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Dublin, Aug. 29, 2018 (GLOBE NEWSWIRE) — The “Big Data, Advanced Analytics, and Artificial Intelligence: Market for Infrastructure and Services 2018 – 2023” report has been added to ResearchAndMarkets.com’s offering.

This research provides an in-depth 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.

It also evaluates the components of Big Data infrastructure and security framework. Additional Big Data topics covered in this report include: Analysis of infrastructure and important issues such as security and privacy, a review of investments sectors and specific use cases for the Big Data market, analysis of the value chain of Big Data and the major players, assessment of the business case, growth drivers and barriers, assessment of the vendor landscape of leading players within the Big Data market.

Solutions for managing unstructured data are evolving beyond systems aligned towards primarily human-generated data (such as social networking, messaging, and browsing habits) towards increasingly greater emphasis upon machine-generated data found across many industry verticals. For example, manufacturing and healthcare are anticipated to create massive amounts of data that may be rendered useful only through advanced analytics and various Artificial Intelligence (AI) technologies. Emerging networks and systems such as IoT and edge computing will generate substantial amounts of unstructured data, which will present both technical challenges and market opportunities for operating companies and their vendors.

The AI component of this research provides a multi-dimensional view into the AI market including analysis of embedded devices and components, embedded software, and AI platforms. This research also assesses the combined Artificial Intelligence (AI) marketplace including embedded IoT and non-IoT devices, embedded components (including AI chipsets), embedded software and AI platforms, and related services. It includes an evaluation of leading solution providers including hardware, software, integrated platforms, and services.

The report includes quantitative analysis with forecasts covering AI technology and systems by type, use case, application, and industry vertical. Forecast also cover each major market sector including consumer, enterprise, industrial, and government. The report also includes specific industry recommendations with respect to Artificial Intelligence hardware, software and services.

Key Topics Covered:

Big Data Market 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
11.4 Streaming IoT Data Analytics Revenue by Country

Artificial Intelligence Market 2018 – 2023

1 Introduction
1.1 Executive Summary
1.2 Research Objectives
1.3 Select Findings
1.4 Target Audience
1.5 Companies in the Report

2 Overview
2.1 Defining Artificial Intelligence
2.2 Artificial General Intelligence
2.3 Artificial Super Intelligence
2.4 Artificial Intelligence Types
2.5 Artificial Intelligence Language
2.6 Artificial Intelligence Systems
2.7 AI Outcomes and Enterprise Benefits
2.8 Conversational User Interfaces
2.9 Cognitive Computing and Swarm Intelligence
2.10 AI Market Drivers and Impact
2.11 AI Market Constraints
2.12 AI Market Opportunities
2.13 AI Market Outlook and Predictions

3 Technology and Impact Analysis
3.1 AI Technology Matrix
3.1.1 Machine Learning
3.1.1.1 Deep Learning
3.1.1.2 Supervised vs. Unsupervised Learning
3.1.1.3 Reinforcement Learning
3.1.2 Natural Language Processing
3.1.3 Computer Vision
3.1.4 Speech Recognition
3.1.5 Context Aware Processing
3.1.6 Artificial Neural Network
3.1.7 Predictive APIs
3.1.8 Autonomous Robotics
3.2 AI Technology Readiness
3.3 Machine Learning APIs
3.3.1 IBM Watson API
3.3.2 Microsoft Azure Machine Learning API
3.3.3 Google Prediction API
3.3.4 Amazon Machine Learning API
3.3.5 BigML
3.3.6 AT&T Speech API
3.3.7 Wit.ai
3.3.8 AlchemyAPI
3.3.9 Diffbot
3.3.10 PredictionIO
3.3.11 General Application Environment
3.4 AI Technology Goal
3.5 AI Tools and Approaches
3.6 Emotion AI
3.6.1 Facial Detection APIs
3.6.2 Text Recognition APIs
3.6.3 Speech Recognition APIs
3.7 IoT Application and Big Data Analytics
3.8 Data Science and Predictive Analytics
3.9 Edge Computing and 5G Network
3.10 Cloud Computing and Machine Learning
3.11 Smart Machine and Virtual Twinning
3.12 Factory Automation and Industry 4.0
3.13 Building Automation and Smart Workplace
3.14 Cloud Robotics and Public Security
3.15 Self Driven Network and Domain Specific Network
3.16 Predictive 3D Design

4 Market and Application Analysis
4.1 AI Market Landscape
4.1.1 Embedded Device and Things
4.1.2 AI Software and Platform
4.1.3 AI Component and Chipsets
4.1.4 AI Service and Deployment
4.2 AI Application Delivery Platform
4.3 AIaaS and MLaaS
4.4 Enterprise Adoption and External Investment
4.5 Enterprise AI Drive Productivity Gains
4.6 AI Patent and Regulatory Framework
4.7 Value Chain Analysis
4.7.1 Artificial Intelligence Companies
4.7.2 IoT Companies and Suppliers
4.7.3 Data Analytics Providers
4.7.4 Connectivity Infrastructure Providers
4.7.5 Components and Chipsets Manufacturers
4.7.6 Software Developers and Data Scientists
4.7.7 End Users
4.8 End User Industry and Application
4.9 AI Use Case Analysis
4.10 Competitive Landscape Analysis

