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Computational Science and Engineering part 3下载
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2019-05-16 01:30:16
Computational Science and Engineering mit 18.085的教材 part 3
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Computational Science and Engineering part 3下载
Computational Science and Engineering mit 18.085的教材 part 3 相关下载链接://download.csdn.net/download/cock_puncher/2227831?utm_source=bbsseo
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Com
putat
ion
al
Science
and
Engine
ering
Com
putat
ion
al
Science
and
Engine
ering
mit 18.085的教材 part1
Com
putat
ion
al
Science
and
Engine
ering
part 3
Com
putat
ion
al
Science
and
Engine
ering
mit 18.085的教材 part 3
Com
putat
ion
al
Interact
ion
Com
putat
ion
al
Interact
ion
ISBN-10 书号: 0198799616 ISBN-13 书号: 9780198799610 出版日期: 2018-04-08 pages 页数: (432) Edited by ANTTI OULASVIRTA Associate Professor A
al
to University PER OLA KRISTENSSON University Reader in Interactive Systems
Engine
ering
University of Cambridge XIAOJUN BI Assistant Professor Stony Brook University ANDREW HOWES Professor and Head of School at the School of
Com
puter
Science
University of Birmingham This book presents
com
putat
ion
al
interact
ion
as an approach to explaining and enhancing the interact
ion
between humans and informat
ion
technology.
Com
putat
ion
al
interact
ion
applies abstract
ion
, automat
ion
, and an
al
ysis to inform our understanding of the structure of interact
ion
and
al
so to inform the design of the software that drives new and exciting human-
com
puter interfaces. The methods of
com
putat
ion
al
interact
ion
al
low, for example, designers to identify user interfaces that are optim
al
against some objective criteria. They
al
so
al
low software
engine
ers to build interactive systems that adapt their behaviour to better suit individu
al
capacities and preferences. Embedded in an iterative design process,
com
putat
ion
al
interact
ion
has the potenti
al
to
com
plement human strengths and provide methods for generating inspiring and elegant designs.
Com
putat
ion
al
interact
ion
does not exclude the messy and
com
plicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variat
ion
s between individu
al
users. It
al
so promotes the idea that there are many aspects of interact
ion
that can be augmented by
al
gorithms. This book introduces
com
putat
ion
al
interact
ion
design to the reader by exploring a wide range of
com
putat
ion
al
interact
ion
techniques, strategies and methods. It explains how techniques such as optimisat
ion
, economic modelling, machine learning, control theory, form
al
methods, cognitive models and statistic
al
language processing can be used to model interact
ion
and design m
Big Data and
Com
putat
ion
al
Intelligence in Networking-CRC(2018).pdf
Recent years have witnessed a deluge of network data propelled by the emerging online soci
al
media, user-generated video contents, and glob
al
-sc
al
e
com
muni- cat
ion
s, bringing people into the era of big data. Such network big data holds much critic
al
and v
al
uable informat
ion
including customer experiences, user behaviors, service levels, and other contents, which could significantly improve the efficiency, effectiveness, and intelligence on the optimizat
ion
of the current Internet, facilitate the smart network operat
ion
and management, and help service providers and content providers reduce capit
al
expenditure (CapEx) and opera- t
ion
al
expenditure (OpEx) while maintaining a relatively high-level qu
al
ity of service (QoS) and qu
al
ity of experience (QoE). Typic
al
examples of network intelligence received from network big data include rapid QoE impairment detect
ion
and mitigat
ion
, optimizat
ion
of network asset utilizat
ion
, proactive maintenance, rapid outage restorat
ion
, and graceful disaster recovery. These aims can be achieved from high-level
com
putat
ion
al
intelligence based on emerging an
al
ytic
al
techniques such as big data pro- cessing, Web an
al
ytics, and network an
al
ytics employing software tools from advanced an
al
ytics disciplines such as machine learning, data mining, and pre- dictive an
al
ytics. The
com
putat
ion
al
intelligence for big data an
al
ysis is playing an ever-increasingly important role in supporting the evolut
ion
of the current Internet toward the next-generat
ion
intelligent Internet. However, the unstructured, heterogeneous, sheer volume and
com
plex nature of network big data pose great ch
al
lenges on the
com
putat
ion
al
intelligence of these emerging an
al
ytic
al
techniques due to high
com
putat
ion
al
overhead and
com
municat
ion
cost, non-re
al
-time response, sparse matrix-vector multi- plicat
ion
s, and high convergence time. It is therefore of critic
al
importance to understand network big data and design novel solut
ion
s of
com
putat
ion
al
intelligence, sc
al
ing up for big data an
al
ytics of large-sc
al
e networks to auto- matic
al
ly discover the hidden and v
al
uable informat
ion
available for smart network operat
ion
s, management, and optimizat
ion
. This has been established as ix x Preface a new cross-discipline research topic in
com
puter
science
, requiring anticipat
ion
of technic
al
and practic
al
ch
al
lenges faced by mixed methods across multiple disciplines. In this book, we have invited world experts in this area to contribute the chapters that cover the following four parts: 1. Part1:BasicsofNetworkedBigData:Thisparthelpsunderstandtheprop- erties, characteristics, ch
al
lenges, and opportunities of networked big data, geospati
al
data, and wireless big data. This part covers the following: a. Mathematic
al
properties: A variety of aspects related to networks, including their topologic
al
and dynamic
al
properties, as well as their applicat
ion
s to re
al
-world examples b. Geospati
al
data and geospati
al
semantic web: Ch
al
lenges and opportunities of the geospati
al
semantic web brought for sharing and utilizing big geospati
al
data c. Big data over wireless networks: Typic
al
scenarios, various ch
al
- lenges, and potenti
al
solut
ion
s for wireless transmiss
ion
of big data 2. Part 2: Network Architecture for Big Data Transmiss
ion
s: This part presents new propos
al
s and network architectures to ensure efficient big data transmiss
ion
s and streaming big data processing. a. Big data transfer: Ch
al
lenges of bandwidth reservat
ion
service for efficient big data transfer and the potenti
al
solut
ion
s b. Internet of Things (IoT): A dynamic and independent Cloud
com
- puting architecture based on a service-oriented architecture for IoT devices, to
al
low users to freely transfer their IoT devices from one vendor to another c. Streamingbigdataprocessing:HowtomaximizeQoSandminimize OpEx when performing task scheduling and resource
al
locat
ion
in geo-distributed Clouds 3. Part3:An
al
ysisandProcessingofNetworkedBigData:Thispartexplains how to perform big data an
al
ytics based on emerging an
al
ytic
al
techniques such as big data an
al
ytics, Web an
al
ytics, network an
al
ytics, and advanced an
al
ytics disciplines such as machine learning, data mining, and predictive an
al
ytics. This part covers the following areas: a.
