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市場調查報告書

高速傳輸的巨量資料策略分析

Strategic Analysis of Big Data in Rapid Transit

出版商 Frost & Sullivan 商品編碼 322710
出版日期 內容資訊 英文 62 Pages
商品交期: 最快1-2個工作天內
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高速傳輸的巨量資料策略分析 Strategic Analysis of Big Data in Rapid Transit
出版日期: 2014年12月31日 內容資訊: 英文 62 Pages
簡介

本報告提供鐵路業界車輛運輸的主要動向和市場活性因子、市場機會、市場規模、市場預測、競爭因子、市佔率、產品組合、地區能力等。

第1章 報告摘要

  • 主要調查結果
  • 巨量資料的影響
  • 巨量資料最大市場的北美與歐洲
  • 主要調查結果與今後展望

第2章 調查的範圍、目的、背景以及調查手法

  • 調查範圍
  • 調查目標與目的
  • 主要調查結果
  • 調查背景
  • 調查手法

第3章 定義與分類

  • 巨量資料市場-分類

第4章 巨量資料的簡介

  • 巨量資料的簡介
  • 千兆位元組規模的巨量資料
  • 預測分析為商業智慧的未來反覆

第5章 巨量資料的移轉策略

  • 漸進式巨量資料的導入有衰退的最低風險
  • Hadoop快速成為巨量資料的標準核心
  • NoSQL成為社群及媒體內容的因應和管理的關鍵
  • 媒體分析打開預測分析的嶄新大道
  • 在巨量資料最具有重要意義的預測分析
  • 需要新專任角色的巨量資料的預測分析
  • 預測分析帶來實績改善的可能性

第6章 巨量資料在鐵路上的意義

  • 鐵路環境下的巨量資料用途事例
  • 巨量資料的用途-需求的模式化與乘客預測
  • 巨量資料的用途-鐵路運行自動化
  • 巨量資料的用途-路徑計畫與時程
  • 巨量資料的用途-車輛定位系統自動化
  • 巨量資料的用途-運費徵收自動化與電子車票
  • 巨量資料的用途-最先進的領先排名與轉換

第7章 大趨勢與業界整合的影響

  • 前4大趨勢在土耳其鐵路市場的影響
  • 前4大趨勢在鐵路巨量資料市場的影響

第8章 活性因子與限制因子-市場整體

  • 市場活性因子
  • 活性因子的解說
  • 市場限制因子
  • 限制因子的解說

第9章 市場預測與動向-鐵路的巨量資料

  • 收益預測情境分析
  • 要素別鐵路巨量資料市場
  • 來源別巨量資料收益

第10章 市佔率與競爭分析-鐵路巨量資料市場

  • 領導全球巨量資料解決方案的IBM、HP、Dell
  • 前20大巨量資料服務供應商-巨量資料的預估收益
  • 巨量資料的純技術服務供應商-預估年度收益

第11章 個案研究

  • Union Pacific
  • 資料虛擬化活用的先鋒Portland TriMet

第12章 結論

  • 結論-3大預測
  • 主要結論與今後展望

第13章 附屬資料

目錄
Product Code: M9BA-01-00-00-00

Strategies to Achieve Predictive Analytics Biggest Driver for Big Data Implementation in Rail

The strategic insight provides an outlook of growth opportunities in the Global rail market for Big Data. Secondary and primary research was conducted, including interviews with suppliers, regulation authorities, and distributors. The study discusses key trends, market drivers, opportunities, market size, and forecast of rolling stock deliveries for the rail industry It also highlights competitive factors, competitor market shares, product portfolio, and capabilities for this region. Key conclusions and a future outlook of the market have been provided. The base year is 2014; the forecast period is through 2021.

Key Questions This Study Will Answer

  • What are the possible business Big Data can generate across the rail industry?
  • Why is Big Data important for the rail environment and where it can help in terms of features and services?
  • What are the current opportunities within the rail industry using Big Data and related, successful case studies?
  • What market dynamics are influencing the implementation of Big Data?

