Global Information
회사소개 | 문의

빅데이터 분석, 모바일 엣지 컴퓨팅(MEC) 및 실시간 데이터 : 기술, 솔루션, 시장 전망(2018-2023년)

Big Data Analytics, Mobile Edge Computing, and Real-time Data: Technologies, Solutions, and Market Outlook 2018 - 2023

리서치사 Mind Commerce
발행일 2018년 06월 상품 코드 647354
페이지 정보 영문 384 Pages
가격
US $ 1,995 ₩ 2,251,900 PDF by E-mail (Single User License)
US $ 2,995 ₩ 3,380,700 PDF by E-mail (2-5 User License)
US $ 3,995 ₩ 4,509,500 PDF by E-mail (Enterprise Site License)
US $ 4,995 ₩ 5,638,300 PDF by E-mail (Global Enterprise License)


빅데이터 분석, 모바일 엣지 컴퓨팅(MEC) 및 실시간 데이터 : 기술, 솔루션, 시장 전망(2018-2023년) Big Data Analytics, Mobile Edge Computing, and Real-time Data: Technologies, Solutions, and Market Outlook 2018 - 2023
발행일: 2018년 06월 페이지 정보 : 영문 384 Pages

한글목차

빅데이터 시장에 대해 조사했으며, 비지니스 사례의 과제/분석, 애플리케이션 이용 사례, 벤더 구도, 밸류체인 분석 및 예측을 이용한 산업의 정량적 평가, MEC 기술의 평가, 아키텍처와 구성요소, 에코시스템, 시장 성장 촉진요인, 애플리케이션, 솔루션 및 도입의 과제 분석 등을 정리하여 전해드립니다.

제1장 개요

제2장 서론

제3장 빅데이터의 과제와 기회

  • 빅데이터 인프라의 보증
  • 비구조화 데이터와 사물인터넷(IoT)

제4장 빅데이터 기술과 비지니스 사례

  • 빅데이터 기술
  • 신규 기술, 툴 및 기법
  • 빅데이터 로드맵
  • 시장 성장 촉진요인
  • 시장 장벽

제5장 빅데이터의 주요 시장

  • 산업용 인터넷 및 M2M(Machine-to-Machine)
  • 소매·호스피탈리티
  • 미디어
  • 유틸리티
  • 금융 서비스
  • 의료·의약품
  • 통신
  • 정부·국토안보
  • 기타 시장

제6장 빅데이터 밸류체인

  • 빅데이터 가치의 세분화
  • 데이터 획득·프로비저닝
  • 데이터 웨어하우징·비지니스 인텔리전스
  • 분석·가시화
  • 액셔닝·비지니스 프로세스 관리
  • 데이터 거버넌스

제7장 빅데이터 분석

  • 빅데이터 분석의 역할과 중요성
  • 빅데이터 분석 프로세스
  • 리액티브 vs. 프로액티브 분석
  • 기술·시행 접근

제8장 규격·규제 구상

  • CSCC(Cloud Standards Customer Council)
  • NIST(National Institute of Standards and Technology)
  • OASIS
  • ODCA(Open Data Center Alliance)
  • CSA(Cloud Security Alliance)
  • ITU(국제전기통신연합)
  • ISO(국제표준화기구)

제9장 세계의 빅데이터 시장·예측

  • 세계의 빅데이터 시장
  • 지역의 빅데이터 시장
  • 빅데이터의 주요 국가
  • 빅데이터 매출 : 제품 부문별

제10장 주요 빅데이터 기업

제11장 부록 : IoT 데이터의 스트리밍을 지원하는 빅데이터

KSA 18.06.12

영문목차

Overview:

While big data analytics solutions are in a rapid adoption phase within most major enterprise companies, edge computing remains in its infancy across most industry verticals. This will soon change with the introduction of mobile edge computing (also known as multi-access edge computing or MEC), driven by major global cellular service providers and supported by a few large IT services firms as well as some key systems integrators. Implemented as a complement to 5G and to optimize capacity allocation, MEC will also enable highly targeted apps and services including zone-based solutions for many segments including smart buildings, self-driving vehicles, robotics, UAVs, and more.

Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service. However, real-time data is anticipated to become a highly valuable aspect of all solutions as a determinant of user behavior, application effectiveness, and identifier of new and enhanced mobile/wireless and/or Internet of Things (IoT) related apps and services.

Mind Commerce findings indicate that no major industry vertical will be without real-time, edge computing driven data strategy by 2023 with up to 64% of all segments implementing at least one IoT related real-time data service offering by 2025. Certain market sub-segments will lead the industry. For example, big data analytics is helping manufacturing sector to design new business models, revamp operational process, and facilitate cost effective supply chain solutions. The use of big data analytics in retail services is becoming an increasingly useful tool to determine future solutions and opportunities to improve sales operations, customer loyalty, company revenues and profitability.

