03963nam 2200793 450 991046567370332120200520144314.01-63157-561-9(CKB)3710000000842452(BEP)4659276(OCoLC)958391029(CaBNVSL)swl00406814(MiAaPQ)EBC4659276(Au-PeEL)EBL4659276(CaPaEBR)ebr11252268(CaONFJC)MIL950603(OCoLC)957655434(EXLCZ)99371000000084245220160914d2016 fy 0engurcnu||||||||rdacontentrdamediardacarrierBig data war how to survive global big data competition /Patrick H. ParkFirst edition.New York, New York (222 East 46th Street, New York, NY 10017) :Business Expert Press,2016.1 recurso en línea (x, 195 páginas)Big data and business analytics collection,2333-6757Includes index.1-63157-560-0 Part 1. Dump the data -- 1. Global data war -- 2. Why did Google TV fail? -- 3. Why do they analyze data? -- Part 2. Data is human -- 4. Think as a customer -- 5. Big data, start from human -- 6. Why does Nike compete with Nintendo? -- Part 3. Data is created by me -- 7. Knowing necessary data is everything in data analytics -- 8. Create data -- Part 4. We don't need the past -- 9. Predict human unconsciousness -- 10. Anything with a pattern can be predicted -- Part 5. What matters at the end is performance -- 11. Data is strategy -- 12. Big data, a long way to go -- Epilogue -- About the author -- Index.Written by Patrick H. Park, an author of Brain Work (Korea, 2014). The book mainly focuses on why data analytics fails in business. It provides an objective analysis and root causes of the phenomenon, instead of abstract criticism of utility of data analytics. The author, then, explains in detail on how companies can survive and win the global big data competition, based on actual cases of companies. Having established the execution and performance-oriented big data methodology based on over 10 years of experience in the field as an authority in big data strategy, the author identifies core principles of data analytics using case analysis of failures and successes of actual companies. Moreover, he endeavors to share with readers the principles regarding how innovative global companies became successful through utilization of big data. This book is a quintessential big data analytics, in which the author's know-how from direct and indirect experiences is condensed. How do we survive at this big data war in which Facebook in SNS, Amazon in e-commerce, and Google in search expand their platforms to other areas based on their respective distinct markets? The answer can be found in this book.Big data and business analytics collection.2333-6757Big dataQuantitative researchElectronic books.AmazonApplebig databusiness intelligenceconsultingcustomer analysiscustomer profilingCRMdatadeep learningFacebookGoogleITmachine learningMBAmarketingproduct profilingproblem solvingstrategyTechVentureBig data.Quantitative research.005.7Park Patrick H.970509MiAaPQMiAaPQMiAaPQBOOK9910465673703321Big data war2205908UNINA05876nam 22008655 450 991076360050332120260708114945.09783031453823303145382410.1007/978-3-031-45382-3(MiAaPQ)EBC30941343(Au-PeEL)EBL30941343(DE-He213)978-3-031-45382-3(CKB)28846137900041(OCoLC)1409686067(EXLCZ)992884613790004120231113d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Concepts for Intelligent Vision Systems 21st International Conference, ACIVS 2023 Kumamoto, Japan, August 21–23, 2023 Proceedings /edited by Jaques Blanc-Talon, Patrice Delmas, Wilfried Philips, Paul Scheunders1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (397 pages)Lecture Notes in Computer Science,1611-3349 ;14124Print version: Blanc-Talon, Jaques Advanced Concepts for Intelligent Vision Systems Cham : Springer,c2023 9783031453816 A hybrid quantum-classical segment-based Stereo Matching algorithm -- Continuous Exposure for Extreme Low-Light Imaging -- Semi-supervised Classification and Segmentation of Forest Fire using Autoencoders -- Descriptive and coherent paragraph generation for image paragraph captioning using vision transformer and post-processing -- Pyramid Swin Transformer for Multi-Task: Expanding to more computer vision tasks -- Person activity classification from an aerial sensor based on a multi-level deep features -- Person Quick-Search Approach based on a Facial Semantic Attributes Description -- Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers -- BENet: A lightweight bottom-up framework for context-aware emotion recognition -- Yolopoint: Joint Keypoint and Object Detection -- Less-than-one shot 3d segmentation hijacking a pre-trained space-time memory network -- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network -- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network -- A Single Image Neuro-Geometric Depth Estimation -- Wave-shaping Neural Activation for Improved 3D Model Reconstruction from Sparse Point Clouds -- A Deep Learning Approach to Segment High-Content Images of the E.coli Bacteria -- Multimodal Emotion Recognition System Through Three Different Channels (MER-3C) -- Multi-Modal Obstacle Avoidance in USVs via Anomaly Detection and Cascaded Datasets -- A Contrario Mosaic Analysis for Image Forensics -- IRIS SEGMENTATION TECHNIQUE USING IRIS-UNet METHOD -- Image Acquisition by Image Retrieval with Color Aesthetics -- Improved Obstructed Facial Feature Reconstruction for Emotion Recognition with Minimal Change CycleGANs -- Quality assessment for high dynamic range stereoscopic omnidirectional image system -- Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging -- Reinforcement Learning for truck Eco-driving: a serious game as driving assistance system -- Underwater mussel segmentation using smoothed shape descriptors with random forest -- A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain simulation -- Construction of a novel data set for pedestrian tree species detection using google street view data -- Texture-based Data Augmentation for Small Datasets -- Multimodal Representations for Teacher-Guided Compositional Visual Reasoning -- Enhanced Color QR Codes with Resilient Error Correction for Dirt-Prone Surfaces.This book constitutes the proceedings of the 21st International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2023, held in Kumamoto, Japan, during August 2023. The 31 papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named: Computer Vision, Affective Computing and Human Interactions, Managing the Biodiversity, Robotics and Drones, Machine Learning.Lecture Notes in Computer Science,1611-3349 ;14124Computer visionBiometric identificationImage processingRoboticsComputer VisionBiometricsImage ProcessingRoboticsVisió per ordinadorthubProcessament d'imatgesthubRobòticathubIdentificació biomètricathubInteracció persona-ordinadorthubAprenentatge automàticthubDronsthubCongressosthubLlibres electrònicsthubComputer vision.Biometric identification.Image processing.Robotics.Computer Vision.Biometrics.Image Processing.Robotics.Visió per ordinadorProcessament d'imatgesRobòticaIdentificació biomètricaInteracció persona-ordinadorAprenentatge automàticDrons006.37Blanc-Talon Jaques1439146Delmas Patrice1439147Philips Wilfried1439148Scheunders Paul1439149MiAaPQMiAaPQMiAaPQBOOK9910763600503321Advanced Concepts for Intelligent Vision Systems3601343UNINA