With intdash provided by aptpod, devices generating large volumes of data rapidly—such as automobiles, robots, industrial machinery, and control/visualization systems—can be seamlessly connected via mobile networks or the internet. Data streamed through intdash is directly stored in a time-series database＊, enabling real-time visualization in Visual M2M and facilitating computational processing and machine learning in Analytics Services.

＊A time-series database is a specialized database designed to store and manage time-stamped data, such as logs or sensor readings. It efficiently handles large volumes of data that are indexed by time, making it ideal for tracking changes over time, such as stock prices or temperature readings.

intdash is an interactive data transmission platform tailored for high-frequency time-series data. It supports high-speed, large-capacity, and stable data streaming through mobile networks and other channels. Data streamed via intdash is directly stored in a time-series database, facilitating real-time visualization and enabling advanced machine learning applications.

1. Real-Time Transmission of Large-Volume Data

• Developed with the proprietary iSCP (intdash Streaming Control Protocol) for efficient, low-latency transmission of large volumes of time-series data (patented).
• Supports the transmission of sensor data, media (video and audio), and fusion data over mobile networks or the internet, handling thousands to tens of thousands of data points per second.
• Facilitates server-based remote control, monitoring, diagnostics, and measurement.

2. High Scalability

• Provides flexible deployment through microservices, enabling linear scalability by adjusting configurations based on data characteristics and operational requirements.
• Utilizes custom-developed middleware for high-speed load-balancing, handling vast amounts of time-series data.
• Capable of collecting and transmitting hundreds of thousands of data points per second and supporting large-scale operations with thousands of simultaneous accesses.

3. Data Analysis Processing and Machine Learning Pipeline

• Applies scientific and technical computing processes to real-time and accumulated data.
• Enables the creation of pipelines for machine learning and AI development, including data collection, training data preparation, and model development and operation.

Visual M2M Data Visualizer

intdash is a web-based dashboard application designed for versatile visualization of time-series data. It supports both real-time streaming data from the intdash data pipeline and historical measurement data stored on the platform. Users can effortlessly create and customize dashboards through intuitive drag-and-drop operations, allowing for easy configuration and visualization with minimal effort.