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市場調查報告書

巨量資料、雲端運算解決方案:服務、軟體、平台、基礎設施的市場展望 2017-2022年

Big Data and Cloud Computing Solutions: Market Outlook for Services, Software, Platforms, and Infrastructure 2017 - 2022

出版商 Mind Commerce 商品編碼 541490
出版日期 內容資訊 英文 412 Pages
商品交期: 最快1-2個工作天內
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巨量資料、雲端運算解決方案:服務、軟體、平台、基礎設施的市場展望 2017-2022年 Big Data and Cloud Computing Solutions: Market Outlook for Services, Software, Platforms, and Infrastructure 2017 - 2022
出版日期: 2017年08月15日 內容資訊: 英文 412 Pages
簡介

本報告提供全球巨量資料市場相關資料,商務案例的研究,應用的利用例,業者情勢,價值鏈分析,案例研究及包含產業的定量評估與預測的調查,還有關於全球雲端運算市場的集中型、分散式服務,平台及基礎設施評價,再加上主要垂直產業整體雲端運算的「即服務」及IoT網路、相關應用 & 服務的雲端支援的電信業者雲端服務、市場機會等彙整資料。

巨量資料市場:商務案例,市場分析及預測

第1章 背景

第2章 摘要整理

第3章 巨量資料技術、商務案例

  • 巨量資料定義
  • 巨量資料的特徵
  • 巨量資料技術
  • 新的範例、技術
  • 巨量資料發展藍圖
  • 市場成長的促進要素
  • 市場障礙

第4章 巨量資料的主要市場

  • 產業用網際網路、M2M
  • 零售、飯店
  • 媒體
  • 公共事業
  • 金融服務
  • 醫療、醫藥品
  • 通訊
  • 政府、國土防衛
  • 其他的市場

第5章 巨量資料的價值鏈

  • 巨量資料價值的細分化
  • 資料收集 & 供應
  • 資料存放系統 & 商業智慧
  • 分析 & 視覺化
  • Actioning及商務流程管理
  • 資料管治

第6章 巨量資料分析

  • 巨量資料分析是什麼?
  • 巨量資料分析的重要性
  • 反應性 vs. 主動性分析
  • 技術及實行方法

第7章 標準化、管理方案

  • Cloud Standards Customer Council
  • National Institute of Standards and Technology
  • OASIS
  • pen Data Foundation
  • Open Data Center Alliance
  • Cloud Security Alliance
  • International Telecommunications Union
  • International Organization for Standardization

第8章 巨量資料的全球市場、預測

  • 全球巨量資料市場
  • 巨量資料的地區市場
  • 巨量資料收益:各產品區隔

第9章 巨量資料市場上主要企業

第10章 附錄:巨量資料的IoT資料串流的支援

圖表

雲端運算服務,平台,基礎設施及一切皆服務

第1章 摘要整理

第2章 簡介

第3章 雲端運算技術、市場

  • 商務的價值命題
  • 雲端運算的生態系統
  • 雲端運算的通訊
  • 雲端運算市場區分
  • 雲端運算市場促進成長要素

第4章 全球雲端運算市場展望

  • 全球雲端運算收益
  • 收益:各雲端運算發展類型
  • 全球雲端收益:各軟體、平台、基礎設施
  • 全球雲端服務收益
  • 地區的雲端運算市場展望
  • 全球雲端運算收益:各垂直產業

第5章 IoT的雲端服務

  • IoT概要
  • IoT雲端運算的主要供應商
  • IoT的雲端運算市場展望

第6章 電信業者雲端服務

  • 概要
  • 電信業者雲端
  • 行動邊緣運算 (MEC)
  • 電信業者雲端市場展望

第7章 重要的雲端運算產業的發展

  • 最新的雲端運算M&A
  • 最新的雲端運算投資

第8章 附錄:雲端運算的基本

第9章 附錄:MEC技術、解決方案

圖表

目錄

Overview:

The management of unstructured data (e.g. Big Data), the leveraging of analytics tools to derive value, and the integration between Cloud, Internet of Things (IoT), and enterprise operational technology are key focus areas for large companies across virtually every industry vertical. A new data economy is developing in which the data associated with corporate products and services becomes almost as value as the company offerings themselves. New models are emerging to reduce friction across the value chain including enhanced Big Data as a Service (BDaaS) offerings. BDaaS is anticipated to make cross-industry, cross-company, and even cross-competitor data exchange a reality that adds value across the ecosystem with minimized security and privacy concerns.