In another example, global governments see a range of opportunities in terms of defense, security, and public safety related improvements possible by leveraging big data and real-time analytics. Mind Commerce see the US Government in particular using data of various types (structured and unstructured) and from various sources as an opportunity to continuously improve predictive capabilities regarding its defense and homeland security programs.

This research provides market projections through 2023 for all key industry verticals including the following:

  • Education
  • Financial Services
  • Government
  • Healthcare
  • Manufacturing
  • Retail Services
  • Telecom and IT
  • Transportation

This research provides an assessment of the global Big Data market, including business case issues/analysis, application use cases, vendor landscape, value chain analysis, and a quantitative assessment of the industry with forecasting from 2018 to 2023. This research also evaluates MEC technology, architecture and building blocks, ecosystem, market drivers, applications, solutions, and deployment challenges.

The report also analyzes MEC industry initiatives, leading companies, and solutions. The report includes a market assessment and forecast for MEC users and MEC revenue globally, regionally, and within the enterprise market. It also includes market analysis for real-time data acquired via edge computing and big data analytics. All direct purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

  • Data analytics providers
  • Wireless service providers
  • ICT managed service providers
  • Software, App, and Content Providers
  • Wireless/mobile infrastructure providers
  • Cloud and IoT product and service providers

Table of Contents

Big Data Market: Business Case, Market Analysis and Forecasts 2018-2023

1. Executive Summary

2. Introduction

  • 2.1. Big Data Overview
    • 2.1.1. Defining Big Data
    • 2.1.2. Big Data Ecosystem
    • 2.1.3. Key Characteristics of Big Data
  • 2.2. Research Background
    • 2.2.1. Scope
    • 2.2.2. Coverage
    • 2.2.3. Company Focus

3. Big Data Challenges and Opportunities

  • 3.1. Securing Big Data Infrastructure
    • 3.1.1. Big Data Infrastructure
    • 3.1.2. Infrastructure Challenges
    • 3.1.3. Big Data Infrastructure Opportunities
  • 3.2. Unstructured Data and the Internet of Things
    • 3.2.1. New Protocols, Platforms, Streaming and Parsing, Software and Analytical Tools
    • 3.2.2. Big Data in IoT will require Lightweight Data Interchange Format
    • 3.2.3. Big Data in IoT will use Lightweight Protocols
    • 3.2.4. Big Data in IoT will need Protocol for Network Interoperability
    • 3.2.5. Big Data in IoT Demands Data Processing on Appropriate Scale

4. Big Data Technology and Business Case

  • 4.1. Big Data Technology
    • 4.1.1. Hadoop
    • 4.1.2. NoSQL
    • 4.1.3. MPP Databases
    • 4.1.4. Others and Emerging Technologies
  • 4.2. Emerging Technologies, Tools, and Techniques
    • 4.2.1. Streaming Analytics
    • 4.2.2. Cloud Technology
    • 4.2.3. Google Search
    • 4.2.4. Customize Analytical Tools
    • 4.2.5. Internet Keywords
    • 4.2.6. Gamification
  • 4.3. Big Data Roadmap
  • 4.4. Market Drivers
    • 4.4.1. Data Volume & Variety
    • 4.4.2. Increasing Adoption of Big Data by Enterprises and Telecom
    • 4.4.3. Maturation of Big Data Software
    • 4.4.4. Continued Investments in Big Data by Web Giants
    • 4.4.5. Business Drivers
  • 4.5. Market Barriers
    • 4.5.1. Privacy and Security: The ‘Big' Barrier
    • 4.5.2. Workforce Re-skilling and Organizational Resistance
    • 4.5.3. Lack of Clear Big Data Strategies
    • 4.5.4. Technical Challenges: Scalability & Maintenance
    • 4.5.5. Big Data Development Expertise

