Big Data, Data Mining and Data Science
Description
Through the application of cutting-edge techniques like Big Data, Data Mining, and Data Science, it is possible to extract insights from massive datasets. These methodologies are crucial in enabling informed decision-making and driving transformative advancements across many fields, industries, and domains. This book offers an overview of latest tools, methods and approaches while also highlighting their practical use through various applications and case studies.
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Persons
G. Dimitoglou , Hood Coll.; L. Deligiannidis , W. Inst. of Tech, USA; H.R. Arabnia , U Georgia, Georgia.
Content
- Intro
- Preface
- Contents
- Methods and instrumentation
- 1 Identifying and estimating outliers in time series with nonstationary mean through multiobjective optimization method
- 2 Using the intentionally linked entities (ILE) database system to create hypergraph databases with fast and reliable relationship linking, with example applications
- 3 Rapid and automated determination of cluster numbers for high-dimensional big data: a comprehensive update
- 4 Canonical correlation analysis and exploratory factor analysis of the four major centrality metrics
- 5 Navigating the landscape of automated data preprocessing: an in-depth review of automated machine learning platforms
- 6 Generating random XML
- Applications and case studies
- 7 Exploring autism risk: a deep dive into graph neural networks and gene interaction data
- 8 Leveraging ChatGPT and table arrangement techniques in advanced newspaper content analysis for stock insights
- 9 An experimental study on road surface classification
- 10 RNN models for evaluating financial indices: examining volatility and demand-supply shifts in financial markets during COVID-19
- 11 Topological methods for vibration feature extraction
- 12 Dyna-SPECTS: DYNAmic enSemble of Price Elasticity Computation models using Thompson Sampling in e-commerce
- 13 Creating a metadata schema for reservoirs of data: a systems engineering approach
- 14 Implementation and evaluation of an eXplainable artificial intelligence to explain the evaluation of an assessment analytics algorithm for freetext exams in psychology courses in higher education to attest QBLM-based competencies
- 15 Toward a skill-centered qualification ontology supporting data mining of human resources in knowledge-based enterprise process representations
- Index
