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市場調查報告書

開採及天然資源產業上的巨量資料:採礦,水、木材、石油、天然氣部門

Big Data in Extraction and Natural Resource Industries: Mining, Water, Timber, Oil and Gas 2014 - 2019

出版商 Mind Commerce 商品編碼 307361
出版日期 內容資訊 英文 49 Pages
商品交期: 最快1-2個工作天內
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開採及天然資源產業上的巨量資料:採礦,水、木材、石油、天然氣部門 Big Data in Extraction and Natural Resource Industries: Mining, Water, Timber, Oil and Gas 2014 - 2019
出版日期: 2014年07月07日 內容資訊: 英文 49 Pages
簡介

天然資源產業為了讓巨量資料和天然資源有非常好的兼容性,有效使用這個豐富資訊,試圖自我定位。巨量資料,是該領域上各組織透過適應指定方向恰當的手法和次序,可開拓的相對未開發的資產。

本報告以開採及天然資源產業上巨量資料為主題,提供這個領域中,為活用巨量資料及分析的課題及機會驗證、主要的企業、解決方案、問題點,以及採礦、水、木材、石油、天然氣等產業的相關展望分析,再加上主要企業概要介紹。

第1章 摘要整理

第2章 簡介

  • 巨量資料概要
  • 資源物流及SCM的巨量資料
  • 供需管理上巨量資料及分析

第3章 資源供給方面的巨量資料

  • 資源管理上的巨量資料
    • 水資源管理
    • 木材及森林管理上的巨量資料
    • 能源、電力的巨量資料
  • 開採及探勘的巨量資料
    • 採礦的巨量資料
    • 石油、天然氣的巨量資料

第4章 資源需求面的巨量資料

  • 為了決定需求的預測分析
    • 結構化的數據模型 VS 巨量資料
    • 資訊來源
  • 價格設定的預測分析
    • 最適合的價格的預測
    • 利益的最佳化 VS 需求的平衡化

第5章 主要企業與解決方案

  • 自來水公司
    • VEOLIA ENVIRONNEMENT
    • SUEZ ENVIRONNEMENT
    • ITT CORPORATION
    • GE WATER
  • 木材公司
    • WEYERHAEUSER CO.
    • PLUM CREEK TIMBER CO.
    • RAYONIER
  • 採礦公司
    • ALCOA
    • NEWMONT MINING CORP
    • TECK
    • FREEPORT-MCMORAN
    • RIO TINTO
    • BHP BILLITON
  • 石油、天然氣公司
    • SAUDI ARAMCO
    • GAZPROM
    • NATIONAL IRANIAN OIL CO.
    • EXXONMOBIL
    • PETROCHINA
    • BP
    • ROYAL DUTCH SHELL
    • PEMEX
    • CHEVRON
    • KUWAIT PETROLEUM CORP.
    • CONOCO PHILLIPS
    • PETROBRAS

第6章 物理資源中巨量資料的未來

第7章 結論與建議

圖表清單

目錄

Big Data and natural resources are made for each other and the natural resources industry is positioning itself to put this wealth of information to better use. Big Data is a comparatively untapped asset that organizations in this vertical can exploit once they adopt a shift of mindset and apply the right methods and processes.

In the natural resource industry, Big Data can come from conventional sources, which are equipment monitoring and maintenance records. Data from these sources is generally captured and used as required, but until now, it was not always preserved for long-term use. With the proper infrastructure and tools, natural resources organizations can gain measurable value from all of these data sources. As the quantity of data, the quantity of sources, and the regularity of data updates increases, so too does the value of Big Data.

This research evaluates the challenges and opportunities for leveraging Big Data and Analytics in the extraction and natural resources industries. The report analyzes companies, solutions, issues, and outlook for mining, water, timber, oil and gas including utilities. The report includes a review of the companies that we believe have key market advantages including scale and scope to best leverage Big Data and Analytics within the extraction and natural resources industry. The report also includes a forecast for Big Data revenue 2014 - 2019.