Cloud Computing technology and the “as a service” business model is transforming Services, Platforms, and Infrastructure for the entire ICT ecosystem. With the Everything as a Service (XaaS) model, leading apps such as Business Process, Communications, and Commerce/Payments may all be offered in a manner in which risk and CapEx are minimized while OpEx is logically scaled to business outcomes. Distributed Cloud Computing is becoming increasingly important in both fixed and wireless networks. Mobile Edge Computing (MEC) in particular is anticipated to become a critically important aspect of Communication Service Provider operations.

This research provides an in-depth assessment of the global Big Data market, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry with forecasting from 2017 to 2022. This research also evaluates the global cloud computing marketplace including centralized and distributed services, platforms, and infrastructure. The research also analyzes the market for cloud computing “as a service” across major industry verticals as well as carrier cloud services and market opportunities for cloud support of IoT networks and associated apps and services. It includes detailed forecasts for the aforementioned from 2017 - 2022

Target Audience:

  • IoT companies
  • Cloud SPI companies
  • Network service providers
  • API management companies
  • SDN and virtualization vendors
  • Systems integration companies
  • Big Data and Analytics companies
  • IT, data center, and CDN companies
  • Fixed and wireless infrastructure providers

Table of Contents

The Big Data Market: Business Case, Market Analysis and Forecasts 2017-2022

1. Background

  • 1.1. Introduction
  • 1.2. Scope of the Report
  • 1.3. Target Audience
  • 1.4. Companies in Report

2. Executive Summary

3. Big Data Technology and Business Case

  • 3.1. Defining Big Data
  • 3.2. Key Characteristics of Big Data
    • 3.2.1. Volume
    • 3.2.2. Variety
    • 3.2.3. Velocity
    • 3.2.4. Variability
    • 3.2.5. Complexity
  • 3.3. Big Data Technology
    • 3.3.1. Hadoop
      • 3.3.1.1. Other Apache Projects
    • 3.3.2. NoSQL
      • 3.3.2.1. Hbase
      • 3.3.2.2. Cassandra
      • 3.3.2.3. Mongo DB
      • 3.3.2.4. Riak
      • 3.3.2.5. CouchDB
    • 3.3.3. MPP Databases
    • 3.3.4. Others and Emerging Technologies
      • 3.3.4.1. Storm
      • 3.3.4.2. Drill
      • 3.3.4.3. Dremel
      • 3.3.4.4. SAP HANA
      • 3.3.4.5. Gremlin & Giraph
  • 3.4. New Paradigms and Techniques
    • 3.4.1. Streaming Analytics
    • 3.4.2. Cloud Technology
    • 3.4.3. Google Search
    • 3.4.4. Customize Analytical Tools
    • 3.4.5. Internet Keywords
    • 3.4.6. Gamification
  • 3.5. Big Data Roadmap
  • 3.6. Market Drivers
    • 3.6.1. Data Volume & Variety
    • 3.6.2. Increasing Adoption of Big Data by Enterprises and Telecom
    • 3.6.3. Maturation of Big Data Software
    • 3.6.4. Continued Investments in Big Data by Web Giants
    • 3.6.5. Business Drivers
  • 3.7. Market Barriers
    • 3.7.1. Privacy and Security: The ‘Big' Barrier
    • 3.7.2. Workforce Re-skilling and Organizational Resistance
    • 3.7.3. Lack of Clear Big Data Strategies
    • 3.7.4. Technical Challenges: Scalability & Maintenance
    • 3.7.5. Big Data Development Expertise

4. Key Sectors for Big Data

  • 4.1. Industrial Internet and Machine-to-Machine
    • 4.1.1. Big Data in M2M
    • 4.1.2. Vertical Opportunities
  • 4.2. Retail and Hospitality
    • 4.2.1. Improving Accuracy of Forecasts & Stock Management
    • 4.2.2. Determining Buying Patterns
    • 4.2.3. Hospitality Use Cases
    • 4.2.4. Personalized Marketing
  • 4.3. Media
    • 4.3.1. Social Media
    • 4.3.2. Social Gaming Analytics
    • 4.3.3. Usage of Social Media Analytics by Other Verticals
    • 4.3.4. Internet Keyword Search
  • 4.4. Utilities
    • 4.4.1. Analysis of Operational Data
    • 4.4.2. Application Areas for the Future
  • 4.5. Financial Services
    • 4.5.1. Fraud Analysis, Mitigation & Risk Profiling
    • 4.5.2. Merchant-Funded Reward Programs
    • 4.5.3. Customer Segmentation
    • 4.5.4. Customer Retention & Personalized Product Offering
    • 4.5.5. Insurance Companies
  • 4.6. Healthcare and Pharmaceutical
    • 4.6.1. Drug Development
    • 4.6.2. Medical Data Analytics
    • 4.6.3. Case Study: Identifying Heartbeat Patterns
  • 4.7. Telecommunications
    • 4.7.1. Telco Analytics: Customer/Usage Profiling and Service Optimization
    • 4.7.2. Big Data Analytic Tools
    • 4.7.3. Speech Analytics
    • 4.7.4. New Products and Services
  • 4.8. Government and Homeland Security
    • 4.8.1. Big Data Research
    • 4.8.2. Statistical Analysis
    • 4.8.3. Language Translation
    • 4.8.4. Developing New Applications for the Public
    • 4.8.5. Tracking Crime
    • 4.8.6. Intelligence Gathering
    • 4.8.7. Fraud Detection & Revenue Generation
  • 4.9. Other Sectors
    • 4.9.1. Aviation
    • 4.9.2. Transportation & Logistics: Optimizing Fleet Usage
    • 4.9.3. Sports: Real-Time Processing of Statistics
    • 4.9.4. Education
    • 4.9.5. Manufacturing