Table of Contents

1. EXECUTIVE SUMMARY

Executive Summary

  • 1. Executive Summary-Key Findings
  • 2. Implications of Big Data
  • 3. North America and Europe are the Largest Markets for Big Data
  • 4. Key Findings and Future Outlook

2. RESEARCH SCOPE, OBJECTIVES, BACKGROUND, AND METHODOLOGY

Research Scope, Objectives, Background, and Methodology

  • 1. Research Scope
  • 2. Research Aims and Objectives
  • 3. Key Questions This Study Will Answer
  • 4. Research Background
  • 5. Research Methodology

3. DEFINITIONS AND SEGMENTATION

Definitions and Segmentation

  • 1. Big Data Market-Segmentation

4. INTRODUCTION TO BIG DATA

Introduction to Big Data

  • 1. Introduction to Big Data
  • 2. Scale of Big Data is in Petabytes
  • 3. Predictive Analysis is the Future iteration of Business Intelligence

5. MIGRATION STRATEGY TOWARDS BIG DATA

Migration Strategy Towards Big Data

  • 1. Incremental Big Data Implementation has Least Risk to Obsolescence
  • 2. Hadoop is Quickly Becoming the Standard Core of Big Data
  • 3. NoSQL is Key to Handling and Managing Social and Media Content
  • 4. Media Analytics Open New Pathways to Predictive Analytics
  • 5. Predictive Analytics is the Most important Implication of Big Data
  • 6. Predictive Analytics from Big Data Requires Dedicated new Job Roles
  • 7. Performance Improvement Possibilities with Predictive Analytics

6. IMPLICATIONS OF BIG DATA ON RAIL

Implications of Big Data on Rail

  • 1. Examples of Big Data Applications in the Rail Environment
  • 2. Big Data Application-Demand Modeling and Ridership Forecasting
  • 3. Big Data Application -Automated Train Operation
  • 4. Big Data Application-Route Planning and Scheduling
  • 5. Big Data Application-Automatic Vehicle Location
  • 6. Big Data Application-Automated Fare Collection and E-Ticketing
  • 7. Big Data Application-Advanced Lead Ranking and Conversion

7. MEGA TRENDS AND INDUSTRY CONVERGENCE IMPLICATIONS

Mega Trends and Industry Convergence Implications

  • 1. Impact of Top 4 Mega Trends on the Turkish Rail Market
  • 2. Impact of Top 4 Mega Trends on the Rail Big Data Market
  • 3. Impact of Top 4 Mega Trends on the Turkish Rail Market

8. DRIVERS AND RESTRAINTS-TOTAL MARKET

Drivers and Restraints-Total Market

  • 1. Market Drivers
  • 2. Drivers Explained
  • 3. Market Restraints
  • 4. Restraints Explained

9. FORECAST AND TRENDS-BIG DATA IN RAIL

Forecast and Trends-Big Data in Rail

  • 1. Revenue Forecast Scenario Analysis
  • 2. Rail Big Data Market F&S By Component
  • 3. Big Data Revenue By Source

10. MARKET SHARE AND COMPETITIVE ANALYSIS-RAIL BIG DATA MARKET

Market Share and Competitive Analysis-Rail Big Data Market

  • 1. IBM, HP and Dell worlds leading suppliers of Big Data Solutions
  • 2. Top 20 Big Data Suppliers-Estimated Revenue from Big Data
  • 3. Pure Big Data Technology Suppliers-Estimated Annual Revenue

11. CASE STUDIES

Case Studies

  • 1. Union Pacific Big Data Case Study
  • 2. Portland TriMet Pioneer In Embracing Data Visualization

12. CONCLUSIONS

Conclusions

  • 1. The Last Word-Three Big Predictions
  • 2. Key Conclusions and Future Outlook

13. APPENDIX

Appendix

  • 1. Legal Disclaimer
  • 2. Total Big Data Market By Component 2014-2021
  • 3. Methodology
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