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020 _a9783319318615
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050 4 _aQA76.9.B45
_bD383 2016
082 0 4 _a005.74
245 1 0 _aData Science and Big Data Computing :
_bFrameworks and Methodologies
_cedited by Zaigham Mahmood
260 _aCham
_bSpringer International Publishing
_c2016
300 _a1 recurso en línea (XXI, 319 páginas)
_b68 ilustraciones
336 _aTexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
505 0 _aPart I: Data Science Applications and Scenarios -- An Interoperability Framework and Distributed Platform for Fast Data Applications -- Complex Event Processing Framework for Big Data Applications -- Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios -- Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective -- Part II: Big Data Modelling and Frameworks -- A Unified Approach to Data Modelling and Management in Big Data Era -- Interfacing Physical and Cyber Worlds: A Big Data Perspective -- Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data -- An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data Repositories -- Part III: Big Data Tools and Analytics -- Large Scale Data Analytics Tools: Apache Hive, Pig and HBase -- Big Data Analytics: Enabling Technologies and Tools -- A Framework for Data Mining and Knowledge Discovery in Cloud Computing -- Feature Selection for Adaptive Decision Making in Big Data Analytics -- Social Impact and Social Media Analysis Relating to Big Data.
520 3 _aThis 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 networks Discusses a big data approach to identifying minimum-sized influential vertices from large-scale weighted graphs Introduces a unified approach to data modeling and management, and offers a distributed computing perspective on interfacing physical and cyber worlds Presents techniques for machine learning in the context of big data, and describes an analytics-driven approach to identifying duplicate records in large data repositories Examines various enabling technologies and tools for data mining, including Apache Hadoop Proposes 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.
650 7 _aRedes informáticas
_0(OCoLC)872297
_2embne
_0comprobar BNE19900997487
_9141354
650 7 _aData mining
_0(OCoLC)887946
_2embne
_0comprobar BNE20033218554
_9162648
700 1 _aMahmood, Zaigham.
_eeditor literario
_999126
_0Local
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-31861-5
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901 _ai9783319318615
907 _a.b12951389
_b10-10-17
_c21-11-16
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