Atjaunināt sīkdatņu piekrišanu

E-grāmata: Analytics and Knowledge Management

5.00/5 (1 ratings by Goodreads)
Edited by , Edited by
  • Formāts: 446 pages
  • Sērija : Data Analytics Applications
  • Izdošanas datums: 06-Aug-2018
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781351807005
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 50,71 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Analytics and Knowledge ManagementPalielināt
  • Formāts: 446 pages
  • Sērija : Data Analytics Applications
  • Izdošanas datums: 06-Aug-2018
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781351807005
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations.

The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics.

Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics.

Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships.

This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.
Preface vii
Editors xi
Contributors xiii
1 Knowledge Management for Action-Oriented Analytics 1(30)
John S. Edwards
Eduardo Rodriguez
Introduction
2(4)
Categorizing Analytics Projects
6(2)
Classification by Technical Type
6(1)
Classification from a Business Perspective
7(1)
How Analytics Developed
8(1)
Analytics, Operations Research/Management Science, and Business Intelligence
9(1)
Overview of Analytics Examples
10(2)
Single-Project Examples
12(8)
Strategic Analytics
12(2)
Managerial Analytics
14(3)
Operational Analytics
17(1)
Customer-Facing Analytics
18(2)
Scientific Analytics
20(1)
Multiple Project Examples
20(3)
Future Developments
23(3)
Organizational Environment
23(1)
Political Environment
24(1)
Analytics Workflow Embedded in Business Processes
25(1)
Conclusion
26(1)
References
27(4)
2 Data Analytics Process: An Application Case on Predicting Student Attrition 31(36)
Dursun Delen
Introduction to Data Analytics Processes
32(14)
Knowledge Discovery in Databases Process
32(2)
Cross-Industry Standard Process for Data Mining
34(5)
Step 1 Business Understanding
35(1)
Step 2 Data Understanding
35(1)
Step 3 Data Preparation
36(1)
Step 4 Model Building
37(1)
Step 5 Testing and Evaluation
38(1)
Step 6 Deployment
39(1)
Sample, Explore, Modify, Model, Assess Process
39(3)
Step 1 Sample
40(1)
Step 2 Explore
41(1)
Step 3 Modify
41(1)
Step 4 Model
41(1)
Step 5 Assess
42(1)
Six Sigma for Data Analytics
42(3)
Step 1 Define
44(1)
Step 2 Measure
44(1)
Step 3 Analyze
45(1)
Step 4 Improve
45(1)
Step 5 Control
45(1)
Which Process Is the Best?
45(1)
Application Case: Predicting Student Attrition with Data Analytics
46(17)
Introduction and Motivation
47(2)
Analytics Methodology
49(8)
Data Description
50(4)
Predictive Analytics Models
54(2)
Sensitivity Analysis
56(1)
Results
57(2)
Discussion and Conclusions
59(4)
References
63(4)
3 Transforming Knowledge Sharing in Twitter-Based Communities Using Social Media Analytics 67(54)
Nicholas Evangelopoulos
Shadi Shakeri
Andrea R. Bennett
Introduction
68(1)
Collective Knowledge within Communities of Practice
69(2)
Evolution of Analytics in Knowledge Management
71(4)
Social Media Analytics
75(6)
Twitter-Based Communities as Communities of Practice
76(1)
Twitter-Based Communities as Organizations
77(47)
Transforming Tacit Knowledge in Twitter-Based Communities
78(1)
Representing Twitter-Based Community Knowledge in a Dimensional Model
78(3)
User Dimension
81(4)
Interaction among Users
85(7)
Time Dimension
92(2)
Location Dimension
94(3)
Topic Dimension
97(5)
Topic-Time Interaction
102(3)
Opinion Dimension
105(5)
Opinion-Location Interaction
110(3)
Conclusion and the Road Ahead
113(2)
Summary
115(1)
References
116(5)
4 Data Analytics for Deriving Knowledge from User Feedback 121(20)
Kuljit Kaur Chahal
Salil Vishnu Kapur
Introduction
121(2)
Collecting User Feedback
123(1)
Analyzing User Feedback
124(4)
Opinion Mining
125(2)
Link Analysis
127(1)
User Feedback Analysis: The Existing Work