5. Key Sectors for Big Data

  • 5.1. Industrial Internet and Machine-to-Machine
    • 5.1.1. Big Data in M2M
    • 5.1.2. Vertical Opportunities
  • 5.2. Retail and Hospitality
    • 5.2.1. Improving Accuracy of Forecasts and Stock Management
    • 5.2.2. Determining Buying Patterns
    • 5.2.3. Hospitality Use Cases
    • 5.2.4. Personalized Marketing
  • 5.3. Media
    • 5.3.1. Social Media
    • 5.3.2. Social Gaming Analytics
    • 5.3.3. Usage of Social Media Analytics by Other Verticals
    • 5.3.4. Internet Keyword Search
  • 5.4. Utilities
    • 5.4.1. Analysis of Operational Data
    • 5.4.2. Application Areas for the Future
  • 5.5. Financial Services
    • 5.5.1. Fraud Analysis, Mitigation & Risk Profiling
    • 5.5.2. Merchant-Funded Reward Programs
    • 5.5.3. Customer Segmentation
    • 5.5.4. Customer Retention & Personalized Product Offering
    • 5.5.5. Insurance Companies
  • 5.6. Healthcare and Pharmaceutical
    • 5.6.1. Drug Development
    • 5.6.2. Medical Data Analytics
    • 5.6.3. Case Study: Identifying Heartbeat Patterns
  • 5.7. Telecommunications
    • 5.7.1. Telco Analytics: Customer/Usage Profiling and Service Optimization
    • 5.7.2. Big Data Analytic Tools
    • 5.7.3. Speech Analytics
    • 5.7.4. New Products and Services
  • 5.8. Government and Homeland Security
    • 5.8.1. Big Data Research
    • 5.8.2. Statistical Analysis
    • 5.8.3. Language Translation
    • 5.8.4. Developing New Applications for the Public
    • 5.8.5. Tracking Crime
    • 5.8.6. Intelligence Gathering
    • 5.8.7. Fraud Detection and Revenue Generation
  • 5.9. Other Sectors
    • 5.9.1. Aviation
    • 5.9.2. Transportation and Logistics: Optimizing Fleet Usage
    • 5.9.3. Real-Time Processing of Sports Statistics
    • 5.9.4. Education
    • 5.9.5. Manufacturing

6. The Big Data Value Chain

  • 6.1. Fragmentation in the Big Data Value
  • 6.2. Data Acquisitioning and Provisioning
  • 6.3. Data Warehousing and Business Intelligence
  • 6.4. Analytics and Visualization
  • 6.5. Actioning and Business Process Management
  • 6.6. Data Governance

7. Big Data Analytics

  • 7.1. The Role and Importance of Big Data Analytics
  • 7.2. Big Data Analytics Processes
  • 7.3. Reactive vs. Proactive Analytics
  • 7.4. Technology and Implementation Approaches
    • 7.4.1. Grid Computing
    • 7.4.2. In-Database processing
    • 7.4.3. In-Memory Analytics
    • 7.4.4. Data Mining
    • 7.4.5. Predictive Analytics
    • 7.4.6. Natural Language Processing
    • 7.4.7. Text Analytics
    • 7.4.8. Visual Analytics
    • 7.4.9. Association Rule Learning
    • 7.4.10. Classification Tree Analysis
    • 7.4.11. Machine Learning
    • 7.4.12. Neural Networks
    • 7.4.13. Multilayer Perceptron (MLP)
    • 7.4.14. Radial Basis Functions
    • 7.4.15. Geospatial Predictive Modelling
    • 7.4.16. Regression Analysis
    • 7.4.17. Social Network Analysis

8. Standardization and Regulatory Initiatives

  • 8.1. Cloud Standards Customer Council
  • 8.2. National Institute of Standards and Technology
  • 8.3. OASIS
  • 8.4. Open Data Foundation
  • 8.5. Open Data Center Alliance
  • 8.6. Cloud Security Alliance
  • 8.7. International Telecommunications Union
  • 8.8. International Organization for Standardization

9. Global Markets and Forecasts for Big Data

  • 9.1. Global Big Data Markets 2018-2023
  • 9.2. Regional Markets for Big Data 2018-2023
  • 9.3. Leading Countries in Big Data
    • 9.3.1. United States
    • 9.3.2. China
  • 9.4. Big Data Revenue by Product Segment 2018-2023
    • 9.4.1. Database Management Systems
    • 9.4.2. Big Data Integration Tools
    • 9.4.3. Application Infrastructure and Middleware
    • 9.4.4. Business Intelligence Tools and Analytics Platforms
    • 9.4.5. Big Data in Professional Services

