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Data Science and Big Data Computing
This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by an authoritative collection of thirty-six researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics.
Topics and features:
Reviews a framework for fast data applications, a technique for complex event processing, and a selection of agglomerative approaches for partitioning of networksDiscusses a big data approach to identifying minimum-sized influential vertices from large-scale weighted graphsIntroduces a unified approach to data modeling and management, and offers a distributed computing perspective on interfacing physical and cyber worldsPresents techniques for machine learning in the context of big data, and describes an analytics-driven approach to identifying duplicate records in large data repositoriesExamines various enabling technologies and tools for data mining, including Apache HadoopProposes a novel framework for data extraction and knowledge discovery, and provides case studies on adaptive decision making and social media analysis
This comprehensive volume is a valuable reference for researchers, lecturers and students interested in data science and big data, in addition to professionals seeking to adopt the latest approaches in data analytics to gain business intelligence for strategic decision-making.
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