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Big Data Analytics for Cultural Heritage / / by Manolis Wallace, Vassilis Poulopoulos, Angeliki Antoniou (editors)
| Big Data Analytics for Cultural Heritage / / by Manolis Wallace, Vassilis Poulopoulos, Angeliki Antoniou (editors) |
| Pubbl/distr/stampa | [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 |
| Descrizione fisica | 1 online resource (210 pages) |
| Disciplina | 658/.05631 |
| Soggetto topico |
Data mining
Management - Data processing Big data |
| ISBN | 3-0365-6327-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | About the Editors vii -- An Overview of Big Data Analytics for Cultural Heritage 1 -- A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems 3 -- Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain 25 -- Data-Assisted Persona Construction Using Social Media Data 53 -- A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies 67 -- ACUX Recommender: A Mobile Recommendation System for Multi-Profile Cultural Visitors Based on Visiting Preferences Classification 85 -- Big Data Analytics for Search Engine Optimization 97 -- Using Big and Open Data to Generate Content for an Educational Game to Increase Student Performance and Interest 119 -- Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia 139 -- Networks and Stories. Analyzing the Transmission of the Feminist Intangible Cultural Heritage on Twitter 163 -- Digital Technologies and the Role of Data in Cultural Heritage: The Past, the Present, and the Future 181. |
| Record Nr. | UNINA-9910647230103321 |
| [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data, data mining, and machine learning : value creation for business leaders and practitioners
| Big data, data mining, and machine learning : value creation for business leaders and practitioners |
| Autore | Dean Jared |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (289 pages) |
| Disciplina |
658
658.05631 658/.05631 |
| Collana |
Wiley and SAS business series
THEi Wiley ebooks |
| Soggetto topico |
Big data
COMPUTERS / Database Management / Data Mining Data mining Database management Information technology -- Management Management -- Data processing Management |
| ISBN | 1-118-69178-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Big Data, Data Mining, and Machine Learning; Contents; Forward; Preface; Acknowledgments; Introduction; Big Data Timeline; Why This Topic Is Relevant Now; Is Big Data a Fad?; Where Using Big Data Makes a Big Difference; Technical Issue; Work Flow Productivity; The Complexities When Data Gets Large; Part One The Computing Environment; Chapter 1 Hardware; Storage (Disk); Central Processing Unit; Graphical Processing Unit; Memory; Network; Chapter 2 Distributed Systems; Database Computing; File System Computing; Considerations; Chapter 3 Analytical Tools; Weka; Java and JVM Languages; R; Python
SASPart Two Turning Data into Business Value; Chapter 4 Predictive Modeling; A Methodology for Building Models; sEMMA; sEMMA for the Big Data Era; Binary Classification; Multilevel Classification; Interval Prediction; Assessment of Predictive Models; Classification; Receiver Operating Characteristic; Lift; Gain; Akaike's Information Criterion; Bayesian Information Criterion; Kolmogorov‐Smirnov; Chapter 5 Common Predictive Modeling Techniques; RFM; Regression; Basic Example of Ordinary Least Squares; Assumptions of Regression Models; Additional Regression Techniques Applications in the Big Data EraGeneralized Linear Models; Example of a Probit GLM; Applications in the Big Data Era; Neural Networks; Basic Example of Neural Networks; Decision and Regression Trees; Support Vector Machines; Bayesian Methods Network Classification; Naive Bayes Network; Parameter Learning; Learning a Bayesian Network; Inference in Bayesian Networks; Scoring for Supervised Learning; Ensemble Methods; Chapter 6 Segmentation; Cluster Analysis; Distance Measures (Metrics); Evaluating Clustering; Number of Clusters; K-means Algorithm; Hierarchical Clustering; Profiling Clusters Chapter 7 Incremental Response ModelingBuilding the Response Model; Measuring the Incremental Response; Chapter 8 Time Series Data Mining; Reducing Dimensionality; Detecting Patterns; Fraud Detection; New Product Forecasting; Time Series Data Mining in Action: Nike+ FuelBand; Seasonal Analysis; Trend Analysis; Similarity Analysis; Chapter 9 Recommendation Systems; What Are Recommendation Systems?; Where Are They Used?; How Do They Work?; Baseline Model; Low‐Rank Matrix Factorization; Stochastic Gradient Descent; Alternating Least Squares; Restricted Boltzmann Machines; Contrastive Divergence Assessing Recommendation QualityRecommendations in Action: SAS Library; Chapter 10 Text Analytics; Information Retrieval; Content Categorization; Text Mining; Text Analytics in Action: Let's Play Jeopardy!; Information Retrieval Steps; Discovering Topics in Jeopardy! Clues; Topics from Clues Having Incorrect or Missing Answers; Discovering New Topics from Clues; Contestant Analysis: Fantasy Jeopardy!; Part Three Success Stories of Putting It All Together; Chapter 11 Case Study of a Large U.S.-Based Financial Services Company; Traditional Marketing Campaign Process High-Performance Marketing Solution |
| Record Nr. | UNINA-9910132334903321 |
Dean Jared
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| Hoboken : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||