10. Key Big Data Players

  • 10.1. Vendor Assessment Matrix
  • 10.2. 1010Data (Advance Communication Corp.)
  • 10.3. Accenture
  • 10.4. Actian Corporation
  • 10.5. Alteryx
  • 10.6. Amazon
  • 10.7. Anova Data
  • 10.8. Apache Software Foundation
  • 10.9. APTEAN (Formerly CDC Software)
  • 10.10. Booz Allen Hamilton
  • 10.11. Bosch Software Innovations: Bosch IoT Suite
  • 10.12. Capgemini
  • 10.13. Cisco Systems
  • 10.14. Cloudera
  • 10.15. CRAY Inc.
  • 10.16. Computer Science Corporation (CSC)
  • 10.17. DataDirect Network
  • 10.18. Dell EMC
  • 10.19. Deloitte
  • 10.20. Facebook
  • 10.21. Fujitsu
  • 10.22. General Electric (GE)
  • 10.23. GoodData Corporation
  • 10.24. Google
  • 10.25. Guavus
  • 10.26. HP Enterprise
  • 10.27. Hitachi Data Systems
  • 10.28. Hortonworks
  • 10.29. IBM
  • 10.30. Informatica
  • 10.31. Intel
  • 10.32. Jasper (Cisco Jasper)
  • 10.33. Juniper Networks
  • 10.34. Longview
  • 10.35. Marklogic
  • 10.36. Microsoft
  • 10.37. Microstrategy
  • 10.38. MongoDB (Formerly 10Gen)
  • 10.39. MU Sigma
  • 10.40. Netapp
  • 10.41. NTT Data
  • 10.42. Open Text (Actuate Corporation)
  • 10.43. Opera Solutions
  • 10.44. Oracle
  • 10.45. Pentaho (Hitachi)
  • 10.46. Qlik Tech
  • 10.47. Quantum
  • 10.48. Rackspace
  • 10.49. Revolution Analytics
  • 10.50. Salesforce
  • 10.51. SAP
  • 10.52. SAS Institute
  • 10.53. Sisense
  • 10.54. Software AG/Terracotta
  • 10.55. Splunk
  • 10.56. Sqrrl
  • 10.57. Supermicro
  • 10.58. Tableau Software
  • 10.59. Tata Consultancy Services
  • 10.60. Teradata
  • 10.61. Think Big Analytics
  • 10.62. TIBCO
  • 10.63. Verint Systems
  • 10.64. VMware (Part of EMC)
  • 10.65. Wipro
  • 10.66. Workday (Platfora)

11. Appendix: Big Data Support of Streaming IoT Data

  • 11.1. Big Data Technology Market Outlook for Streaming IoT Data
    • 11.1.1. IoT Data Management is a Ubiquitous Opportunity across Enterprise
    • 11.1.2. IoT Data becomes a Big Data Revenue Opportunity
    • 11.1.3. Real-time Streaming IoT Data Analytics becoming a Substantial Business Opportunity
  • 11.2. Global Streaming IoT Data Analytics Revenue
    • 11.2.1. Overall Streaming Data Analytics Revenue for IoT
    • 11.2.2. Global Streaming IoT Data Analytics Revenue by App, Software, and Services
    • 11.2.3. Global Streaming IoT Data Analytics Revenue in Industry Verticals
  • 11.3. Regional Streaming IoT Data Analytics Revenue
    • 11.3.1. Revenue in Region
    • 11.3.2. APAC Market Revenue
    • 11.3.3. Europe Market Revenue
    • 11.3.4. North America Market Revenue
    • 11.3.5. Latin America Market Revenue
    • 11.3.6. ME&A Market Revenue
  • 11.4. Streaming IoT Data Analytics Revenue by Country
    • 11.4.1. Revenue by APAC Countries
    • 11.4.2. Revenue by Europe Countries
    • 11.4.3. Revenue by North America Countries
    • 11.4.4. Revenue by Latin America Countries
    • 11.4.5. Revenue by ME&A Countries

Figures

  • Figure 1: Big Data Ecosystem
  • Figure 2: Key Characteristics of Big Data
  • Figure 3: Big Data Use Cases in Industry Verticals
  • Figure 4: Big Data Stack
  • Figure 5: Framework for Big Data in IoT
  • Figure 2: NoSQL vs Legacy DB Performance Comparisons
  • Figure 7: Roadmap Big Data Technologies 2018-2030
  • Figure 8: The Big Data Value Chain
  • Figure 9: Big Data Value Flow
  • Figure 10: Big Data Analytics
  • Figure 11: Global Big Data Markets 2018-2023
  • Figure 12: Regional Big Data Markets 2018-2023
  • Figure 13: Database Management Systems 2018-2023
  • Figure 14: Data Integration and Quality Tools 2018-2023
  • Figure 15: Application Infrastructure and Middleware 2018-2023
  • Figure 16: Business Intelligence Tools and Analytics Platforms 2018-2023
  • Figure 17: Big Data in Professional Services 2018-2023
  • Figure 18: Big Data Vendor Ranking Matrix
  • Figure 19: Streaming IoT Data Sources Compared
  • Figure 20: Overall Streaming IoT Data Analytics

