| import{_ as t,a,b as i}from"./time-series-data-en-03-DxRzmiyh.js";import{_ as r,c as s,b as n,o}from"./app-C-fAkKj6.js";const d={};function l(c,e){return o(),s("div",null,e[0]||(e[0]=[n('<h1 id="timeseries-data-model" tabindex="-1"><a class="header-anchor" href="#timeseries-data-model"><span>Timeseries Data Model</span></a></h1><h2 id="_1-what-is-time-series-data" tabindex="-1"><a class="header-anchor" href="#_1-what-is-time-series-data"><span>1. What is Time Series Data?</span></a></h2><p>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:</p><ul><li><strong>Motors</strong> record voltage and current.</li><li><strong>Wind Turbines</strong> track blade speed, angular velocity, and power output.</li><li><strong>Vehicles</strong> capture GPS coordinates, speed, and fuel consumption.</li><li><strong>Bridges</strong> monitor vibration frequency, deflection, and displacement.</li></ul><p>Sensor data collection has permeated almost every industry, generating vast amounts of <strong>time series data</strong>.</p><figure><img src="'+t+'" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><p>Each data collection point is referred to as a <strong>measurement point</strong> (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 <strong>time series</strong>. In tabular form, a time series consists of two columns: <strong>timestamp</strong> and <strong>value</strong>. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device.</p><figure><img src="'+a+'" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><p>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 <strong>devices</strong> and <strong>sensors</strong>.</p><h2 id="_2-key-concepts-in-time-series-data" tabindex="-1"><a class="header-anchor" href="#_2-key-concepts-in-time-series-data"><span>2. Key Concepts in Time Series Data</span></a></h2><p>Several fundamental concepts define time-series data:</p><table><thead><tr><th><strong>Device</strong></th><th>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.</th></tr></thead><tbody><tr><td><strong>FIELD</strong></td><td>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.</td></tr><tr><td><strong>Data Point</strong></td><td>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="'+i+'" alt="" style="width:70%;"></td></tr><tr><td><strong>Frequency</strong></td><td>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).</td></tr><tr><td><strong>TTL</strong></td><td>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.</td></tr></tbody></table>',12)]))}const g=r(d,[["render",l],["__file","Navigating_Time_Series_Data.html.vue"]]),u=JSON.parse(`{"path":"/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.html","title":"Timeseries Data Model","lang":"en-US","frontmatter":{"description":"Timeseries Data Model 1. What is Time Series Data? 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