Target Audience:

  • Telecom services companies
  • Big Data and Analytics companies
  • Telecom and IT infrastructure companies
  • Data infrastructure, cloud, and services companies
  • Extraction and natural resources management companies

Companies in Report:

  • Accenture
  • Alcoa
  • Alteryx
  • Amazon
  • Apache
  • BHP Billiton
  • BP
  • CA Technologies
  • Cassandra
  • Chevron
  • Conoco Phillips
  • Dow Jones
  • eBay
  • EMC
  • ExxonMobil
  • Facebook
  • Freeport-McMoran
  • Gazprom
  • GE Water
  • Google
  • IBM
  • InfoBright
  • InsightPricing
  • Instagram
  • International Telecommunication
  • ITT Corporation
  • Kuwait Petroleum Corp.
  • LinkedIn
  • Microsoft
  • MongoDB
  • National Iranian Oil Co.
  • Newmont Mining Corp
  • Opera Solutions
  • Orkut
  • Pegasystems
  • Pemex
  • Pentaho
  • Petrobras
  • PetroChina
  • Pinterest
  • Plum Creek Timber Co.
  • Practical Ecommerce
  • Rayonier
  • Rio Tinto
  • Royal Dutch Shell
  • Saudi Aramco
  • Schneider
  • Suez Environnement
  • Tableau
  • Teck
  • Teradata
  • Twitter
  • Veolia Environnement
  • Weyerhaeuser Co.
  • WisePricer
  • Yahoo

Table of Contents

1.0 EXECUTIVE SUMMARY

2.0 INTRODUCTION

  • 2.1 BIG DATA OVERVIEW
  • 2.2 BIG DATA IN RESOURCE LOGISTICS AND SCM
  • 2.3 BIG DATA AND ANALYTICS IN SUPPLY AND DEMAND MANAGEMENT

3.0 BIG DATA IN THE RESOURCE SUPPLY SIDE

  • 3.1 BIG DATA IN RESOURCE MANAGEMENT
    • 3.1.1 WATER MANAGEMENT
    • 3.1.2 BIG DATA IN TIMBER AND FOREST MANAGEMENT
    • 3.1.3 BIG DATA IN ENERGY AND ELECTRICITY
  • 3.2 BIG DATA IN EXTRACTION AND EXPLORATION
    • 3.2.1 BIG DATA IN MINING
    • 3.2.2 BIG DATA IN OIL AND GAS

4.0 BIG DATA IN THE RESOURCE DEMAND SIDE

  • 4.1 PREDICTIVE ANALYTICS TO DETERMINE DEMAND
    • 4.1.1 STRUCTURED DATA MODELS VS. BIG DATA
    • 4.1.2 SOURCES OF DATA
  • 4.2 PREDICTIVE ANALYTICS FOR PRICING
    • 4.2.1 PREDICTING OPTIMAL PRICE POINT
    • 4.2.2 OPTIMIZING PROFITS VS. SMOOTHING DEMAND

5.0 LEADING COMPANIES AND SOLUTIONS

  • 5.1 WATER COMPANIES
    • 5.1.1 VEOLIA ENVIRONNEMENT
    • 5.1.2 SUEZ ENVIRONNEMENT
    • 5.1.3 ITT CORPORATION
    • 5.1.4 GE WATER
  • 5.2 TIMBER COMPANIES
    • 5.2.1 WEYERHAEUSER CO.
    • 5.2.2 PLUM CREEK TIMBER CO.
    • 5.2.3 RAYONIER
  • 5.3 MINING COMPANIES
    • 5.3.1 ALCOA 36
    • 5.3.2 NEWMONT MINING CORP
    • 5.3.3 TECK 37
    • 5.3.4 FREEPORT-MCMORAN
    • 5.3.5 RIO TINTO
    • 5.3.6 BHP BILLITON
  • 5.4 OIL AND GAS COMPANIES
    • 5.4.1 SAUDI ARAMCO
    • 5.4.2 GAZPROM
    • 5.4.3 NATIONAL IRANIAN OIL CO.
    • 5.4.4 EXXONMOBIL
    • 5.4.5 PETROCHINA
    • 5.4.6 BP
    • 5.4.7 ROYAL DUTCH SHELL
    • 5.4.8 PEMEX
    • 5.4.9 CHEVRON
    • 5.4.10 KUWAIT PETROLEUM CORP.
    • 5.4.11 CONOCO PHILLIPS
    • 5.4.12 PETROBRAS

6.0 THE FUTURE OF BIG DATA IN PHYSICAL RESOURCES

7.0 CONCLUSIONS AND RECOMMENDATIONS

Figures

  • Figure 1: Usage of Big Data in Resources
  • Figure 2: Smart Solution Options in Organizations
  • Figure 3 : Energy Consumption in a Production Line
  • Figure 4: Big Data Exploration
  • Figure 5: Mining Equipment Analytics
  • Figure 6: Big Data in Oil and Gas
  • Figure 7: Big Data Model
  • Figure 8: Mapping the Predictive Analytics
  • Figure 9: Color to Display and Product Differentiation
  • Figure 10: Global Big Data Revenue 2014 - 2019
  • Figure 11: Regional Big Data Revenue 2014 - 2019
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