-0365-0888-0 311 08$a3-0365-0889-9 330 $aWe invite you to read the Special Issue on business models in tourism, in the context of considering the principles of sustainable development. It is a collection of 14 articles published in a Special Issue of Sustainability MDPI in 2019-2021. The dynamic changes taking place in the world economy, social life, and the natural environment force entrepreneurs to change their business models. This also happens in the tourism business. The SARS-COV2 virus pandemic has increased the need for change. It is necessary to offer managers modern management tools that cover the broadest possible scope of integration of the elements of the conducted business activities, at the same time adjusted to the specificity of the market and needs of the natural environment in which the enterprises managed by them operate. This book, formulated in the light of the presented needs, aims to use the concept of business models and sustainability business models in the context of a tourism enterprise adapted to the existing conditions of tourist and spa activities. 606 $aInformation technology industries$2bicssc 610 $abusiness environment 610 $abusiness model 610 $abusiness models 610 $aCarbon Footprint (CFP) 610 $aclimate change 610 $aconsumer behavior 610 $adestination branding 610 $aDoxey model 610 $aecological impact 610 $aGeneration X 610 $aGeneration Y 610 $aGeneration Z 610 $ahealth insurance 610 $ahealth resorts 610 $ahealth tourism 610 $aICT 610 $aindustrial tourism 610 $aKrakow 610 $aLife Cycle Assessment (LCA) 610 $alifestyle 610 $alogistic function 610 $am-tourism 610 $amanagement 610 $amedical spas 610 $amobile applications 610 $amuseums 610 $anational park 610 $aovertourism 610 $apeer-to-peer accommodation 610 $aPoland 610 $apost-industrial facilities 610 $asharing economy 610 $asmart technologies 610 $asmart tourism 610 $aspa tourism 610 $astate support 610 $asustainability 610 $asustainability in tourism 610 $asustainable business models 610 $asustainable development 610 $asustainable tourism 610 $aTALC 610 $aTeH2O Industrial Themed Trail 610 $atourism 610 $atourism market 610 $avisual identity 610 $awinter sports 610 $awinter sports resorts 615 7$aInformation technology industries 700 $aSzromek$b Adam R$4edt$01283181 702 $aSzromek$b Adam R$4oth 906 $aBOOK 912 $a9910557522003321 996 $aSustainable Business Models in Tourism$93018934 997 $aUNINA