| 000 | 05364nam a22004095i 4500 | ||
|---|---|---|---|
| 710 | 2 |
_aSpringerLink (Online service) _0Local _9106996 |
|
| 999 |
_c86343 _d86343 _x1 |
||
| 001 | 86343 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230207040554.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 161027s2016 gw | s |||| 0|eng d | ||
| 020 | _a9783319448817 | ||
| 040 | _aES-MaUEC | ||
| 050 | 4 |
_aQA76.9.B45 _bR476 2016 |
|
| 082 | 0 | 4 | _a004.6 |
| 245 | 1 | 0 |
_aResource Management for Big Data Platforms : _bAlgorithms, Modelling, and High-Performance Computing Techniques _cedited by Florin Pop, Joanna Kołodziej, Beniamino Di Martino |
| 260 |
_aCham _bSpringer International Publishing _c2016 |
||
| 300 |
_a1 recurso en línea (XIII, 516 páginas) _b138 ilustraciones, 57 ilustraciones en color |
||
| 336 |
_aTexto _btxt _2rdacontent |
||
| 337 |
_aelectrónico _bc _2rdamedia |
||
| 338 |
_arecurso electrónico _bcr _2rdacarrier |
||
| 490 | 0 |
_aComputer Communications and Networks _x1617-7975 |
|
| 505 | 0 | _aPerformance Modeling of Big Data Oriented Architectures -- Workflow Scheduling Techniques for Big Data Platforms -- Cloud Technologies: A New Level for Big Data Mining -- Agent Based High-Level Interaction Patterns for Modeling Individual and Collective Optimizations Problems -- Maximize Profit for Big Data Processing in Distributed Datacenters -- Energy and Power Efficiency in the Cloud -- Context Aware and Reinforcement Learning Based Load Balancing System for Green Clouds -- High-Performance Storage Support for Scientific Big Data Applications on the Cloud -- Information Fusion for Improving Decision-Making in Big Data Applications -- Load Balancing and Fault Tolerance Mechanisms for Scalable and Reliable Big Data Analytics -- Fault Tolerance in MapReduce: A Survey -- Big Data Security -- Big Biological Data Management -- Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms -- Feature Dimensionality Reduction for Mammographic Report Classification -- Parallel Algorithms for Multi-Relational Data Mining: Application to Life Science Problems -- Parallelization of Sparse Matrix Kernels for Big Data Applications -- Delivering Social Multimedia Content with Scalability -- A Java-Based Distributed Approach for Generating Large-Scale Social Network Graphs -- Predicting Video Virality on Twitter -- Big Data uses in Crowd Based Systems -- Evaluation of a Web Crowd�Sensing IoT Ecosystem Providing Big Data Analysis -- A Smart City Fighting Pollution by Efficiently Managing and Processing Big Data from Sensor Networks. | |
| 520 | 3 | _aThis book constitutes a flagship driver towards presenting and supporting advance research in the area of Big Data platforms and applications. Extracting valuable information from raw data is especially difficult considering the velocity of growing data from year to year and the fact that 80% of data is unstructured. In addition, data sources are heterogeneous (various sensors, users with different profiles, etc.) and are located in different situations or contexts. Successful contributions may range from advanced technologies, applications and innovative solutions to global optimization problems in scalable large-scale computing systems to development of methods, conceptual and theoretical models related to Big Data applications and massive data storage and processing. The book provides, in this sense, a platform for the dissemination of advanced topics of theory, research efforts and analysis and implementation for Big Data platforms and applications being oriented on methods, techniques and performance evaluation. This book presents new ideas, analysis, implementations and evaluation of next-generation Big Data platforms and applications. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These subjects represent the main objectives of ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) and the research presented in these chapters was performed by joint collaboration of members from this action. This volume will serve as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and potential solutions for the selected topics. | |
| 650 | 0 | 7 |
_aSoftware _0 _2embne _9143838 |
| 650 | 7 |
_aRedes informáticas _0(OCoLC)872297 _2embne _0comprobar BNE19900997487 _9141354 |
|
| 700 | 1 |
_aPop, Florin. _eeditor literario _999994 _0Local |
|
| 700 | 1 |
_aKołodziej, Joanna _eeditor literario _0Local _997568 |
|
| 700 | 1 |
_aDi Martino, Beniamino _eeditor literario _0Local _999995 |
|
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-44881-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 901 | _ai9783319448817 | ||
| 907 |
_a.b12956211 _b10-10-17 _c21-11-16 |
||
| 942 |
_2lcc _cLE |
||
| 945 |
_aQA76.9.B45 R476 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11598153 _z06-04-17 |
||
| 988 | _aEBOOK, asignarmaterias , EBSPRINGER | ||
| 998 |
_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0 |
||