128(2)
Deriving Knowledge from User Feedback
130(7)
Data Management
133(1)
Data Analytics
134(2)
Knowledge Management
136(1)
Conclusions and Future Work
137(1)
References
138(3)
5 Relating Big Data and Data Science to the Wider Concept of Knowledge Management 141(26)
Hillary Stark
Suliman Hawamdeh
Introduction
142(1)
The Wider Concept of Knowledge Management
143(1)
The Shift in Data Practices and Access
144(2)
Data Science as a New Paradigm
146(1)
Big Data Cost and Anticipated Value
147(1)
Information Visualization
148(4)
Data Analytics Tools
152(5)
Applications and Case Studies
157(3)
Emerging Career Opportunities
160(3)
Conclusion
163(1)
References
163(4)
6 Fundamentals of Data Science for Future Data Scientists 167(28)
Jiangping Chen
Brenda Reyes Ayala
Duha Alsmadi
Guonan Wang
Data, Data Types, and Big Data
168(2)
Data Science and Data Scientists
170(4)
Defining Data Science: Different Perspectives
170(2)
Most Related Disciplines and Fields for Data Science
172(1)
Data Scientists: The Professions of Doing Data Science
173(1)
Data Science and Data Analytics Jobs: An Analysis
174(8)
Purposes of Analysis and Research Questions
175(1)
Data Collection
175(1)
Data Cleanup and Integration
176(1)
Tools for Data Analysis
176(1)
Results and Discussion
177(5)
Characteristics of the Employers
177(1)
Job Titles
178(1)
Word Cloud and Clusters on Qualifications and Responsibilities
178(4)
Summary of the Job Posting Analysis
182(1)
Data Science Education: Current Data Science Programs and Design Considerations
182(10)
Data Science Programs Overview
182(9)
PhD Programs in Data Science
182(4)
Masters Programs in Data Science
186(1)
Graduate Certificate Programs in Data Science
186(1)
Massive Open Online Courses
186(5)
Bootcamps
191(1)
Data Science Program: An Integrated Design
191(1)
Summary and Conclusions
192(1)
References
193(2)
7 Social Media Analytics 195(26)
Miyoung Chong
Hsia-Ching Chang
Introduction
196(3)
Historical Perspective of Social Networks and Social Media
199(2)
Evolution of Analytics
201(2)
Social Media Analytics
203(4)
Defining Social Media Analytics
203(1)
Processes of Social Media Analytics
204(3)
Social Media Analytics Techniques
207(4)
Identifying Data Sources
207(1)
Data Acquisition
208(1)
Data Analysis Techniques
208(3)
Sentiment Analysis
208(1)
Topic Modeling
209(1)
Visual Analytics
210(1)
Stream Processing
211(1)
Social Media Analytics Tools
211(3)
Scientific Programming Tools
211(1)
Network Visualization Tools
212(1)
Business Applications
212(1)
Social Media Monitoring Tools
213(1)
Text Analysis Tools
213(1)
Data Visualization Tools
213(1)
Social Media Management Tools
214(1)
Representative Fields of Social Media Analytics
214(1)
Conclusions
215(1)
References
216(5)
8 Transactional Value Analytics in Organizational Development 221(30)
Christian Stary
Introduction
222(1)
Value Network Analysis
223(1)
Organizations as Self-Adapting Complex Systems
224(2)
Patterns of Interaction as Analytical Design Elements
226(1)
Tangible and Intangible Transactions
226(1)
Value Network Representation of Organizations
227(4)
Analyzing the Value Network
231(7)
How to Ensure Coherent Value Creation
238(1)
Eliciting Methodological Knowledge
238(3)
Externalizing Value Systems through Repertory Grids
241(4)
Developing Commitment for an Organizational Move
245(1)
Conclusive Summary
246(1)
References
247(4)
9 Data Visualization Practices and Principles 251(26)
Jeonghyun Kim
Eric R. Schuler
Introduction
251(2)
Data Visualization Practice
253(12)
Multidimensional Visualization
253(7)
Hierarchical Data Visualization
260(5)
Data Visualization Principles
265(8)
General Principles
267(4)
Specific Principles: Text
271(1)
Specific Principles: Color
271(1)
Specific Principles: Layout
272(1)
Implications for Future Directions
273(1)
Note
274(1)
References
274(3)
