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diff --git a/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md
index eed444c82..024ecef37 100644
--- a/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md
@@ -31,11 +31,11 @@ In today's interconnected world, industries such as the Internet of Things (IoT)
Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**.
-
+
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.
-
+
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**.
@@ -43,9 +43,9 @@ Given the vast amount of time-series data generated by sensors, structuring this
Several fundamental concepts define time-series data:
-| **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:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- 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:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- 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.
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.
|
-| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
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.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. |
\ No newline at end of file
+| **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:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- 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:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- 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.
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.
|
+| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
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.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. |
\ No newline at end of file
diff --git a/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md b/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md
index 6ff81da37..e365acb32 100644
--- a/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md
@@ -24,25 +24,25 @@
In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
-
+
Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
-
+
The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
## Key Concepts of Time Series Data
The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
-
+
### Data Point
- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
-
+
### Measurement Points
diff --git a/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md
index eed444c82..024ecef37 100644
--- a/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md
@@ -31,11 +31,11 @@ In today's interconnected world, industries such as the Internet of Things (IoT)
Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**.
-
+
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.
-
+
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**.
@@ -43,9 +43,9 @@ Given the vast amount of time-series data generated by sensors, structuring this
Several fundamental concepts define time-series data:
-| **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:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- 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:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- 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.
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.
|
-| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
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.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. |
\ No newline at end of file
+| **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:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- 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:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- 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.
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.
|
+| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
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.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. |
\ No newline at end of file
diff --git a/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md
index 6ff81da37..e365acb32 100644
--- a/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md
@@ -24,25 +24,25 @@
In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
-
+
Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
-
+
The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
## Key Concepts of Time Series Data
The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
-
+
### Data Point
- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
-
+
### Measurement Points