Tables

  • Table 1: Global Big Data Markets 2018-2023
  • Table 2: Regional Big Data Markets 2018-2023
  • Table 3: Big Data Markets by Product Segments 2018-2023
  • Table 4: Database Management Systems 2018-2023
  • Table 5: Data Integration Tools 2018-2023
  • Table 6: Application Infrastructure and Middleware 2018-2023
  • Table 7: Business Intelligence Tools and Analytics Platforms 2018-2023
  • Table 8: Big Data in Professional Services 2018-2023
  • Table 9: Big Data Analytics Platforms by Company
  • Table 10: Global Streaming IoT Data Analytics Revenue by App, Software, and Service
  • Table 11: Global Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 12: Retail Streaming IoT Data Analytics Revenue by Retail Segment
  • Table 13: Retail Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 14: Telecom & IT Streaming IoT Data Analytics Rev by Segment
  • Table 15: Telecom & IT Streaming IoT Data Analytics Rev by App, Software, and Services
  • Table 16: Energy & Utilities Streaming IoT Data Analytics Rev by Segment
  • Table 17: Energy & Utilities Streaming IoT Data Analytics Rev by App, Software, and Services
  • Table 18: Government Streaming IoT Data Analytics Revenue by Segment
  • Table 19: Government Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 20: Healthcare & Life Science Streaming IoT Data Analytics Revenue by Segment
  • Table 21: Healthcare & Life Science Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 22: Manufacturing Streaming IoT Data Analytics Revenue by Segment
  • Table 23: Manufacturing Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 24: Transportation & Logistics Streaming IoT Data Analytics Revenue by Segment
  • Table 25: Transportation & Logistics Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 26: Banking and Finance Streaming IoT Data Analytics Revenue by Segment
  • Table 27: Banking & Finance Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 28: Smart Cities Streaming IoT Data Analytics Revenue by Segment
  • Table 29: Smart Cities Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 30: Automotive Streaming IoT Data Analytics Revenue by Segment
  • Table 31: Automotive Streaming IoT Data Analytics Revenue by Apps, Software, and Services
  • Table 32: Education Streaming IoT Data Analytics Revenue by Segment
  • Table 33: Education Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 34: Outsourcing Service Streaming IoT Data Analytics Revenue by Segment
  • Table 35: Outsourcing Service Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 36: Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 37: Streaming IoT Data Analytics Revenue in Region
  • Table 38: APAC Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 39: APAC Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 40: APAC Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 41: Europe Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 42: Europe Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 43: Europe Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 44: North America Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 45: North America Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 46: North America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 47: Latin America Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 48: Latin America Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 49: Latin America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 50: ME&A Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 51: ME&A Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 52: ME&A Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 53: Streaming IoT Data Analytics Revenue by APAC Countries
  • Table 54: Japan Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 55: Japan Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 56: China Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 57: China Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 58: India Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 59: India Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 60: Australia Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 61: Australia Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 62: Streaming IoT Data Analytics Revenue by Europe Countries
  • Table 63: Germany Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 64: Germany Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 65: UK Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 66: UK Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 67: France Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 68: France Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 69: Streaming IoT Data Analytics Revenue by North America Countries
  • Table 70: US Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 71: US Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 72: Canada Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 73: Canada Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 74: Streaming IoT Data Analytics Revenue by Latin America Countries
  • Table 75: Brazil Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 76: Brazil Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 77: Mexico Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 78: Mexico Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 79: Streaming IoT Data Analytics Revenue by ME&A Countries
  • Table 80: South Africa Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 81: South Africa Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 82: UAE Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 83: UAE Streaming IoT Data Analytics Revenue in Industry Vertical

Multi-access Edge Computing (MEC): Market Outlook and Forecasts 2018-2023

1. Executive Summary

2. Introduction

  • 2.1. Understanding Multi-access Edge Computing
    • 2.1.1. Edge Computing in an ICT Context
    • 2.1.2. Proximity Computing: The Edge in Physical and Logical Context
    • 2.1.3. Edge Computing vs. Other Computational Approaches
    • 2.1.4. Multi-access Edge Computing
  • 2.2. Important Characteristics of MEC
    • 2.2.1. Processing at the Edge
    • 2.2.2. Low Latency
    • 2.2.3. Context Based
    • 2.2.4. Location and Analytics
  • 2.3. MEC Benefits
    • 2.3.1. Business Benefits
    • 2.3.2. Technical Benefits
    • 2.3.3. Mobile Network Operator Benefits
    • 2.3.4. Key Element of Carrier Heterogeneous Network Strategy

3. MEC Technology, Platforms, and Architecture

  • 3.1. MEC Platform Architecture Building Blocks
    • 3.1.1. MEC Infrastructure
    • 3.1.2. MEC Application Platforms
    • 3.1.3. MEC Management Framework
  • 3.2. The Edge Cloud Computing Value Chain
  • 3.3. MEC Technology Building Blocks
    • 3.3.1. Radio Network Information Service
    • 3.3.2. Traffic Offload Function
    • 3.3.3. MEC Interfaces
    • 3.3.4. Configuration Management
    • 3.3.5. Application Lifecycle Management
    • 3.3.6. VM Operations and Management
    • 3.3.7. Hardware Virtualization and Infrastructure Management
    • 3.3.8. Core Network Elements
    • 3.3.9. Open Standards
  • 3.4. MEC Technology Enablers
    • 3.4.1. Mobile Computing to Mobile Cloud Computing
    • 3.4.2. Cloudlet based Mobile Cloud Computing
    • 3.4.3. Cloudlet to Cloud
    • 3.4.4. PacketCloud Open Platform for Cloudlets
    • 3.4.5. Enterprise Cloud Architecture
    • 3.4.6. Cloudlet Solutions
    • 3.4.7. Cloudlet Storage Frameworks
  • 3.5. MEC Deployment Considerations
    • 3.5.1. MEC Implementation Challenges
    • 3.5.2. MEC Operational Challenges

