Atjaunināt sīkdatņu piekrišanu

E-grāmata: Data Industry: The Business and Economics of Information and Big Data

3.00/5 (7 ratings by Goodreads)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 03-May-2016
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119138426
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 83,22 €*
  • * 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.
  • Bibliotēkām
Data Industry: The Business and Economics of Information and Big DataPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 03-May-2016
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119138426
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.

Provides an introduction of the data industry to the field of economics

This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees.





Defines and develops the concept of a Data Industry, and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology

The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics.

CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudans Institute for Data Industry and proposed the concept of the data industry. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.
Preface xix
1 What is Data Industry? 1(18)
1.1 Data,
2(4)
1.1.1 Data Resources,
3(1)
1.1.2 The Data Asset,
4(2)
1.2 Industry,
6(4)
1.2.1 Industry Classification,
7(1)
1.2.2 The Modern Industrial System,
7(3)
1.3 Data Industry,
10(9)
1.3.1 Definitions,
10(1)
1.3.2 An Industry Structure Study,
10(3)
1.3.3 Industrial Behavior,
13(3)
1.3.4 Market Performance,
16(3)
2 Data Resources 19(22)
2.1 Scientific Data,
19(3)
2.1.1 Data-Intensive Discovery in the Natural Sciences,
20(1)
2.1.2 The Social Sciences Revolution,
21(1)
2.1.3 The Underused Scientific Record,
22(1)
2.2 Administrative Data,
22(4)
2.2.1 Open Governmental Affairs Data,
24(1)
2.2.2 Public Release of Administrative Data,
25(1)
2.2.3 A "Numerical" Misunderstanding in Governmental Affairs,
26(1)
2.3 Internet Data,
26(7)
2.3.1 Cyberspace: Data of the Sole Existence,
27(1)
2.3.2 Crawled Fortune,
28(1)
2.3.3 Forum Opinion Mining,
28(1)
2.3.4 Chat with Hidden Identities,
29(1)
2.3.5 Email: The First Type of Electronic Evidence,
30(1)
2.3.6 Evolution of the Blog,
31(1)
2.3.7 Six Degrees Social Network,
32(1)
2.4 Financial Data,
33(1)
2.4.1 Twins on News and Financial Data,
33(1)
2.4.2 The Annoyed Data Center,
33(1)
2.5 Health Data,
34(2)
2.5.1 Clinical Data: EMRs, EHRs, and PHRs,
34(1)
2.5.2 Medicare Claims Data Fraud and Abuse Detection,
35(1)
2.6 Transportation Data,
36(2)
2.6.1 Trajectory Data,
37(1)
2.6.2 Fixed-Position Data,
37(1)
2.6.3 Location-Based Data,
38(1)
2.7 Transaction Data,
38(3)
2.7.1 Receipts Data,
39(1)
2.7.2 e-Commerce Data,
39(2)
3 Data Industry Chain 41(18)
3.1 Industrial Chain Definition,
41(2)
3.1.1 The Meaning and Characteristics,
41(2)
3.1.2 Attribute-Based Categories,
43(1)
3.2 Industrial Chain Structure,
43(3)
3.2.1 Economic Entities,
44(1)
3.2.2 Environmental Elements,
44(2)
3.3 Industrial Chain Formation,
46(5)
3.3.1 Value Analysis,
46(4)
3.3.2 Dimensional Matching,
50(1)
3.4 Evolution of Industrial Chain,
51(2)
3.5 Industrial Chain Governance,
53(3)
3.5.1 Governance Patterns,
53(1)
3.5.2 Instruments of Governance,
54(2)
3.6 The Data Industry Chain and its Innovation Network,
56(3)
3.6.1 Innovation Layers,
56(1)
3.6.2 A Support System,
57(2)
4 Existing Data Innovations 59(14)
4.1 Web Creations,
59(4)
4.1.1 Network Writing,
60(1)
4.1.2 Creative Designs,
61(1)
4.1.3 Bespoke Style,
62(1)
4.1.4 Crowdsourcing,
63(1)
4.2 Data Marketing,
63(4)
4.2.1 Market Positioning,
64(1)
4.2.2 Business Insights,
64(2)
4.2.3 Customer Evaluation,
66(1)
