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| # Timeseries Data Model |
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| ## 1. What is Time Series Data? |
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| In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example: |
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| - **Motors** record voltage and current. |
| - **Wind Turbines** track blade speed, angular velocity, and power output. |
| - **Vehicles** capture GPS coordinates, speed, and fuel consumption. |
| - **Bridges** monitor vibration frequency, deflection, and displacement. |
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| Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**. |
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| Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device. |
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| Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**. |
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| ## 2. Key Concepts in Time Series Data |
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| Several fundamental concepts define time-series data: |
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| | **Device** | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:<br>- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.<br>- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.<br>- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).<br>- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. | |
| | ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | **FIELD** | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:<br>- Energy and power: Current, voltage, wind speed, rotational speed.<br>- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.<br>- Manufacturing: Temperature, humidity. | |
| | **Data Point** | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32. <br>In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.<br> <img src="/img/time-series-data-en-03.png" alt="" style="width: 70%;"/> | |
| | **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.<br>For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second). | |
| | **TTL** | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.<br>IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:<br>- Manage disk space efficiently, preventing storage overflow.<br>- Maintain high query performance.<br>- Reduce memory resource consumption. | |