5. The Big Data Value Chain

  • 5.1. Fragmentation in the Big Data Value
  • 5.2. Data Acquisitioning & Provisioning
  • 5.3. Data Warehousing & Business Intelligence
  • 5.4. Analytics & Visualization
  • 5.5. Actioning and Business Process Management
  • 5.6. Data Governance

6. Big Data Analytics

  • 6.1. What is Big Data Analytics?
  • 6.2. The Importance of Big Data Analytics
  • 6.3. Reactive vs. Proactive Analytics
  • 6.4. Technology and Implementation Approaches
    • 6.4.1. Grid Computing
    • 6.4.2. In-Database processing
    • 6.4.3. In-Memory Analytics
    • 6.4.4. Data Mining
    • 6.4.5. Predictive Analytics
    • 6.4.6. Natural Language Processing
    • 6.4.7. Text Analytics
    • 6.4.8. Visual Analytics
    • 6.4.9. Association Rule Learning
    • 6.4.10. Classification Tree Analysis
    • 6.4.11. Machine Learning
    • 6.4.12. Neural Networks
    • 6.4.13. Multilayer Perceptron (MLP)
    • 6.4.14. Radial Basis Functions
      • 6.4.14.1. Support Vector Machines
      • 6.4.14.2. Naïve Bayes
      • 6.4.14.3. K-nearest Neighbors
    • 6.4.15. Geospatial Predictive Modelling
    • 6.4.16. Regression Analysis
    • 6.4.17. Social Network Analysis

7. Standardization and Regulatory Initiatives

  • 7.1. Cloud Standards Customer Council
  • 7.2. National Institute of Standards and Technology
  • 7.3. OASIS
  • 7.4. Open Data Foundation
  • 7.5. Open Data Center Alliance
  • 7.6. Cloud Security Alliance
  • 7.7. International Telecommunications Union
  • 7.8. International Organization for Standardization

8. Global Markets and Forecasts for Big Data

  • 8.1. Global Big Data Markets 2017-2022
  • 8.2. Regional Markets for Big Data 2017-2022
  • 8.3. Big Data Revenue by Product Segment 2017-2022
    • 8.3.1. Database Management Systems
    • 8.3.2. Big Data Integration Tools
    • 8.3.3. Application Infrastructure and Middleware
    • 8.3.4. Business Intelligence Tools and Analytics Platforms
    • 8.3.5. Big Data in Professional Services