Al
ternatingdirect
ion
methodofmultiplier(ADMM):Itsapplicat
ion
s to large-sc
al
e network optimizat
ion
s b. Dynamicnetworkmanagementandoptimizat
ion
:Rethinkofcurrent network an
al
ysis, management and operat
ion
practices; impact of Preface xi network evolut
ion
on the
com
putat
ion
of key network metrics; hyperbolic big data an
al
ytics c. Predictivean
al
yticsandsmartretriev
al
:Utilizethenetworkbigdata by performing a data, informat
ion
, knowledge, and wisdom (DIKW) hierarchy to the product of its processes d. Re
com
mendat
ion
systems: Key ch
al
lenges and solut
ion
s for data sparsity problem, data sc
al
e issue, and cold-start problem e. Coordinate gradient descent methods: Unconstrained convex mini- mizat
ion
problems with differentiable objective funct
ion
in network problems f. MapReduce: Data loc
al
ity and dependency an
al
ysis; dependency- aware loc
al
ity for MapReduce g. Distributed machine learning: Big data and big models for network big data; how to par
al
lelize parameter updates on multiple work- ers; how to synchronize concurrent parameter updates performed by multiple workers h. Biggraph:Biggraphde
com
posit
ion
;re
al
-timeandlarge-sc
al
egraph processing; big data security 4. Part 4: Emerging Applicat
ion
s of Networked Big Data: This part covers some emerging applicat
ion
s on the following: a. Intelligent m
al
l shopping: Locat
ion
-based mobile augmented re
al
- ity applicat
ion
s; using network data to enable intelligent shopping; robust feature learning in cold-start heterogeneous-device loc
al
iza- t
ion
; learning to query in the cold-start retailer content b. Networkanom
al
ydetect
ion
:Howtoefficientlyusenetworkbigdata to perform accurate anom
al
y detect
ion
c. Transportat
ion
: Advances of spati
al
network big data (SNBD) tech- niques; ch
al
lenges posed by SNBD in transportat
ion
applicat
ion
s and the potenti
al
solut
ion
s d. Biomedic
al
and soci
al
media domain: Graph as a representat
ion
schema for big data; graph-based models and an
al
yses in soci
al
text mining, and bioinformatics and biomedic
al
e. Smart manufacturing: Big data characteristics in manufacturing; data collect
ion
and data mining in manufacturing; applicat
ion
s of big data in manufacturing. This book presents the state-of-the-art solut
ion
s to the theoretic
al
and prac- tic
al
ch
al
lenges stemming from the leverage of big data and its
com
putat
ion
al
intelligence in supporting smart network operat
ion
, management, and optimiza- t
ion
. In particular, the technic
al
focus covers the
com
prehensive understanding xii Preface of network big data, efficient collect
ion
and management of network big data, distributed and sc
al
able online an
al
ytics for network big data, and emerging applicat
ion
s of network big data for
com
putat
ion
al
intelligence. Targeted audiences: This book targets both academia and industry readers. Grad- uate students can select promising research topics from this book that are suitable for their thesis or dissertat
ion
research. Researchers will have a deep under- standing of the ch
al
lenging issues and opportunities of network big data and can thus easily find an unsolved research problem to pursue. Industry
engine
ers from IT
com
panies, service providers, content providers, network operators, and equipment manufacturers can get to know the
engine
ering
design issues and cor- responding solut
ion
s after reading some practic
al
schemes described in some chapters. We have required
al
l chapter authors to provide as much technic
al
detail as possible. Each chapter
al
so includes references for readers’ further studies and investigat
ion
s. If you have any
com
ments or quest
ion
s on certain chapters, please contact the chapter authors for more informat
ion
. Thank you for reading this book. We wish that this book will help you with the scientific research and practic
al
problems of network big data.
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