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008 220601s2016 sz | s |||| 0|eng d
020 _a9783031017490
024 7 _a10.1007/978-3-031-01749-0
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA76.9.A73
_b2016 EB
100 1 _aBordawekar, Rajesh
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9686866
245 1 0 _aAnalyzing Analytics
_cby Rajesh Bordawekar, Bob Blainey, Ruchir Puri
250 _a1st edition 2016
264 1 _aCham
_bSpringer International Publishing
_c2016
300 _a1 recurso en línea (X, 118 páginas)
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
490 0 _aSynthesis Lectures on Computer Architecture
_x1935-3243
505 0 _aIntroduction -- Overview of Analytics Exemplars -- Accelerating Analytics -- Accelerating Analytics in Practice: Case Studies -- Architectural Desiderata for Analytics -- Bibliography -- Authors' Biographies .
520 _aThis book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.
988 _aSynthesis Collection of Technology_2016
700 1 _aBlainey, Bob
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9686867
700 1 _aPuri, Ruchir,
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9686868
_d1966-
776 0 8 _iPrinted edition:
_z9783031006210
776 0 8 _iPrinted edition:
_z9783031028779
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01749-0
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b02/2023
_dz
_esc
_zSI