9. Key Players in the Big Data Market

  • 9.1. Vendor Assessment Matrix
  • 9.2. 1010Data
  • 9.3. Accenture
  • 9.4. Actian Corporation
  • 9.5. Amazon
  • 9.6. Apache Software Foundation
  • 9.7. APTEAN (Formerly CDC Software)
  • 9.8. Booz Allen Hamilton
  • 9.9. Bosch Software Innovations: Bosch IoT Suite
  • 9.10. Capgemini
  • 9.11. Cisco Systems
  • 9.12. Cloudera
  • 9.13. CRAY Inc.
  • 9.14. Computer Science Corporation (CSC)
  • 9.15. DataDirect Network
  • 9.16. Dell
  • 9.17. Deloitte
  • 9.18. EMC
  • 9.19. Facebook
  • 9.20. Fujitsu
  • 9.21. General Electric (GE)
  • 9.22. GoodData Corporation
  • 9.23. Google
  • 9.24. Guavus
  • 9.25. HP
  • 9.26. Hitachi Data Systems
  • 9.27. Hortonworks
  • 9.28. IBM
  • 9.29. Informatica
  • 9.30. Intel
  • 9.31. Jasper (Cisco Jasper)
  • 9.32. Juniper Networks
  • 9.33. Marklogic
  • 9.34. Microsoft
  • 9.35. MongoDB (Formerly 10Gen)
  • 9.36. MU Sigma
  • 9.37. Netapp
  • 9.38. NTT Data
  • 9.39. Open Text (Actuate Corporation)
  • 9.40. Opera Solutions
  • 9.41. Oracle
  • 9.42. Pentaho
  • 9.43. Qlik Tech
  • 9.44. Quantum
  • 9.45. Rackspace
  • 9.46. Revolution Analytics
  • 9.47. Salesforce
  • 9.48. SAP
  • 9.49. SAS Institute
  • 9.50. Sisense
  • 9.51. Software AG/Terracotta
  • 9.52. Splunk
  • 9.53. Sqrrl
  • 9.54. Supermicro
  • 9.55. Tableau Software
  • 9.56. Tata Consultancy Services
  • 9.57. Teradata
  • 9.58. Think Big Analytics
  • 9.59. TIBCO
  • 9.60. Tidemark Systems
  • 9.61. VMware (Part of EMC)
  • 9.62. Wipro
  • 9.63. Workday (Platfora)
  • 9.64. Zettics

10. Appendix: Big Data Support of Streaming IoT Data

  • 10.1. Big Data Technology Market Outlook for Streaming IoT Data
    • 10.1.1. IoT Data Management is a Ubiquitous Opportunity across Enterprise
    • 10.1.2. IoT Data becomes a Big Data Revenue Opportunity
    • 10.1.3. Real-time Streaming IoT Data Analytics becoming a Substantial Business Opportunity
  • 10.2. Global Streaming IoT Data Analytics Revenue
    • 10.2.1. Overall Streaming Data Analytics Revenue for IoT
    • 10.2.2. Global Streaming IoT Data Analytics Revenue by App, Software, and Services
    • 10.2.3. Global Streaming IoT Data Analytics Revenue in Industry Verticals
      • 10.2.3.1. Streaming IoT Data Analytics Revenue in Retail
        • 10.2.3.1.1. Streaming IoT Data Analytics Revenue by Retail Segment
        • 10.2.3.1.2. Streaming IoT Data Analytics Retail Revenue by App, Software, and Service
      • 10.2.3.2. Streaming IoT Data Analytics Revenue in Telecom and IT
        • 10.2.3.2.1. Streaming IoT Data Analytics Revenue by Telecom and IT Segment
        • 10.2.3.2.2. Streaming IoT Data Analytics Revenue by Telecom & IT App, Software, and Service
      • 10.2.3.3. Streaming IoT Data Analytics Revenue in Energy and Utility
        • 10.2.3.3.1. Streaming IoT Data Analytics Revenue by Energy and Utility Segment
        • 10.2.3.3.2. Streaming IoT Data Analytics Energy and Utilities Revenue by App, Software, and Service
      • 10.2.3.4. Streaming IoT Data Analytics Revenue in Government
        • 10.2.3.4.1. Streaming IoT Data Analytics Revenue by Government Segment
        • 10.2.3.4.2. Streaming IoT Data Analytics Government Revenue by App, Software, and Service