4.3 Push Services,
67(2)
4.3.1 Targeted Advertising,
67(1)
4.3.2 Instant Broadcasting,
68(1)
4.4 Price Comparison,
69(1)
4.5 Disease Prevention,
70(3)
4.5.1 Tracking Epidemics,
71(1)
4.5.2 Whole-Genome Sequencing,
72(1)
5 Data Services in Multiple Domains 73(26)
5.1 Scientific Data Services,
73(3)
5.1.1 Literature Retrieval Reform,
74(1)
5.1.2 An Alternative Scholarly Communication Initiative,
74(1)
5.1.3 Scientific Research Project Services,
75(1)
5.2 Administrative Data Services,
76(3)
5.2.1 Police Department,
77(1)
5.2.2 Statistical Office,
78(1)
5.2.3 Environmental Protection Agency,
78(1)
5.3 Internet Data Services,
79(3)
5.3.1 Open Source,
79(1)
5.3.2 Privacy Services,
80(2)
5.3.3 People Search,
82(1)
5.4 Financial Data Services,
82(4)
5.4.1 Describing Correlations,
83(1)
5.4.2 Simulating Market-Makers' Behaviors,
84(1)
5.4.3 Forecasting Security Prices,
85(1)
5.5 Health Data Services,
86(5)
5.5.1 Approaching the Healthcare Singularity,
87(1)
5.5.2 New Drug of Launching Shortcuts,
87(1)
5.5.3 Monitoring in Chronic Disease,
88(2)
5.5.4 Data Supporting Data: Brain Sciences and Traditional Chinese Medicine,
90(1)
5.6 Transportation Data Services,
91(3)
5.6.1 Household Travel Characteristics,
91(1)
5.6.2 Multivariate Analysis of Traffic Congestion,
92(1)
5.6.3 Short-Term Travel Time Estimation,
93(1)
5.7 Transaction Data Services,
94(5)
5.7.1 Pricing Reform,
94(1)
5.7.2 Sales Transformation,
95(1)
5.7.3 Payment Upgrading,
96(3)
6 Data Services in Distinct Sectors 99(24)
6.1 Natural Resource Sectors,
99(5)
6.1.1 Agriculture: Rely on What?,
100(1)
6.1.2 Forestry Sector: Grain for Green at All Costs?,
101(1)
6.1.3 Livestock and Poultry Sector: Making Early Warning to Be More Effective,
101(1)
6.1.4 Marine Sector: How to Support the Ocean Economy?,
102(1)
6.1.5 Extraction Sector: A New Exploration Strategy,
103(1)
6.2 Manufacturing Sector,
104(2)
6.2.1 Production Capacity Optimization,
104(1)
6.2.2 Transforming the Production Process,
105(1)
6.3 Logistics and Warehousing Sector,
106(1)
6.3.1 Optimizing Order Picking,
106(1)
6.3.2 Dynamic Equilibrium Logistic Channels,
107(1)
6.4 Shipping Sector,
107(2)
6.4.1 Extracting More Transportation Capacity,
108(1)
6.4.2 Determining the Optimal Transfer in Road, Rail, Air, and Water Transport,
108(1)
6.5 Real Estate Sector,
109(2)
6.5.1 Urban Planning: Along the Timeline,
109(1)
6.5.2 Commercial Layout: To Be Unique,
110(1)
6.5.3 Property Management: Become Intelligent,
110(1)
6.6 Tourism Sector,
111(2)
6.6.1 Travel Arrangements,
111(1)
6.6.2 Pushing Attractions,
112(1)
6.6.3 Gourmet Food Recommendations,
112(1)
6.6.4 Accommodation Bidding,
113(1)
6.7 Education and Training Sector,
113(2)
6.7.1 New Knowledge Appraisal Mechanism,
114(1)
6.7.2 Innovative Continuing Education,
114(1)
6.8 Service Sector,
115(4)
6.8.1 Prolong Life: More Scientific,
115(1)
6.8.2 Elderly Care: Technology-Enhanced, Enough?,
116(1)
6.8.3 Legal Services: Occupational Changes,
117(1)
6.8.4 Patents: The Maximum Open Data Resource,
117(1)
6.8.5 Meteorological Data Services: How to Commercialize?,
118(1)
6.9 Media, Sports, and the Entertainment Sector,
119(2)
6.9.1 Data Talent Scout,
119(1)
6.9.2 Interactive Script,
120(1)
6.10 Public Sector,
121(2)
6.10.1 Wargaming,
121(1)
6.10.2 Public Opinion Analysis,
122(1)
7 Business Models in the Data Industry 123(12)
7.1 General Analysis of the Business Model,
123(3)
7.1.1 A Set of Elements and Their Relationships,
124(1)
7.1.2 Forming a Specific Business Logic,
125(1)
7.1.3 Creating and Commercializing Value,
125(1)
7.2 Data Industry Business Models,
126(3)
7.2.1 A Resource-Based View: Resource Possession,
126(1)