10 Analytics Using Machine Learning-Guided Simulations with Application to Healthcare Scenarios 277(48)
Mahmoud Elbattah
Owen Molloy
Introduction
279(1)
Motivation
280(2)
Simulation Modeling and Machine Learning: Toward More Integration
280(1)
The Prospective Role of Machine Learning in Simulation Modeling
281(1)
Related Work
282(2)
Hybrid Simulations
282(1)
Artificial Intelligence-Assisted Simulations
283(1)
Simulation-Based Healthcare Planning
283(1)
Background: Big Data, Analytics, and Simulation Modeling
284(7)
Definitions of Big Data
284(1)
Characteristics of Big Data
284(3)
Analytics
287(1)
Simulation Modeling
288(2)
What Can Big Data Add to Simulation Modeling?
290(1)
Analytics Use Case: Elderly Discharge Planning
291(2)
Case Description
291(1)
Questions of Interest
292(1)
Overview of Analytics Approach
293(1)
Data Description
293(3)
Unsupervised Machine Learning: Discovering Patient Clusters
296(6)
Outliers Removal
296(1)
Feature Scaling
297(1)
Features Extraction
297(1)
Clustering Approach
298(1)
Selected Features
298(1)
Clustering Experiments
298(2)
Exploring Clusters
300(2)
Modeling Cluster-Based Flows of Patients
302(3)
Initial System Dynamics Model
302(1)
Model Assumptions and Simplifications
302(1)
Cluster-Based System Dynamics Model
303(2)
Modeling Patient's Care Journey
305(3)
Simulation Approach
305(2)
Generation of Patients
307(1)
Model Implementation
307(1)
Supervised Machine Learning: Predicting Care Outcomes
308(6)
Overview of Predictive Models
308(1)
Significance of Predictive Models
309(1)
Training Data
309(1)
Data Preprocessing
309(2)
Feature Extraction
310(1)
Tackling Data Imbalances
310(1)
Feature Selection
311(1)
Learning Algorithm: Random Forests
312(1)
Predictors Evaluation
312(2)
Results and Discussion
314(2)
Model Verification and Validation
316(1)
Model Verification
316(1)
Model Validation
317(1)
Future Directions
317(1)
Study Limitations
318(1)
Conclusions
319(1)
References
319(6)
11 Intangible Dynamics: Knowledge Assets in the Context of Big Data and Business Intelligence 325(30)
G. Scott Erickson
Helen N. Rothberg
Introduction
326(1)
A Wider View of Intangibles
326(2)
Big Data and Business Analytics/Intelligence
328(1)
Reimagining the Intangibles Hierarchy
329(4)
Assessment of the Intelligence Hierarchy in Organizations
333(3)
Measuring Intangible Asset Scenarios
336(4)
Intangible Assets and Metrics: Illustrative Applications
340(11)
Healthcare
340(4)
Financial Services
344(3)
Automated Driving
347(4)
Conclusions
351(1)
References
352(3)
12 Analyzing Data and Words-Guiding Principles and Lessons Learned 355(52)
Denise A.D. Bedford
Introduction
356(1)
Conceptual Framework
357(2)
Research and Business Goals (Why?)
359(11)
What Are You Trying to Achieve? What Is Your Research or Business Goal?
359(1)
What Good Practices and Good Practice Models Exist?
360(1)
How Will You Measure the Results of Your Analysis?
360(1)
What Level of Risk Are You Willing to Assume?
361(1)
What Level of Investment Are You Willing to Make?
361(1)
Is This a Project or Enterprise-Level Goal?
362(1)
Understanding Why in Context-Use Case Scenarios
362(8)
How We Use the Tools-Analysis as a Process
370(11)
Analytical Method Is Best Suited to the Goals?
371(4)
Quantitative Analysis and Data Analytics
371(1)
Qualitative Analysis and Language Based Analytics
372(2)
Mixed Methods Analysis and Variant Sources
374(1)
When Is a Quantitative Analysis Approach Warranted?
375(1)
When Is a Qualitative Analysis Approach Warranted?
375(1)
When Should We Choose a Mixed Methods Approach?
375(1)
Which of These Analytical Methods Are Supported by Tools?
376(1)
Which of These Analytical Methods Are Not Supported by Tools?
376(1)
What Opportunities Are There for Mixing Analytical Methods?
377(1)
How in Context-Use Cases
377(4)
What Sources Are We Analyzing?
381(15)
What Is the Nature of Language, Information, and Knowledge We're Using as Source Evidence?