      • 10.2.3.5. Streaming IoT Data Analytics Revenue in Healthcare and Life Science
        • 10.2.3.5.1. Streaming IoT Data Analytics Revenue by Healthcare Segment
      • 10.2.3.6. Streaming IoT Data Analytics Revenue in Manufacturing
        • 10.2.3.6.1. Streaming IoT Data Analytics Revenue by Manufacturing Segment
        • 10.2.3.6.2. Streaming IoT Data Analytics Manufacturing Revenue by App, Software, and Service
      • 10.2.3.7. Streaming IoT Data Analytics Revenue in Transportation & Logistics
        • 10.2.3.7.1. Streaming IoT Data Analytics Revenue by Transportation & Logistics Segment
        • 10.2.3.7.2. Streaming IoT Data Analytics Transportation & Logistics Revenue by App, Software, and Service
      • 10.2.3.8. Streaming IoT Data Analytics Revenue in Banking and Finance
        • 10.2.3.8.1. Streaming IoT Data Analytics Revenue by Banking and Finance Segment
        • 10.2.3.8.2. Streaming IoT Data Analytics Revenue by Banking & Finance App, Software, and Service
      • 10.2.3.9. Streaming IoT Data Analytics Revenue in Smart Cities
        • 10.2.3.9.1. Streaming IoT Data Analytics Revenue by Smart City Segment
      • 10.2.3.10. Streaming IoT Data Analytics Revenue in Automotive
        • 10.2.3.10.1. Streaming IoT Data Analytics Revenue by Automobile Industry Segment
        • 10.2.3.10.2. Streaming IoT Data Analytics Revenue by Automotive Industry App, Software, and Service
      • 10.2.3.11. Streaming IoT Data Analytics Revenue in Education
        • 10.2.3.11.1. Streaming IoT Data Analytics Revenue by Education Industry Segment
        • 10.2.3.11.2. Streaming IoT Data Analytics Revenue by Education Industry App, Software, and Service
      • 10.2.3.12. Streaming IoT Data Analytics Revenue in Outsourcing Services
        • 10.2.3.12.1. Streaming IoT Data Analytics Revenue by Outsourcing Segment
        • 10.2.3.12.2. Streaming IoT Data Analytics Revenue by Outsourcing Industry App, Software, and Service
      • 10.2.3.13. Streaming IoT Data Analytics Revenue by Leading Vendor Platform
  • 10.3. Regional Streaming IoT Data Analytics Revenue
    • 10.3.1. Revenue in Region
    • 10.3.2. APAC Market Revenue
    • 10.3.3. Europe Market Revenue
    • 10.3.4. North America Market Revenue
    • 10.3.5. Latin America Market Revenue
    • 10.3.6. ME&A Market Revenue
  • 10.4. Streaming IoT Data Analytics Revenue by Country 2016-2021
    • 10.4.1. Revenue by APAC Countries
      • 10.4.1.1. Leading Countries
      • 10.4.1.2. Japan Market Revenue
      • 10.4.1.3. China Market Revenue
      • 10.4.1.4. India Market Revenue
      • 10.4.1.5. Australia Market Revenue
    • 10.4.2. Revenue by Europe Countries
      • 10.4.2.1. Leading Countries
      • 10.4.2.2. Germany Market Revenue
      • 10.4.2.3. UK Market Revenue
      • 10.4.2.4. France Market Revenue
    • 10.4.3. Revenue by North America Countries
      • 10.4.3.1. Leading Countries
      • 10.4.3.2. US Market Revenue
      • 10.4.3.3. Canada Market Revenue
    • 10.4.4. Revenue by Latin America Countries
      • 10.4.4.1. Leading Countries
      • 10.4.4.2. Brazil Market Revenue
      • 10.4.4.3. Mexico Market Revenue
    • 10.4.5. Revenue by ME&A Countries
      • 10.4.5.1. Leading Countries
      • 10.4.5.2. South Africa Market Revenue
      • 10.4.5.3. UAE Market Revenue