7.2.2 A Dynamic-Capability View: Endogenous Capacity,
127(1)
7.2.3 A Capital-Based View: Venture-Capital Operation,
128(1)
7.3 Innovation of Data Industry Business Models,
129(6)
7.3.1 Sources,
129(2)
7.3.2 Methods,
131(1)
7.3.3 A Paradox,
132(3)
8 Operating Models in the Data Industry 135(12)
8.1 General Analysis of the Operating Model,
136(2)
8.1.1 Strategic Management,
136(1)
8.1.2 Competitiveness,
137(1)
8.1.3 Convergence,
137(1)
8.2 Data Industry Operating Models,
138(6)
8.2.1 Gradual Development: Google,
138(1)
8.2.2 Micro-Innovation: Baidu,
139(1)
8.2.3 Outsourcing: EMC,
140(1)
8.2.4 Data-Driven Restructuring: IBM,
140(1)
8.2.5 Mergers and Acquisitions: Yahoo!,
141(1)
8.2.6 Reengineering: Facebook,
142(1)
8.2.7 The Second Venture: Alibaba,
143(1)
8.3 Innovation of Data Industry Operating Models,
144(3)
8.3.1 Philosophy of Business,
144(1)
8.3.2 Management Styles,
145(1)
8.3.3 Force Field Analysis,
145(2)
9 Enterprise Agglomeration of the Data Industry 147(12)
9.1 Directive Agglomeration,
148(1)
9.1.1 Data Resource Endowment,
148(1)
9.1.2 Multiple Target Sites,
149(1)
9.2 Driven Agglomeration,
149(3)
9.2.1 Labor Force,
150(1)
9.2.2 Capital,
150(1)
9.2.3 Technology,
151(1)
9.3 Industrial Symbiosis,
152(2)
9.3.1 Entity Symbiosis,
152(1)
9.3.2 Virtual Derivative,
153(1)
9.4 Wheel-Axle Type Agglomeration,
154(1)
9.4.1 Vertical Leadership Development,
154(1)
9.4.2 The Radiation Effect of Growth Poles,
154(1)
9.5 Refocusing Agglomeration,
155(4)
9.5.1 "Smart Heart" of the Central Business District,
155(1)
9.5.2 The Core Objective "Besiege",
156(3)
10 Cluster Effects of the Data Industry 159(12)
10.1 External Economies,
159(2)
10.1.1 External Economies of Scale,
160(1)
10.1.2 External Economies of Scope,
160(1)
10.2 Internal Economies,
161(3)
10.2.1 Coopetition,
161(2)
10.2.2 Synergy,
163(1)
10.3 Transaction Cost,
164(3)
10.3.1 The Division of Cost,
164(1)
10.3.2 Opportunity Cost,
165(1)
10.3.3 Monitoring Cost,
166(1)
10.4 Competitive Advantages,
167(2)
10.4.1 Innovation Performance,
167(1)
10.4.2 The Impact of Expansion,
168(1)
10.5 Negative Effects,
169(2)
10.5.1 Innovation Risk,
169(1)
10.5.2 Data Asset Specificity,
169(1)
10.5.3 Crowding Effect,
170(1)
11 A Mode of Industrial Development for the Data Industry 171(12)
11.1 General Analysis of the Development Mode,
171(2)
11.1.1 Influence Factors,
172(1)
11.1.2 Dominant Styles,
172(1)
11.2 A Basic Development Mode for the Data Industry,
173(3)
11.2.1 Industrial Structure: A Comprehensive Advancement Plan,
173(1)
11.2.2 Industrial Organization: Dominated by the SMEs,
174(1)
11.2.3 Industrial Distribution: Endogenous Growth,
174(1)
11.2.4 Industrial Strategy: Self-Dependent Innovation,
175(1)
11.2.5 Industrial Policy: Market Driven,
176(1)
11.3 An Optimized Development Mode for the Data Industry,
176(7)
11.3.1 New Industrial Structure: Built on Upgrading of Traditional Industries,
176(2)
11.3.2 New Industrial Organization: Small Is Beautiful,
178(1)
11.3.3 New Industrial Distribution: Constructing a Novel Type of Industrial Bases,
178(1)
11.3.4 New Industrial Strategy: Industry/University Cooperation,
179(1)
11.3.5 New Industrial Policy: Civil-Military Coordination,
180(3)
12 A Guide to the Emerging Data Law 183(6)
12.1 Data Resource Law,
183(2)
12.2 Data Antitrust Law,
185(1)
12.3 Data Fraud Prevention Law,
186(1)
12.4 Data Privacy Law,
187(1)
12.5 Data Asset Law,
188(1)
References 189(4)
Index 193
CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudans Institute for Data Industry and proposed the concept of the data industry. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811191384266e.html
Keywords: