01522nam 2200409 450 00000879320070503173200.088-85134-37-820011010d1995----km-y0itay0103----baitaIT"Periferia" e "Centro"un'antitesi nella "questione della lingua" di alcune storicità linguisticheWalter Belardi1 0002261RomaDipartimento di studi glottoantropologici, Università La Sapienza<<Il>> Calamo1995428 p.ill.24 cm.Biblioteca di ricerche linguistiche e filologiche37Linguistica storica417.7(20 ed.)Linguistica storicaBelardi,Walter131289ITUniversità della Basilicata - B.I.A.RICAunimarc000008793"Periferia" e "Centro"77582UNIBASMONLETMONOGRLETTEREPETRUCCELL0520011010BAS011136PETRUCCELL0520011010BAS011137PETRUCCELL0520011010BAS01113920050601BAS011754batch0120050718BAS01105020050718BAS01110920050718BAS01113920050718BAS011153BATCH0020070503BAS011732BAS01BAS01BOOKBASA1Polo Storico-UmanisticoDSLACollezione DiSSLADD/50985098D50982001120304Prestabile Didattica01351nam0 22003131i 450 UON0040276620231205104704.426978-27-01-80294-720120104d2011 |0itac50 bafreFR|||| |||||Lampes antiques du Bilad es ShamJordanie, Syrie, Liban, PalestinelAncient Lamps of Bilad es ShamActes du Colloque de Pétra-Amman (6-13 novembre 2005)éds. Dina Frangié, Jean-Francois SallesParisDe Boccard2011435 p.ill,25 cmdono dell'EditoreIT-UONSI K468001UON000663842001 De l'archeologie a l'histoireLAMPADEVicino OrienteAntichitàCongressiUONC080610FIFRParisUONL002984745.5932Lampade - Artigianato21FRANGIEDinaUONV206595SALLESJean FrancoisUONV004690De BoccardUONV265257650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00402766SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI K 468 SI MC 34438 5 dono dell'EditoreLampes antiques du Bilad es Sham1348737UNIOR06512nam 22007095 450 991073482480332120251009083524.03-031-31785-810.1007/978-3-031-31785-9(CKB)27451731000041(MiAaPQ)EBC30618345(Au-PeEL)EBL30618345(DE-He213)978-3-031-31785-9(OCoLC) 1389613855(PPN)272252115(EXLCZ)992745173100004120230704d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierThe Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control /by Mario Vanhoucke1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (331 pages)Management for Professionals,2192-810X9783031317842 Intro -- Preface -- Acknowledgements -- Contents -- Part I Data-Driven Project Management -- 1 About This Book -- 1.1 Theory and Practice -- 1.2 Data and People -- 1.3 Book Outline -- 1.4 Keep Reading -- References -- 2 Each Book Tells a Story -- 2.1 Bookstore -- 2.2 Only a Click Away -- 2.3 Keep Writing -- References -- 3 The Data-Driven Project Manager -- 3.1 Three Components -- 3.2 A Reference Point -- 3.3 The Beauty of Details -- 3.4 Literature (in a Nutshell) -- References -- Part II What Academics Do -- 4 Understanding -- 4.1 Measuring Time -- 4.2 Shedding New Light -- 4.3 Thank You, Tony -- References -- 5 Wisdom -- 5.1 Tolerance Limits -- 5.2 Control Points -- 5.3 Signal Quality -- 5.4 Mission Accomplished -- References -- 6 Learning -- 6.1 Schedule -- 6.2 Risk -- 6.3 Control -- 6.4 Torture -- References -- Part III What Professionals Want -- 7 Control Efficiency -- 7.1 Effort of Control -- Top-Down Project Control -- Bottom-up Project Control -- 7.2 Quality of Actions -- 7.3 Accuracy Pays Off -- 7.4 Empirical Evidence -- 7.5 The Control Room -- Afterthought -- References -- 8 Analytical Project Control -- 8.1 Project Control Methods (Revisited) -- 8.2 Best of Both Worlds -- 8.3 The Signal (Not the Noise) -- 8.4 Hope and Dream -- References -- 9 Reference Class Forecasting -- 9.1 Outside View -- 9.2 Construction Project (Study 1) -- 9.3 Hybrid Approach (Study 2) -- 9.4 Similarity Properties (Study 3) -- 9.5 Thank You, Bent -- References -- Part IV About Project Data -- 10 Project Data -- 10.1 Where Are We Now? -- 10.2 Two Types of Project Data -- Reference -- 11 Artificial Projects -- 11.1 Random Data -- 11.2 Structured Data -- 11.3 Generating Data -- 11.4 Twilight Zone -- 11.5 Data and Algorithms -- 11.6 Diverse Data -- 11.7 Core Data -- 11.8 Equivalent Data -- 11.9 From a Distance -- 11.10 Final Words -- References -- 12 Progress Data.12.1 Imagination -- 12.2 Variation Model -- 12.3 Risk Model -- 12.4 Scenario Model -- 12.5 Fiction -- References -- 13 Empirical Projects -- 13.1 Curiosity -- 13.2 Classification -- 13.3 New Library -- 13.4 Reality -- References -- 14 Calibrating Data -- 14.1 Calibrating Data -- 14.2 Partitioning Heuristic -- 14.3 Human Partitioning (the rider) -- 14.4 Automatic Partitioning (the horse) -- 14.5 Calibration Results -- 14.6 Conclusion -- References -- 15 More Data -- 15.1 Resources -- 15.2 Modes -- 15.3 Subgraphs -- 15.4 Skills -- 15.5 Reality -- 15.6 Portfolio -- References -- Part V Afterword -- 16 The Perfect Researcher -- 16.1 Doubt -- 16.2 Ignorance -- 16.3 Wildness -- 16.4 Serendipity -- References -- A Operations Research &amp -- Scheduling Group -- B Earned Value Management (Glossary) -- C Properties of Similarity -- D Patterson Format -- E Network and Resource Indicators -- F Network Resources = NetRes -- G Example Project Card -- H OR&amp -- S Project Datasets -- References.This book comprehensively assesses the growing importance of project data for project scheduling, risk analysis and control. It discusses the relevance of project data for both researchers and professionals, and illustrates why the collection, processing and use of such data is not as straightforward as most people think. The theme of this book is known in the literature as data-driven project management and includes the discussion of using computer algorithms, human intuition, and project data for managing projects under risk. The book reviews the basic components of data-driven project management by summarizing the current state-of-the-art methodologies, including the latest computer and machine learning algorithms and statistical methodologies, for project risk and control. It highlights the importance of artificial project data for academics, and describes the specific requirements such data must meet. In turn, the book discusses a wide variety of statistical methods available to generate these artificial data and shows how they have helped researchers to develop algorithms and tools to improve decision-making in project management. Moreover, it examines the relevance of project data from a professional standpoint and describes how professionals should collect empirical project data for better decision-making. Finally, the book introduces a new approach to data collection, generation, and analysis for creating project databases, making it relevant for academic researchers and professional project managers alike.Management for Professionals,2192-810XProduction managementProject managementOperations researchBig dataAlgorithmsOperations ManagementProject ManagementOperations Research and Decision TheoryBig DataAlgorithmsProduction management.Project management.Operations research.Big data.Algorithms.Operations Management.Project Management.Operations Research and Decision Theory.Big Data.Algorithms.658.4040285658.4040285Vanhoucke Mario963281MiAaPQMiAaPQMiAaPQBOOK9910734824803321The Illusion of Control3404564UNINA