Figures

  • Figure 1: Key Characteristics of Big Data
  • Figure 2: NoSQL vs Legacy DB Performance Comparisons
  • Figure 3: Roadmap Big Data Technologies 2016-2030
  • Figure 4: The Big Data Value Chain
  • Figure 5: Big Data Value Flow
  • Figure 6: Big Data Analytics
  • Figure 7: Global Big Data Markets 2017-2022
  • Figure 8: Regional Big Data Markets 2017-2022
  • Figure 9: Database Management Systems 2017-2022
  • Figure 10: Data Integration and Quality Tools 2017-2022
  • Figure 11: Application Infrastructure and Middleware 2017-2022
  • Figure 12: Business Intelligence Tools and Analytics Platforms 2017-2022
  • Figure 13: Big Data in Professional Services 2017-2022
  • Figure 14: Big Data Vendor Ranking Matrix
  • Figure 15: Streaming IoT Data Sources Compared
  • Figure 16: Overall Streaming IoT Data Analytics 2016-2021

Tables

  • Table 1: Global Big Data Markets 2017-2022
  • Table 2: Regional Big Data Markets 2017-2022
  • Table 3: Big Data Markets by Product Segments 2017-2022
  • Table 4: Database Management Systems 2017-2022
  • Table 5: Data Integration Tools 2017-2022
  • Table 6: Application Infrastructure and Middleware 2017-2022
  • Table 7: Business Intelligence Tools and Analytics Platforms 2017-2022
  • Table 8: Big Data in Professional Services 2017-2022
  • Table 9: Big Data Analytics Platforms by Company
  • Table 10: Global Streaming IoT Data Analytics Revenue by App, Software, and Service 2016-2021
  • Table 11: Global Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 12: Retail Streaming IoT Data Analytics Revenue by Retail Segment 2016-2021
  • Table 13: Retail Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 14: Telecom & IT Streaming IoT Data Analytics Rev by Segment 2016-2021
  • Table 15: Telecom & IT Streaming IoT Data Analytics Rev by App, Software, and Services 2016-2021
  • Table 16: Energy & Utilities Streaming IoT Data Analytics Rev by Segment 2016-2021
  • Table 17: Energy & Utilities Streaming IoT Data Analytics Rev by App, Software, and Services 2016-2021
  • Table 18: Government Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 19: Government Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 20: Healthcare & Life Science Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 21: Healthcare & Life Science Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 22: Manufacturing Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 23: Manufacturing Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 24: Transportation & Logistics Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 25: Transportation & Logistics Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 26: Banking and Finance Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 27: Banking & Finance Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 28: Smart Cities Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 29: Smart Cities Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 30: Automotive Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 31: Automotive Streaming IoT Data Analytics Revenue by Apps, Software, and Services 2016-2021
  • Table 32: Education Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 33: Education Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 34: Outsourcing Service Streaming IoT Data Analytics Revenue by Segment 2016-2021
  • Table 35: Outsourcing Service Streaming IoT Data Analytics Revenue by App, Software, and Services 2016-2021
  • Table 36: Streaming IoT Data Analytics Revenue by Leading Vendor Platforms 2016-2021
  • Table 37: Streaming IoT Data Analytics Revenue in Region 2016-2021
  • Table 38: APAC Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 39: APAC Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 40: APAC Streaming IoT Data Analytics Revenue by Leading Vendor Platforms 2016-2021
  • Table 41: Europe Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 42: Europe Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 43: Europe Streaming IoT Data Analytics Revenue by Leading Vendor Platforms 2016-2021
  • Table 44: North America Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 45: North America Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 46: North America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms 2016-2021
  • Table 47: Latin America Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 48: Latin America Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 49: Latin America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms 2016-2021
  • Table 50: ME&A Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 51: ME&A Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 52: ME&A Streaming IoT Data Analytics Revenue by Leading Vendor Platforms 2016-2021
  • Table 53: Streaming IoT Data Analytics Revenue by APAC Countries 2016-2021
  • Table 54: Japan Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 55: Japan Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 56: China Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 57: China Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 58: India Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 59: India Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 60: Australia Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 61: Australia Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 62: Streaming IoT Data Analytics Revenue by Europe Countries 2016-2021
  • Table 63: Germany Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 64: Germany Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 65: UK Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 66: UK Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 67: France Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 68: France Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 69: Streaming IoT Data Analytics Revenue by North America Countries 2016-2021
  • Table 70: US Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 71: US Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 72: Canada Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 73: Canada Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 74: Streaming IoT Data Analytics Revenue by Latin America Countries 2016-2021
  • Table 75: Brazil Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 76: Brazil Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 77: Mexico Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 78: Mexico Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 79: Streaming IoT Data Analytics Revenue by ME&A Countries 2016-2021
  • Table 80: South Africa Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 81: South Africa Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021
  • Table 82: UAE Streaming IoT Data Analytics Revenue by Solution and Services 2016-2021
  • Table 83: UAE Streaming IoT Data Analytics Revenue in Industry Vertical 2016-2021

Cloud Computing Services, Platforms, Infrastructure and Everything as a Service 2017-2022

1. Executive Summary

2. Introduction

  • 2.1. Overview
  • 2.2. Study Scope
  • 2.3. Target Audience
  • 2.4. Key Companies Report
  • 2.5. Global Cloud Computing Market Outlook

3. Cloud Computing Technology and Markets

  • 3.1. Business Value Proposition
  • 3.2. Cloud Computing Ecosystem
  • 3.3. Telecom in Cloud Computing
  • 3.4. Cloud Computing Market Segmentation
  • 3.5. Cloud Computing Market Growth Drivers

4. Global Cloud Computing Market Outlook

  • 4.1. Global Cloud Computing Revenue 2017-2022
  • 4.2. Revenue by Cloud Computing Deployment Type
  • 4.3. Global Cloud Revenue by Software, Platform, and Infrastructure
    • 4.3.1. Global Cloud Computing IaaS Revenue by Sub-segment 2017-2022
    • 4.3.2. Global Cloud Computing SaaS Revenue by Sub-segment 2017-2022
    • 4.3.3. Global Cloud Computing PaaS Revenue by Sub-segment 2017-2022
  • 4.4. Global Cloud Services Revenue 2017-2022
    • 4.4.1. Cloud Based Business Process as a Service
    • 4.4.2. Cloud Data Migration Services
    • 4.4.3. Cloud Based Advertising
    • 4.4.4. Cloud Based Payments
    • 4.4.5. Cloud Based Data as a Service
    • 4.4.6. Cloud Based Communication as a Service
  • 4.5. Regional Cloud Computing Market Outlook
    • 4.5.1. North American Cloud Computing Market
    • 4.5.2. Western European Cloud Computing Market
    • 4.5.3. China Cloud Computing Market
  • 4.6. Global Cloud Computing Revenue by Industry Vertical 2017-2022
    • 4.6.1. Global Cloud Computing Revenue in Government Sector 2017-2022
    • 4.6.2. Global Cloud Computing Revenue in Financial Sector 2017-2022
    • 4.6.3. Global Cloud Computing Revenue in Healthcare 2017-2022
    • 4.6.4. Global Cloud Computing Revenue in Retail Sector 2017-2022
    • 4.6.5. Global Cloud Computing Revenue in Manufacturing 2017-2022
    • 4.6.6. Global Cloud Computing Revenue in Automobile Sector 2017-2022
    • 4.6.7. Global Cloud Computing Revenue in Agriculture 2017-2022