382(2)
What Meaning or Understanding Are We Deriving from the Language?
384(1)
What Linguistic Registers Are Represented in the Source Evidence?
384(1)
What Knowledge Structures Are Represented in the Source Evidence?
385(1)
What Types of Semantic Methods Are Available for Us to Work With?
386(1)
What Does the Analytical Workflow Look Like for Each of These Methods?
386(1)
Do These Methods Offer the Possibility of Reducing or Eliminating the Subjective Interpretation Element?
386(1)
What Is the Return on Investment for the Analysis?
387(1)
What Level of Effort Is Involved in Doing a Rigorous Analysis?
387(1)
What Types of Competencies Are Needed to Support the Analysis?
387(1)
What Is the Feasibility of the Analysis Without Semantic Methods?
387(1)
Understanding Sources in Context-Use Case Examples
388(8)
Fitting Tools to Methods and Sources
396(2)
Reviewing Your Tool Choices
397(1)
Reviewing Your Successes
397(1)
Exposing Your Failures
397(1)
Lessons Learned and Future Work
398(2)
Conclusions
400(1)
References
401(6)
13 Data Analytics for Cyber Threat Intelligence 407(26)
Hongmei Chi
Angela R. Martin
Carol Y. Scarlett
Introduction
408(1)
Cyber Threat Intelligence
409(11)
Related Work in Cyber Threat Intelligence
410(1)
Computational Methods in Cyber Treat Intelligence
411(2)
Challenges in Cyber Threat Intelligence
413(2)
Prevalence of Social Media Analysis in Cyber Threat Intelligence
415(1)
Benefits of Social Media Analysis in Cyber Threat Intelligence
416(1)
Establishing Motivations in Cyber Threat Intelligence through Social Media Analysis
417(1)
Role of Behavioral and Predicative Analysis in Cyber Threat Intelligence
418(2)
Text Analysis Tools
420(7)
Linguistic Inquiry Word Count
421(2)
Sentiment Analysis
423(1)
SentiStrength
424(1)
Case Study Using Linguistic Inquiry Word Count
425(2)
Conclusions
427(1)
References
427(6)
Index 433
Suliman Hawamdeh is a professor and department chair of the Department of Information Science in the College of Information at the University of North Texas. He is the director of the Information Science PhD program, one of the largest interdisciplinary information science PhD programs in the country. He is the editor in chief of the Journal of Information and Knowledge Management (JIKM) and the editor of a book series on innovation and knowledge management published by World Scientific. Dr. Hawamdeh founded and directed several academic programs including the first Master of Science in Knowledge Management in Asia at the School of Communication and Information at Nanyang Technological University in Singapore. Dr. Hawamdeh has extensive industrial experience. He was the Managing Director of ITC Information Technology Consultant Ltd, a company that developed and marketed a line of software development products. He worked as a consultant to several organizations including NEC, Institute of Southeast Asian Studies, Petronas, and Shell. Dr. Hawamdeh has authored and edited several books on knowledge management including Information and Knowledge Society published by McGraw Hill and Knowledge Management: Cultivating the Knowledge Professionals Published by Chandos Publishing, as well as several edited and co-edited books published by World Scientific.

Hsia-Ching Chang is an assistant professor in the Department of Information Science, College of Information at the University of North Texas. She received her PhD in informatics and MS in information science from the University at Albany, State University of New York as well as her MA in public policy from the National Taipei University in Taiwan. Her research interests concentrate on data analytics, social media, cybersecurity, knowledge mapping, scientometrics, information architecture, and information interaction.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97813518070052e.html
Keywords: