Cognitive computing and big data analytics:
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Buch |
Sprache: | English |
Veröffentlicht: |
Indianapolis, Indiana
Wiley
[2015]
|
Schlagworte: | |
Online-Zugang: | Cover Inhaltsverzeichnis |
Beschreibung: | xxi, 266 Seiten Illustrationen |
ISBN: | 9781118896624 |
Internformat
MARC
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Datensatz im Suchindex
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---|---|
adam_text | Contents
Introduction
:vn
Chapter 1 The Foundation of Cognitive Computing
Cognitive Computing as a New Generation
The Uses of Cognitive Systems
What Makes a System Cognitive?
Gaining Insights from Data
Domains Where Cognitive Computing Is Well Suited
Artificial Intelligence as the Foundation
of Cognitive Computing
Understanding Cognition
Two Systems of Judgment and Choice
System 1—Automatic Thinking: Intuition and Biases
System 2—Controlled, Rule-Centric, and Concentrated Effort
Understanding Complex Relationships
Between Systems
Types of Adaptive Systems
The Elements of a Cognitive System
Infrastructure and Deployment Modalities
Data Access, Metadata, and Management Services
The Corpus, Taxonomies, and Data Catalogs
Data Analytics Services
Continuous Machine Learning
Hypothesis Generation and Evaluation
The Learning Process
Presentation and Visualization Services
Cognitive Applications
Summary
‘II
2
2
3
4
5
6
11
12
13
14
15
16
17
1/
18
18
18
19
19
19
20
20
20
X
Contents
Chapter 2 Design Principles for Cognitive Systems 21
Components of a Cognitive System 22
Building the Corpus 23
Corpus Management Regulatory and
Security Considerations 25
Bringing Data into the Cognitive System 26
Leveraging Internal and External Data Sources 26
Data Access and Feature Extraction Services 27
Analytics Services 28
Machine Learning 29
Finding Patterns in Data 29
Supervised Learning 29
Reinforcement Learning 31
Unsupervised Learning 32
Hypotheses Generation and Scoring 33
Hypothesis Generation 34
Hypothesis Scoring 35
Presentation and Visualization Services 36
Infrastructure 37
Summary 37
Chapter 3 Natural Language Processing in Support of a
Cognitive System 39
The Role of NLP in a Cognitive System 40
The Importance of Context 40
Connecting Words for Meaning 42
Understanding Linguistics 43
Language Identification and Tokenization 43
Phonology 44
Morphology 44
Lexical Analysis 45
Syntax and Syntactic Analysis 45
Construction Grammars 46
Discourse Analysis 46
Pragmatics 47
Techniques for Resolving Structural Ambiguity 47
Importance of Hidden Markov Models 48
Word-Sense Disambiguation (WSD) 49
Semantic Web 50
Applying Natural Language Technologies
to Business Problems 50
Enhancing the Shopping Experience 50
Leveraging the Connected World of Internet of Things 51
Voice of the Customer 51
Fraud Detection 53
Summary 53
Contents
XI
Chapter 4 The Relationship Between Big Data and Cognitive Computing 55
Dealing with Human-Generated Data 55
Defining Big Data 56
Volume, Variety, Velocity, and Veracity 56
The Architectural Foundation for Big Data 57
The Physical Foundation for Big Data 58
Security Infrastructure 58
Operational Databases 58
Role of Structured and Unstructured Data 59
Data Services and Tools 61
Analytical Data Warehouses 61
Big Data Analytics 62
Hadoop 64
Data in Motion and Streaming Data 67
Analyzing Dark Data 68
integration of Big Data with Traditional Data 69
Summary 70
Chapter 5 Representing Knowledge m Taxonomies and Ontologies 1%
Representing Knowledge 71
Developing a Cognitive System 72
Defining Taxonomies and Ontologies 73
Explaining How to Represent Knowledge 75
Managing Multiple Views of Knowledge 79
Models for Knowledge Representation 80
Taxonomies 80
Ontologies 81
Other Methods of Knowledge Representation 83
Simple Trees 83
The Semantic Web 83
The Importance of Persistence and State 84
Implementation Considerations 85
Summary 85
Chapter 6 Applying Advanced Analytics to Cognitive Competing 87
Advanced Analytics Is ori a Path to Cognitive Computing 87
Key Capabilities in Advanced Analytics 91
The Relationship Between Statistics, Data Mining,
and Machine Learning 92
Using Machine Learning in the Analytics Process 93
Supervised Learning 94
Unsupervised Learning 96
Predictive Analytics 98
Business Value of Predictive Analytics 98
Text Analytics 99
Business Value of Text Analytics 100
XII
Contents
Image Analytics 101
Speech Analytics 103