5. Cloud Services in IoT

  • 5.1. IoT Overview
    • 5.1.1. IoT will Drive Massive Data Storage and Processing Needs
    • 5.1.2. Processing Cloud IoT Data
    • 5.1.3. Dealing with Centralized Storage and Decentralized Processing
    • 5.1.4. Data Security and Personal Information Privacy are the Biggest Hurdles
    • 5.1.5. Enhanced Tools needed for Machine Generated Data in IoT
    • 5.1.6. Cloud Data Management for IoT Devices
  • 5.2. Leading Vendors in IoT Cloud Computing
  • 5.3. Cloud Computing in IoT Market Outlook

6. Carrier Cloud Services

  • 6.1. Overview
  • 6.2. Carrier Clouds
  • 6.3. Mobile Edge Computing
    • 6.3.1. MEC Benefits to Carriers
    • 6.3.2. Commercialization of MEC
  • 6.4. Carrier Cloud Market Outlook
    • 6.4.1. Global Carrier Cloud Revenue 2017-2022
    • 6.4.2. Carrier Cloud Revenue by Region 2017-2022
    • 6.4.3. Global Carrier Cloud Revenue by Industry Vertical 2017-2022
    • 6.4.4. Global Carrier Cloud Revenue by Services and Solutions 2017-2022
    • 6.4.5. Carrier Distributed Computing Market
    • 6.4.6. Global MEC Enabled Application Revenue 2017-2022

7. Important Cloud Computing Industry Developments

  • 7.1. Recent Cloud Computing Mergers and Acquisitions
  • 7.2. Recent Cloud Computing Investments

8. Appendix: Fundamentals of Cloud Computing

  • 8.1. Cloud Computing Deployment Model Categories
  • 8.2. Cloud Technologies and Architecture
  • 8.3. Cloud Computing and Virtualization
  • 8.4. Moving Beyond Cloud Computing
  • 8.5. Rise of the Cloud-Based Networked Enterprise
  • 8.6. General Cloud Service Enablers
    • 8.6.1. Wireless Broadband Connectivity
    • 8.6.2. Security Solutions
    • 8.6.3. Presence and Location
  • 8.7. Personal Cloud Service Enablers
    • 8.7.1. Identity Management
    • 8.7.2. Preference Management
  • 8.8. Cloud Computing Services
    • 8.8.1. Infrastructure as a Service (IaaS)
    • 8.8.2. Platform as a Service (PaaS)
    • 8.8.3. Software as a Service (SaaS)
      • 8.8.3.1. Differences between IaaS, SaaS, and PaaS
  • 8.9. Emerging Models: XaaS (Everything as a Service)
    • 8.9.1. Business Process as a Service (BPaaS)
    • 8.9.2. Communication as a Service (CaaS)
    • 8.9.3. Monitoring as a Service (MaaS)
    • 8.9.4. Network-as-a-service (NaaS)
    • 8.9.5. Storage as a Service (SaaS)
  • 8.10. APIs and Database
  • 8.11. The Need for Federated Database Model
  • 8.12. Enterprise Resource Planning (ERP) in the Cloud
  • 8.13. Supply Chain Management in the Cloud
  • 8.14. Emerging Cloud Based Applications
    • 8.14.1. B2B Applications
    • 8.14.2. B2C Applications
    • 8.14.3. Entertainment in the Cloud: TV, Video, Gaming and More
  • 8.15. Cloud Myths and Realities