4. MEC Market Drivers and Opportunities

  • 4.1. Limitations of Cloud Convergence
  • 4.2. IT and Telecom Network Convergence
  • 4.3. Base Station Evolution
  • 4.4. Cell Aggregation
  • 4.5. Virtualization in the Cloud
  • 4.6. Continually Improving Server Capacity
  • 4.7. Data Center to Network Interactions
  • 4.8. Open and Flexible App and Service Ecosystem
  • 4.9. Fifth Generation (5G) Wireless
  • 4.10. Edge Cloud and Data Transferability
  • 4.11. Proximate Cloud Computing
  • 4.12. Increasingly Faster Content Delivery
  • 4.13. Advantages of MEC Small Cell Deployment
  • 4.14. Overall Mobile Data Demand
  • 4.15. Low Latency Applications
  • 4.16. Integration of MEC with Cloud RAN
  • 4.17. MEC Enhances Real-time Data and Analytics
    • 4.17.1. Why Data at the Edge?
    • 4.17.2. Convergence of Distributed Cloud and Big Data

5. MEC Ecosystem

  • 5.1. The Overall Edge Computing Ecosystem
  • 5.2. MEC Ecosystem Players
    • 5.2.1. ETSI MEC ISG
    • 5.2.2. Software and ASPs
    • 5.2.3. OTT Service and Content Providers
    • 5.2.4. Network Infrastructure and Equipment Providers
    • 5.2.5. Mobile Network Operators
  • 5.3. Individual Company Analysis
    • 5.3.1. ADLINK Technology Inc.
    • 5.3.2. Advantech
    • 5.3.3. Akamai Technologies
    • 5.3.4. Allot Communications
    • 5.3.5. Advanced Micro Devices
    • 5.3.6. Brocade Communications Systems
    • 5.3.7. Cavium Networks
    • 5.3.8. Ceragon Networks
    • 5.3.9. Cisco Systems
    • 5.3.10. Fujitsu Technology Solutions
    • 5.3.11. Hewlett Packard Enterprise
    • 5.3.12. Huawei Technologies Co. Ltd
    • 5.3.13. IBM Corporation
    • 5.3.14. Integrated Device Technology
    • 5.3.15. Intel Corporation
    • 5.3.16. InterDigital Inc.
    • 5.3.17. Juniper Networks
    • 5.3.18. NEC Corporation
    • 5.3.19. Nokia Corporation
    • 5.3.20. PeerApp Ltd.
    • 5.3.21. Quortus
    • 5.3.22. Redhat, Inc.
    • 5.3.23. Saguna Networks
    • 5.3.24. Samsung Electronics Co., Ltd
    • 5.3.25. Sony Corporation
    • 5.3.26. SpiderCloud Wireless
    • 5.3.27. Vasona Networks
    • 5.3.28. Xilinx, Inc.
    • 5.3.29. Yaana Ltd.
    • 5.3.30. ZTE Corporation

6. MEC Application and Service Strategies

  • 6.1. Optimizing the Mobile Cloud
    • 6.1.1. Mobile Network Operator Strategies
    • 6.1.2. Service Strategies and End-user Demand
  • 6.2. Context Aware Services
    • 6.2.1. Commerce
    • 6.2.2. Education
    • 6.2.3. Gaming
    • 6.2.4. Healthcare
    • 6.2.5. Location-based Services
    • 6.2.6. Public Safety
    • 6.2.7. Connected Vehicles
    • 6.2.8. Wearables
  • 6.1. Data Services and Analytics
    • 6.1.1. Localized Real-time Data Becomes King
    • 6.1.2. Anonymizing Local and Real-time Data for Third-party Usage
    • 6.1.3. Increasing Demand for Data as a Service (DaaS) in MEC Environment

7. MEC Market Forecasts 2018-2023

  • 7.1. Global Market 2018-2023
    • 7.1.1. Combined MEC Market
    • 7.1.2. MEC Market by Segment
      • 7.1.2.1. MEC Cloud Server Market
      • 7.1.2.2. MEC Equipment Market
      • 7.1.2.3. MEC Platform Market
      • 7.1.2.4. MEC Software and API Market
      • 7.1.2.5. MEC Service Market
    • 7.1.3. MEC Enterprise CAPEX and OPEX Spend
    • 7.1.4. MEC Network Migration
    • 7.1.5. MEC Enterprise Adoption
  • 7.2. MEC Regional Market 2018-2023
    • 7.2.1. North America Market Forecast
    • 7.2.2. APAC Market Forecasts
    • 7.2.3. Europe Market Forecast
  • 7.3. MEC Network Users/Devices 2018-2023
    • 7.3.1. Global MEC Network Users/Devices
    • 7.3.2. MEC Network User by Supporting Network
    • 7.3.3. Regional MEC Network User
      • 7.3.3.1. North America User
      • 7.3.3.2. APAC User
      • 7.3.3.3. Europe User