Using Advanced Analytics to Create Value 104
Building Value with In-memory Capabilities 105
Impact of Open Source Tools on Advanced Analytics 106
Summary 106
Chapter 7 The Role of Cloud and Distributed Computing in
Cognitive Computing 109
Leveraging Distributed Computing for
Shared Resources 109
Why Cloud Services Are Fundamental to
Cognitive Computing Systems 110
Characteristics of Cloud Computing 111
Elasticity and Self-service Provisioning 111
Scaling 111
Distributed Processing 111
Cloud Computing Models 112
The Public Cloud 112
The Private Cloud 114
Managed Service Providers 114
The Hybrid Cloud Model 115
Delivery Models of the Cloud 117
Infrastructure as a Service 117
Virtualization 117
Software-defined Environment 118
Containers 118
Software as a Service 118
Platform as a Service 120
Managing Workloads 120
Security and Governance 121
Data Integration and Management in the Cloud 122
Summary 122
Chapter 8 The Business Implications of Cognitive
Computing 125
Preparing for Change 125
Advantages of New Disruptive Models 126
What Does Knowledge Mean to the Business? 127
The Difference with a Cognitive Systems Approach 128
Meshing Data Together Differently 129
Using Business Knowledge to Plan
for the Future 131
Answering Business Questions in New Ways 134
Building Business Specific Solutions 134
Making Cognitive Computing a Reality 135
How a Cognitive Application Can Change a Market 136
Summary 136
Contents xiii
Chapter 9 IBM s Watson as a Cognitive System 137
Watson Defined 137
How Watson Is Different from Other Search Engines 138
Advancing Research with a Grand Challenge 139
Preparing Watson for Jeopardy! 140
Preparing Watson for Commercial Applications 141
Watson s Software Architecture 142
The Components of DeepQA Architecture 144
Building the Watson Corpus: Answer and Evidence Sources 145
Source Acquisition 146
Source Transformation 146
Source Expansion and Updates 147
Question Analysis 148
Slot Grammar Parser and Components for
Semantic Analysis 149
✓
Question Classification 150
Hypothesis Generation 152
Scoring and Confidence Estimation 153
Summary 154
Chapter 10 The Process of Building a Cognitive Арріісыііші W7
The Emerging Cognitive Platform 158
Defining the Objective 159
Defining the Domain 160
Understanding the Intended Users and Defining
their Attributes 161
Defining Questions and Exploring Insights 162
Typical Question-Answer Pairs 163
Anticipatory Analytics 164
Acquiring the Relevant Data Sources 166
The Importance of Leveraging Structured Data Sources 166
Analyzing Dark Data 167
Leveraging External Data 167
Creating and Refining the Corpora 168
Preparing the Data 169
ingesting the Data 169
Refining and Expanding the Corpora 170
Governance of Data 171
Training and Testing 1/1
Summary 173
Chapter Vi Building a Cognitive Healthcare Application
Foundations of Cognitive Computing for Healthcare 1.76
Constituents in the Healthcare Ecosystem 177
Learning from Patterns in Healthcare Data 179
Building on a Foundation of Big Data Analytics 180
Cognitive Applications across the Healthcare Ecosystem 181
xiv Contents
Two Different Approaches to Emerging Cognitive
Healthcare Applications 181
The Role of Healthcare Ontologies in a
Cognitive Application 182
Starting with a Cognitive Application for Healthcare 183
Define the Questions Users will Ask 183
Ingest Content to Create the Corpus 184
Training the Cognitive System 185
Question Enrichment and Adding to the Corpus 185
Using Cognitive Applications to Improve Health
and Wellness 186
Welltok 187
Overview of Welltok s Solution 187
CaféWell Concierge in Action 190
GenieMD 191
Consumer Health Data Platforms 191
Using a Cognitive Application to Enhance
the Electronic Medical Record 191
Using a Cognitive Application to Improve
Clinical Teaching 193
Summary 195
Chapter 12 Smarter Cities: Cognitive Computing in Government 197
How Cities Have Operated 197
The Characteristics of a Smart City 199
Collecting Data for Planning 200
Managing Operations 201
Managing Security and Threats 202
Managing Citizen-produced Documentation and Data 202
Data Integration Across Government Departments 203
The Rise of the Open Data Movement Will Fuel
Cognitive Cities 204
The Internet of Everything and Smarter Cities 204
Understanding the Ownership and Value of Data 205
Cities Are Adopting Smarter Technology Today
for Major Functions 206
Managing Law Enforcement Issues Cognitively 207
