Mapped

Platforma inteligencji zbiorowej

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This innovative platform integrates and organizes data from various building systems, enabling users to access clean, structured information for improved decision-making. Key features include rapid data ingestion from both modern and legacy systems, AI-driven data mapping, and seamless connectivity with existing applications, addressing challenges in energy efficiency, operational performance, and user experience for building operators, facility managers, and solution providers across diverse industries.

Mapped można znaleźć w Collective Intelligence Platform kategorie.

Produkty najbardziej podobne do Mapped to:

Drequ Zweryfikowany profil Lansowany → Datore → Novant → MODE → Switch Automation →

Zrzuty ekranu

Filmy na YouTube

Bliższe dane

Mapped Główne cechy

• Device Mappings
  It has over 40,000 device mappings to help identify and connect to different devices.
• Digital Twin API
  Mapped aims to create an API for digital twins to improve building management.
• On-Prem Protocols
  Mapped can work with 12 on-premises protocols to access local data.
• Geospatial Formats
  It supports 14 different geospatial formats for better data representation.
• Sandbox Environment
  It offers a sandbox with sample data and connectors for quick setup and testing.
• Real-Time Data Access
  Mapped allows users to quickly access real-time data from building systems and devices.
• Knowledge Graph Access
  Users can access a knowledge graph through GraphQL API, webhooks, and connectors.
• Data Pipeline Management
  Mapped helps manage the entire data pipeline for various industries like retail and healthcare.
• Data Tagging and Mapping
  The software automatically tags and maps raw data into standard formats using AI.
• Cloud Sensor Integrations
  Mapped connects with over 90 cloud sensors to gather data from various sources.
• Data Discovery Automation
  It automates the process of discovering and extracting data, making it easier for users.
• Machine Learning Integration
  Mapped uses machine learning to simplify data integration and create a user-friendly data layer.
• Focus on Application Development
  Developers can spend more time on building applications instead of data integration.

Odznaki

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