8. Conclusions and Recommendations

  • 8.1. Anticipated Market Needs and Opportunities
    • 8.1.1. The need for MEC Integration with Public Cloud Platforms
    • 8.1.2. Enterprise (Dedicated and Shared Resources) MEC Integration
    • 8.1.3. Dedicated MEC Public Safety and Homeland Security Infrastructure
  • 8.2. Insights into Future Market Dynamics
    • 8.2.1. MEC will Facilitate Downward Price Pressure on Non-real-time Data
    • 8.2.2. MEC will Drive Demand for Virtual Network Operators
    • 8.2.3. MEC will Drive the Need for New Players as well as M&A

9. Appendix: Real-time Data Analytics Revenue 2018-2023

  • 9.1. Global Streaming Data Analytics Revenue
  • 9.2. Global Real-time Data Analytics Revenue by App, Software, and Services
  • 9.3. Global Real-time Data Analytics Revenue in Industry Verticals
    • 9.3.1. Real-time Data Analytics Revenue in Retail
      • 9.3.1.1. Real-time Data Analytics Revenue by Retail Segment
      • 9.3.1.2. Real-time Data Analytics Retail Revenue by App, Software, and Service
    • 9.3.2. Real-time Data Analytics Revenue in Telecom and IT
      • 9.3.2.1. Real-time Data Analytics Revenue by Telecom and IT Segment
      • 9.3.2.2. Real-time Data Analytics Revenue by Telecom and IT App, Software, and Service
    • 9.3.3. Real-time Data Analytics Revenue in Energy and Utility
      • 9.3.3.1. Real-time Data Analytics Revenue by Energy and Utility Segment
      • 9.3.3.2. Real-time Data Analytics Energy and Utilities Revenue by App, Software, and Service
    • 9.3.4. Real-time Data Analytics Revenue in Government
      • 9.3.4.1. Real-time Data Analytics Revenue by Government Segment
      • 9.3.4.2. Real-time Data Analytics Government Revenue by App, Software, and Service
    • 9.3.5. Real-time Data Analytics Revenue in Healthcare and Life Science
      • 9.3.5.1. Real-time Data Analytics Revenue by Healthcare Segment
    • 9.3.6. Real-time Data Analytics Revenue in Manufacturing
      • 9.3.6.1. Real-time Data Analytics Revenue by Manufacturing Segment
      • 9.3.6.2. Real-time Data Analytics Manufacturing Revenue by App, Software, and Service
    • 9.3.7. Real-time Data Analytics Revenue in Transportation and Logistics
      • 9.3.7.1. Real-time Data Analytics Revenue by Transportation and Logistics Segment
      • 9.3.7.2. Real-time Data Analytics Transportation and Logistics Revenue by App, Software, and Service
    • 9.3.8. Real-time Data Analytics Revenue in Banking and Finance
      • 9.3.8.1. Real-time Data Analytics Revenue by Banking and Finance Segment
      • 9.3.8.2. Real-time Data Analytics Revenue by Banking and Finance App, Software, and Service
    • 9.3.9. Real-time Data Analytics Revenue in Smart Cities
      • 9.3.9.1. Real-time Data Analytics Revenue by Smart City Segment
      • 9.3.9.2. Real-time Data Analytics Revenue by Smart City App, Software, and Service
    • 9.3.10. Real-time Data Analytics Revenue in Automotive
      • 9.3.10.1. Real-time Data Analytics Revenue by Automobile Industry Segment
      • 9.3.10.2. Real-time Data Analytics Revenue by Automotive Industry App, Software, and Service
    • 9.3.11. Real-time Data Analytics Revenue in Education
      • 9.3.11.1. Real-time Data Analytics Revenue by Education Industry Segment
      • 9.3.11.2. Real-time Data Analytics Revenue by Education Industry App, Software, and Service
    • 9.3.12. Real-time Data Analytics Revenue in Outsourcing Services
      • 9.3.12.1. Real-time Data Analytics Revenue by Outsourcing Segment
      • 9.3.12.2. Real-time Data Analytics Revenue by Outsourcing Industry App, Software, and Service
  • 9.4. Real-time Data Analytics Revenue by Leading Vendor Platform
    • 9.4.1. Global Investment in Data by Industry Sector 2018-2023