The Problem of Correlating Crime Data 207
The COPLink Project 208
Smart Energy Management: From Visualization
to Distribution 209
The Problem of Integrating Regional Utilities
Management 209
The Area Energy Management Solutions Project 209
The Cognitive Computing Opportunity 210
Protecting the Power Grid with Machine Learning 211
The Problem of Identifying Threats from New Patterns 211
Contents XV
The Grid Cybersecurity Analytics Project 211
The Cognitive Computing Opportunity 211
Improving Public Health with Cognitive
Community Services 212
Smarter Approaches to Preventative Healthcare 212
The Town Health Station Project 212
The Cognitive Computing Opportunity 213
Building a Smarter Transportation Infrastructure 213
Managing Traffic in Growing Cities 213
The Adaptive Traffic Signals Controller Project 214
The Cognitive Computing Opportunity 214
Using Analytics to Close the Workforce Skills Gap 215
Identifying Emerging Skills Requirements
and Just-iivTime Training 215
The Digital On-Ramps (DOR) Project 215
The Cognitive Computing Opportunity 216
Creating a Cognitive Community Infrastructure 217
The Smart + Connected Communities Initiative 217
The Cognitive Computing Opportunity 218
The Next Phase of Cognitive Cities 218
Summary 219
Chapter 13 Emerging Cognitive Computing Areas 221
Characteristics of Ideal Markets for Cognitive
Computing 222
Vertical Markets and Industries 223
Retail 224
Cognitive Computing Opportunities 224
Retail Staff Training and Support 226
Travel 226
Cognitive Computing Opportunities for the
Travel Industry 227
Transportation and Logistics 228
Cognitive Computing Opportunities for
Transportation and Logistics 228
Telecommunications 229
Cognitive Computing Opportunities for
Telecommunications 229
Security and Threat Detection 230
j
Cognitive Computing Opportunities for
Security and Threat Detection 230
Other Areas That Are Impacted by a Cognitive Approach 231
Call Centers 231
Cognitive Computing Opportunities 231
Solutions in Other Areas 232
Summary 233
xvi Contents
Chapter 14 Future Applications for Cognitive Computing
Requirements for the Next Generation
Leveraging Cognitive Computing to Improve Predictability
The New Life Cycle for Knowledge Management
Creating Intuitive Human-to-Machine Interfaces
Requirements to Increase the Packaging of Best Practices
Technical Advancements That Will Change
the Future of Cognitive Computing
What the Future Will Look Like
The Next Five Years
Looking at the Long Term
Emerging Innovations
Deep QA and Hypothesis Generation
NLP
Cognitive Training Tools
Data Integration and Representation
Emerging Hardware Architectures
Neurosynaptic Architectures
Quantum Architectures
Alternative Models for Natural Cognitive Models
Summary
235
236
236
236
237
238
239
239
240
241
242
243
243
244
245
245
246
248
249
249
251
Index
261
|
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author | Hurwitz, Judith 1951- Kaufman, Marcia Bowles, Adrian |
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author_sort | Hurwitz, Judith 1951- |
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building | Verbundindex |
bvnumber | BV042539175 |
classification_rvk | ST 265 ST 300 |
ctrlnum | (OCoLC)911201208 (DE-599)BSZ425729168 |
discipline | Informatik |
format | Book |
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spellingShingle | Hurwitz, Judith 1951- Kaufman, Marcia Bowles, Adrian Cognitive computing and big data analytics Künstliche Intelligenz (DE-588)4033447-8 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
subject_GND | (DE-588)4033447-8 (DE-588)4802620-7 (DE-588)4123037-1 |
title | Cognitive computing and big data analytics |
title_auth | Cognitive computing and big data analytics |
title_exact_search | Cognitive computing and big data analytics |
title_full | Cognitive computing and big data analytics Judith Hurwitz, Marcia Kaufman, Adrian Bowles |
title_fullStr | Cognitive computing and big data analytics Judith Hurwitz, Marcia Kaufman, Adrian Bowles |
title_full_unstemmed | Cognitive computing and big data analytics Judith Hurwitz, Marcia Kaufman, Adrian Bowles |
title_short | Cognitive computing and big data analytics |
title_sort | cognitive computing and big data analytics |
topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
topic_facet | Künstliche Intelligenz Big Data Datenanalyse |
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