5 Artificial Intelligence Market Analysis and Forecasts 2018 – 2023
5.1 Global AI Market Forecasts 2018 – 2023
5.1.1 Total AI Market
5.1.2 AI Market by Segment
5.1.3 AI Market by Hardware Segment
5.1.3.1 AI Market by Embedded Device
5.1.3.1.1 AI Market by Embedded Non-IoT Device
5.1.3.1.2 AI Market by Embedded IoT Device
5.1.3.1.3 AI Market by Embedded Wearable Device
5.1.3.1.4 AI Market by Embedded Medical and Healthcare Device
5.1.3.1.5 AI Market by Embedded Appliances
5.1.3.1.6 AI Market by Embedded Industrial Machine
5.1.3.1.7 AI Market by Embedded Entertainment Device
5.1.3.1.8 AI Market by Embedded Security Device
5.1.3.1.9 AI Market by Embedded Network Device
5.1.3.1.10 AI Market by Embedded Connected Vehicle Device
5.1.3.1.11 AI Market by Embedded Smart Grid Device
5.1.3.1.12 AI Market by Embedded Military Device
5.1.3.1.13 AI Market by Embedded Energy Management Device
5.1.3.1.14 AI Market by Embedded Agriculture Specific Device
5.1.3.1.15 AI Market by Embedded Industrial IoT Device
5.1.3.2AI Market by Embedded IoT Things and Objects
5.1.3.1AI Market by Embedded Components
5.1.3.1.1 AI Market by Embedded Processors
5.1.3.1.2 AI Embedded Chipsets Market by Technology
5.1.3.1.3 AI Embedded Chipsets Market by Machine Learning Technology
5.1.4 AI Market by Software Segment
5.1.4.1 AI Application Embedded Software Market by Application Type
5.1.4.2 AI Application Embedded Software Market by Deployment Type
5.1.4.3 AI Market by AI Platform
5.1.5 AI Market by Service Segment
5.1.5.1 AI Market by Professional Service Segment
5.1.6 AI Market by AI Technology
5.1.6.1 AI Market by Machine Learning Technology
5.1.7 AI Market by AI System
5.1.8 AI Market by AI Category
5.1.9 AI Market by End User Industry
5.1.9.1 AI Market by Medical and Healthcare Application
5.1.9.2 AI Market by Manufacturing Application
5.1.9.3 AI Market by Consumer Electronics Application
5.1.9.4 AI Market by Automotive and Transportation Application
5.1.9.5 AI Market by Retail and Apparel Application
5.1.9.6 AI Market by Marketing Application
5.1.9.7 AI Market by FinTech Application
5.1.9.8 AI Market by Building and Construction Application
5.1.9.9 AI Market by Agriculture Application
5.1.9.10 AI Market by Security and Surveillance Application
5.1.9.11 AI Market by Government and Military Application
5.1.9.12 AI Market by Human Resource Application
5.1.9.13 AI Market by Law Application
5.1.9.14 AI Market by Telecommunication and IT Application
5.1.9.15 AI Market by Oil, Gas and Mining Application
5.1.9.16 AI Market by Logistics Application
5.1.9.17 AI Market by Education Application
5.1.10 AI Market by Consumer, Enterprise, Industrial, and Government Sector
5.2 Regional AI Market Forecasts 2018 – 2023
5.2.1 AI Market by Region

6 AI Embedded IoT Device and Things Deployment Forecasts 2018 – 2023
6.1 AI Embedded Connected IoT Device Deployment 2018 – 2023
6.1.1 Total AI Embedded Connected IoT Device
6.1.2 AI Embedded Connected IoT Device by IoT Sector
6.1.2.1 AI Embedded Consumer IoT Device Deployment
6.1.2.1.1 AI Embedded Consumer IoT Device Deployment by Device Type
6.1.2.1.2 AI Embedded Consumer IoT Device Deployment by Wearable Device
6.1.2.1.3 AI Embedded Consumer IoT Device Deployment by Smart Appliances
6.1.2.1.4 AI Embedded Consumer IoT Device Deployment by Entertainment Device
6.1.2.1.5 AI Embedded Consumer IoT Device Deployment by Connected Vehicle Device
6.1.2.1.6 AI Embedded Consumer IoT Device Deployment by Energy Device
6.1.2.2AI Embedded Enterprise IoT Device Deployment
6.1.2.2.1 AI Embedded Enterprise IoT Device Deployment by Device Type
6.1.2.2.2 AI Embedded Enterprise IoT Device Deployment by Medical and Healthcare Device
6.1.2.2.3 AI Embedded Enterprise IoT Device Deployment by Home Healthcare Device
6.1.2.2.4 AI Embedded Enterprise IoT Device Deployment by Security Device
6.1.2.2.5 AI Embedded Enterprise IoT Device Deployment by Network Device
6.1.2.3AI Embedded Industrial IoT Device Deployment
6.1.2.3.1 AI Embedded Industrial IoT Device Deployment by Device Type
6.1.2.3.2 AI Embedded Industrial IoT Device Deployment by Industrial Machine
6.1.2.3.3 AI Embedded Industrial IoT Device Deployment by Smart Grid Device
6.1.2.3.4 AI Embedded Industrial IoT Device Deployment by Agriculture Specific Device
6.1.2.4 AI Embedded Government IoT Device Deployment by Device Type
6.1.3 AI Embedded Connected IoT Device Deployment by AI Technology
6.1.4 AI Embedded Connected IoT Device Deployment by AI System
6.1.5 AI Embedded Connected IoT Device Deployment by End User Industry
6.1.6 AI Embedded Connected IoT Device Deployment by Region
6.2 AI Embedded Connected IoT Things and Objects Deployment 2018 – 2023
6.2.1 Total AI Embedded Connected IoT Things and Objects
6.2.2 AI Embedded Connected IoT Things and Objects by IoT Sector
6.2.3 AI Embedded Connected IoT Things and Objects by Application
6.2.4 AI Embedded Connected IoT Things and Objects Deployment by AI Technology
6.2.5 AI Embedded Connected IoT Things and Objects Deployment by AI System
6.2.6 AI Embedded Connected IoT Things and Objects Deployment by End User Industry
6.2.7 AI Embedded Connected IoT Things and Objects Deployment by Region