9. Appendix: MEC Technology and Solutions

  • 9.1. MEC Characteristics
    • 9.1.1. Processing at the Edge
    • 9.1.2. Low Latency Network
    • 9.1.3. Context Based Service
    • 9.1.4. Location Service and Analytics
  • 9.2. Benefits of MEC
    • 9.2.1. MEC Business Benefits
    • 9.2.2. Technical Benefits
    • 9.2.3. Communication Service Provider Specific Benefits
  • 9.3. MEC Architecture and Platforms
    • 9.3.1. MEC Platform Architecture and Building Blocks
      • 9.3.1.1. MEC Infrastructure
      • 9.3.1.2. MEC Application Platform
      • 9.3.1.3. MEC Management Framework
    • 9.3.2. MEC Value Chain for Edge Cloud
  • 9.4. MEC Technology and Building Blocks
    • 9.4.1. Radio Network Information Service
    • 9.4.2. Traffic Offload Function
    • 9.4.3. Interface
    • 9.4.4. Configuration Management
    • 9.4.5. Application Lifecycle Management
    • 9.4.6. Hardware Virtualization and Infrastructure Management System
    • 9.4.7. Core Network Elements

Figures

  • Figure 1: Cloud Computing Enterprise Applications
  • Figure 2: Cloud Computing Market Segmentation
  • Figure 3: Global Cloud Computing Revenue 2017-2022
  • Figure 4: Global Cloud Computing Revenue by SaaS, PaaS, and IaaS 2017-2022
  • Figure 5: Global Cloud Services Revenue 2017-2022
  • Figure 6: Cloud Computing Revenue by Region 2017-2022
  • Figure 7: Global Cloud Computing Revenue in Government Sector 2017-2022
  • Figure 8: Global Cloud Computing Revenue in Financial Sector 2017-2022
  • Figure 9: Global Cloud Computing Revenue in Healthcare 2017-2022
  • Figure 10: Global Cloud Computing Revenue in Retail Sector 2017-2022
  • Figure 11: Global Cloud Computing Revenue in Manufacturing 2017-2022
  • Figure 12: Global Cloud Computing Revenue in Automobile Sector 2017-2022
  • Figure 13: Global Cloud Computing Revenue in Agriculture 2017-2022
  • Figure 14: Cloud Based IoT Data Processing
  • Figure 15: Distributed Cloud IoT Data Architecture
  • Figure 16: IoT Data will NOT be Simply Stored in the Cloud
  • Figure 17: Real-time IoT Data Management and Analytics
  • Figure 18: Security in IoT Data Architecture
  • Figure 19: Cloud Computing Architecture
  • Figure 20: Server Virtualization Architecture
  • Figure 21: Deployment Ratio of by Categories of SaaS Application
  • Figure 22: Difference between IaaS, PaaS, and SaaS
  • Figure 23: Cloud Services and APIs
  • Figure 24: Cloud ERP vs. On-premise ERP
  • Figure 25: SCM Cloud Structure

Tables

  • Table 1: Global Cloud Computing Revenue 2017-2022
  • Table 2: Revenue by Cloud Computing Deployment Type
  • Table 3: Global Cloud Computing Revenue by SaaS, PaaS, and IaaS 2017-2022
  • Table 4: Global Cloud Computing IaaS by Sub-segment 2017-2022
  • Table 5: Global Cloud Computing SaaS Revenue by Sub-segment 2017-2022
  • Table 6: Global Cloud Computing PaaS Revenue by Sub-segment 2017-2022
  • Table 7: Global Cloud Services Revenue 2017-2022
  • Table 8: Global Revenues for Cloud Services by sub-segments 2022
  • Table 9: Cloud Computing Revenue by Region 2017-2022
  • Table 10: Global Cloud Computing Revenue by Industry Vertical 2017-2022
  • Table 11: Global Cloud Computing Revenue in Government Sector 2017-2022
  • Table 12: Global Cloud Computing Revenue in Financial Sector 2017-2022
  • Table 13: Global Cloud Computing Revenue in Healthcare 2017-2022
  • Table 14: Global Cloud Computing Revenue in Retail Sector 2017-2022
  • Table 15: Global Cloud Computing Revenue in Manufacturing 2017-2022
  • Table 16: Global Cloud Computing Revenue in Automobile Sector 2017-2022
  • Table 17: Global Cloud Computing Revenue in Agriculture 2017-2022
  • Table 18: Global Carrier Cloud Revenue 2017-2022
  • Table 19: Carrier Cloud Revenue by Region 2017-2022
  • Table 20: Global Carrier Cloud Revenue by Industry Vertical 2017-2022
  • Table 21: Global Carrier Cloud Revenue by Services and Solutions 2017-2022
  • Table 22: Global Revenues for Centralized Carrier Cloud Services 2017-2022
  • Table 23: Global MEC Revenue by Application 2017-2022
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