Figures

  • Figure 1: MEC Value Chain for Edge Cloud Computing
  • Figure 2: Extreme Outdoor Server
  • Figure 3: Cloudlet based PacketCloud Framework
  • Figure 4: MEC and C-RAN Architecture
  • Figure 5: Mobile Edge Computing Network
  • Figure 6: MEC Network and Application Clients
  • Figure 6: ETSI MEC ISG Members
  • Figure 7: MEC enables Many Cloud-based Apps
  • Figure 8: Combined MEC Market Size 2018-2023
  • Figure 9: MEC Network Migration Ratio
  • Figure 10: MEC Enterprise Adoption Ratio
  • Figure 12: Global MEC Network Users 2018-2023
  • Figure 13: Global Real-time Data Analytics 2018-2023
  • Figure 14: Investment in Data by Industry Vertical 2018-2023

Tables

  • Table 1: MEC Market Size by Market Segment 2018-2023
  • Table 2: MEC Cloud Server Market by Category 2018-2023
  • Table 3: MEC Equipment Market by Category 2018-2023
  • Table 4: MEC Platform Market by Category 2018-2023
  • Table 5: MEC Software and API Market in Vertical Segment 2018-2023
  • Table 6: MEC Service Market by Type 2018-2023
  • Table 7: MEC Optimization CAPEX and OPEX Spend by Enterprise
  • Table 8: MEC Market by Region 2018-2023
  • Table 9: North America MEC Market by Segment 2018-2023
  • Table 10: North America MEC Cloud Server Market by Category 2018-2023
  • Table 11: North America MEC Equipment Market by Category 2018-2023
  • Table 12: North America MEC Platform Market by Category 2018-2023
  • Table 13: North America MEC Software and API Market in Vertical Segment 2018-2023
  • Table 14: North America MEC Service Market by Type 2018-2023
  • Table 15: North America MEC Market by Country 2018-2023
  • Table 16: APAC MEC Market by Market Segment 2018-2023
  • Table 17: APAC MEC Cloud Server Market by Category 2018-2023
  • Table 18: APAC MEC Equipment Market by Category 2018-2023
  • Table 19: APAC MEC Platform Market by Category 2018-2023
  • Table 20: APAC MEC Software and API Market in Vertical Segment 2018-2023
  • Table 21: APAC MEC Service Market by Type 2018-2023
  • Table 22: APAC MEC Market by Country 2018-2023
  • Table 23: Europe MEC Market by Market Segment 2018-2023
  • Table 24: Europe MEC Cloud Server Market by Category 2018-2023
  • Table 25: Europe MEC Equipment Market by Category 2018-2023
  • Table 26: Europe MEC Platform Market by Category 2018-2023
  • Table 27: Europe MEC Software and API Market in Vertical Segment 2018-2023
  • Table 28: Europe MEC Service Market by Type 2018-2023
  • Table 29: Europe MEC Market by Country 2018-2023
  • Table 30: MEC User by Supporting Network 2018-2023
  • Table 31: MEC Network User by Region 2018-2023
  • Table 32: North America MEC User by Supporting Network 2018-2023
  • Table 33: APAC MEC User by Supporting Network 2018-2023
  • Table 34: Europe MEC User by Supporting Network 2018-2023
  • Table 35: Global Real-time Data Analytics Revenue by App, Software, and Service 2018-2023
  • Table 36: Global Real-time Data Analytics Revenue in Industry Vertical 2018-2023
  • Table 37: Retail Real-time Data Analytics Revenue by Retail Segment 2018-2023
  • Table 38: Retail Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 39: Telecom and IT Real-time Data Analytics Rev by Segment 2018-2023
  • Table 40: Telecom and IT Real-time Data Analytics Rev by App, Software, and Services 2018-2023
  • Table 41: Energy and Utilities Real-time Data Analytics Rev by Segment 2018-2023
  • Table 42: Energy and Utilities Real-time Data Analytics Rev by App, Software, and Services 2018-2023
  • Table 43: Government Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 44: Government Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 45: Healthcare and Life Science Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 46: Healthcare and Life Science Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 47: Manufacturing Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 48: Manufacturing Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 49: Transportation and Logistics Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 50: Transportation and Logistics Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 51: Banking and Finance Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 52: Banking and Finance Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 53: Smart Cities Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 54: Smart Cities Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 55: Automotive Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 56: Automotive Real-time Data Analytics Revenue by Apps, Software, and Services 2018-2023
  • Table 57: Education Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 58: Education Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 59: Outsourcing Service Real-time Data Analytics Revenue by Segment 2018-2023
  • Table 60: Outsourcing Service Real-time Data Analytics Revenue by App, Software, and Services 2018-2023
  • Table 61: Real-time Data Analytics Revenue by Leading Vendor Platforms 2018-2023
  • Table 62: Investment in Data by Industry Vertical 2018-2023
Back to Top
아시아 최대 시장정보 제공
전세계에서 발행되고 있는 모든 시장조사보고서를
다루고 있습니다.
사이트에서 검색되지 않는 보고서도 문의 바랍니다.