7 Company Analysis
7.1 NVIDIA Corporation
7.2 IBM Corporation
7.3 Intel Corporation
7.4 Samsung Electronics Co Ltd.
7.5 Microsoft Corporation
7.6 Google Inc.
7.7 Baidu Inc.
7.8 Qualcomm Incorporated
7.9 Huawei Technologies Co. Ltd.
7.10 Fujitsu Ltd.
7.11 H2O.ai
7.12 Juniper Networks, Inc.
7.13 Nokia Corporation
7.14 ARM Limited
7.15 Hewlett Packard Enterprise (HPE)
7.16 Oracle Corporation
7.17 SAP
7.18 Siemens AG
7.19 Apple Inc.
7.20 General Electric (GE)
7.21 ABB Ltd.
7.22 LG Electronics
7.23 Koninklijke Philips N.V
7.24 Whirlpool Corporation
7.25 AB Electrolux
7.26 Wind River Systems Inc.
7.27 Cumulocity GmBH
7.28 Digital Reasoning Systems Inc.
7.29 SparkCognition Inc.
7.30 KUKA AG
7.31 Rethink Robotics
7.32 Motion Controls Robotics Inc.
7.33 Panasonic Corporation
7.34 Haier Group Corporation
7.35 Miele
7.36 Next IT Corporation
7.37 Nuance Communications Inc.
7.38 InteliWISE
7.39 Facebook Inc.
7.40 Salesforce
7.41 Amazon Inc.
7.42 SK Telecom
7.43 motion.ai
7.44 Buddy
7.45 AOL Inc.
7.46 Tesla Inc.
7.47 Inbenta Technologies Inc.
7.48 Cisco Systems
7.49 MAANA
7.50 Veros Systems Inc.
7.51 PointGrab Ltd.
7.52 Tellmeplus
7.53 Xiaomi Technology Co. Ltd.
7.54 Leap Motion Inc.
7.55 Atmel Corporation
7.56 Texas Instruments Inc.
7.57 Advanced Micro Devices (AMD) Inc.
7.58 XILINX Inc.
7.59 Omron Adept Technology
7.60 Gemalto N.V.
7.61 Micron Technology
7.62 SAS Institute Inc.
7.63 AIBrian Inc.
7.64 QlikTech International AB
7.65 MicroStrategy Incorporated
7.66 Brighterion Inc.
7.67 IPsoft Inc.
7.68 24/7.ai Inc.
7.69 General Vision Inc.
7.70 Sentient Technologies Holdings Limited
7.71 Graphcore
7.72 CloudMinds
7.73 Rockwell Automation Inc.
7.74 Tend.ai
7.75 SoftBank Robotics Holding Corp.
7.76 iRobot Corp.
7.77 Lockheed Martin
7.78 Spacex

8 Conclusions and Recommendations
8.1 Advertisers and Media Companies
8.2 Artificial Intelligence Providers
8.3 Automotive Companies
8.4 Broadband Infrastructure Providers
8.5 Communication Service Providers
8.6 Computing Companies
8.7 Data Analytics Providers
8.8 Equipment (AR, VR, and MR) Providers
8.9 Networking Equipment Providers
8.10 Networking Security Providers
8.11 Semiconductor Companies
8.12 IoT Suppliers and Service Providers
8.13 Software Providers
8.14 Smart City System Integrators
8.15 Automation System Providers
8.16 Social Media Companies
8.17 Workplace Solution Providers
8.18 Large Businesses and SMBs

For more information about this report visit https://www.researchandmarkets.com/research/dwgsf6/global_big_data?w=12

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Related Topics: Big Data, Data Analytics , Artificial Intelligence 

(Excerpt) Read more Here | 2018-08-29 13:40:00

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