Big Data, Data Mining and Data Science: Algorithms, Infrastructures, Management and SecurityКНИГИ » ОС И БД
Название: Big Data, Data Mining and Data Science: Algorithms, Infrastructures, Management and Security Автор: George Dimitoglou, Leonidas Deligiannidis, Hamid R. Arabnia Издательство: De Gruyter Серия: Intelligent Computing Год: 2025 Страниц: 350 Язык: английский Формат: pdf (true), epub Размер: 66.3 MB
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.
From a computing perspective in our data-rich world, Big Data, Data Mining, and Data Science collectively leverage data to uncover hidden knowledge and solve complex problems. Big Data deals with the vast volumes, velocity, and variety of structured and unstructured data. Data Mining focuses on extracting meaningful patterns and insights from large datasets for predictive modeling and decision support. Data Science aims to extract actionable insights using various techniques to solve complex problems and drive decision-making. These techniques are applied to diverse problems and domains, such as the financial sector, healthcare, e-commerce, and cybersecurity.
The work presented in this book can be loosely categorized into two distinct themes. The first theme is “methods and instrumentation,” where authors provide insight into systematic methods, procedures, and techniques within a research or experimental framework and the tooling to measure, observe, or manipulate variables of interest. In this thematic collection, papers explore a range of topics such as hypergraph databases, automated determination of cluster numbers for high-dimensional big data, centrality metrics for identifying dominant factors in datasets, Machine Learning-based data preprocessing approaches, estimation of time-series outliers using multi-objective optimization with non-stationary means, and the development of languages for generating random data to facilitate random testing of hardware and software applications.
The second theme is “applications and case studies,” where authors apply and implement theories, techniques, methodologies, and technologies in specific contexts, showcasing their practical relevance and effectiveness. In this thematic collection, papers explore a range of topics such as using high-volume dynamic ensemble-based model computations in e-commerce, deploying explainable Artificial Intelligence (AI) to explain an assessment analytics algorithm for free text exams, using graph neural networks (NN) and gene interaction data, applying recurrent neural network (RNN) models to examine the volatility in financial markets during a global pandemic, using skill-centered qualification ontologies to support data mining of human resources in knowledge-based enterprise process-representations, extracting information from vibration sensor data using topological data analysis, leveraging Generative AI (GenAI) and table arrangement techniques to analyze newspaper stories for stock price insight, creating metadata schemas for data reservoirs, and exploring the discrimination capabilities of a set of features for road surface classification.
- Presents advances in Big Data, Data Mining, and Data Science research and applications. - Discusses novel algorithms, techniques, methodologies, infrastructures, frameworks, and privacy preservation. - Includes Real-world case studies and use cases.
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