| import{_ as n,o as e,c as s,a as i,f as a}from"./app-rEULWpiv.js";const d={},l=a(`<h1 id="数据匹配" tabindex="-1"><a class="header-anchor" href="#数据匹配" aria-hidden="true">#</a> 数据匹配</h1><h2 id="cov" tabindex="-1"><a class="header-anchor" href="#cov" aria-hidden="true">#</a> Cov</h2><h3 id="函数简介" tabindex="-1"><a class="header-anchor" href="#函数简介" aria-hidden="true">#</a> 函数简介</h3><p>本函数用于计算两列数值型数据的总体协方差。</p><p><strong>函数名：</strong> COV</p><p><strong>输入序列：</strong> 仅支持两个输入序列，类型均为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>输出序列：</strong> 输出单个序列，类型为 DOUBLE。序列仅包含一个时间戳为 0、值为总体协方差的数据点。</p><p><strong>提示：</strong></p><ul><li>如果某行数据中包含空值、缺失值或<code>NaN</code>，该行数据将会被忽略；</li><li>如果数据中所有的行都被忽略，函数将会输出<code>NaN</code>。</li></ul><h3 id="使用示例" tabindex="-1"><a class="header-anchor" href="#使用示例" aria-hidden="true">#</a> 使用示例</h3><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+---------------+ |
| | Time|root.test.d2.s1|root.test.d2.s2| |
| +-----------------------------+---------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| 101.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| null| |
| |2020-01-01T00:00:04.000+08:00| 102.0| 101.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| 102.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| 102.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| 103.0| |
| |2020-01-01T00:00:12.000+08:00| null| 103.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| 104.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| null| |
| |2020-01-01T00:00:16.000+08:00| 114.0| 104.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| 105.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| 105.0| |
| |2020-01-01T00:00:22.000+08:00| 100.0| 106.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| 108.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| 108.0| |
| |2020-01-01T00:00:30.000+08:00| NaN| 108.0| |
| +-----------------------------+---------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> cov<span class="token punctuation">(</span>s1<span class="token punctuation">,</span>s2<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------+ |
| | Time|cov(root.test.d2.s1, root.test.d2.s2)| |
| +-----------------------------+-------------------------------------+ |
| |1970-01-01T08:00:00.000+08:00| 12.291666666666666| |
| +-----------------------------+-------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h2 id="dtw" tabindex="-1"><a class="header-anchor" href="#dtw" aria-hidden="true">#</a> Dtw</h2><h3 id="函数简介-1" tabindex="-1"><a class="header-anchor" href="#函数简介-1" aria-hidden="true">#</a> 函数简介</h3><p>本函数用于计算两列数值型数据的 DTW 距离。</p><p><strong>函数名：</strong> DTW</p><p><strong>输入序列：</strong> 仅支持两个输入序列，类型均为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>输出序列：</strong> 输出单个序列，类型为 DOUBLE。序列仅包含一个时间戳为 0、值为两个时间序列的 DTW 距离值。</p><p><strong>提示：</strong></p><ul><li>如果某行数据中包含空值、缺失值或<code>NaN</code>，该行数据将会被忽略；</li><li>如果数据中所有的行都被忽略，函数将会输出 0。</li></ul><h3 id="使用示例-1" tabindex="-1"><a class="header-anchor" href="#使用示例-1" aria-hidden="true">#</a> 使用示例</h3><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+---------------+ |
| | Time|root.test.d2.s1|root.test.d2.s2| |
| +-----------------------------+---------------+---------------+ |
| |1970-01-01T08:00:00.001+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.002+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.003+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.004+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.005+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.006+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.007+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.008+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.009+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.010+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.011+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.012+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.013+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.014+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.015+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.016+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.017+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.018+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.019+08:00| 1.0| 2.0| |
| |1970-01-01T08:00:00.020+08:00| 1.0| 2.0| |
| +-----------------------------+---------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> dtw<span class="token punctuation">(</span>s1<span class="token punctuation">,</span>s2<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------+ |
| | Time|dtw(root.test.d2.s1, root.test.d2.s2)| |
| +-----------------------------+-------------------------------------+ |
| |1970-01-01T08:00:00.000+08:00| 20.0| |
| +-----------------------------+-------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h2 id="pearson" tabindex="-1"><a class="header-anchor" href="#pearson" aria-hidden="true">#</a> Pearson</h2><h3 id="函数简介-2" tabindex="-1"><a class="header-anchor" href="#函数简介-2" aria-hidden="true">#</a> 函数简介</h3><p>本函数用于计算两列数值型数据的皮尔森相关系数。</p><p><strong>函数名：</strong> PEARSON</p><p><strong>输入序列：</strong> 仅支持两个输入序列，类型均为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>输出序列：</strong> 输出单个序列，类型为 DOUBLE。序列仅包含一个时间戳为 0、值为皮尔森相关系数的数据点。</p><p><strong>提示：</strong></p><ul><li>如果某行数据中包含空值、缺失值或<code>NaN</code>，该行数据将会被忽略；</li><li>如果数据中所有的行都被忽略，函数将会输出<code>NaN</code>。</li></ul><h3 id="使用示例-2" tabindex="-1"><a class="header-anchor" href="#使用示例-2" aria-hidden="true">#</a> 使用示例</h3><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+---------------+ |
| | Time|root.test.d2.s1|root.test.d2.s2| |
| +-----------------------------+---------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| 101.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| null| |
| |2020-01-01T00:00:04.000+08:00| 102.0| 101.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| 102.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| 102.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| 103.0| |
| |2020-01-01T00:00:12.000+08:00| null| 103.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| 104.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| null| |
| |2020-01-01T00:00:16.000+08:00| 114.0| 104.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| 105.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| 105.0| |
| |2020-01-01T00:00:22.000+08:00| 100.0| 106.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| 108.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| 108.0| |
| |2020-01-01T00:00:30.000+08:00| NaN| 108.0| |
| +-----------------------------+---------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> pearson<span class="token punctuation">(</span>s1<span class="token punctuation">,</span>s2<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-----------------------------------------+ |
| | Time|pearson(root.test.d2.s1, root.test.d2.s2)| |
| +-----------------------------+-----------------------------------------+ |
| |1970-01-01T08:00:00.000+08:00| 0.5630881927754872| |
| +-----------------------------+-----------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h2 id="ptnsym" tabindex="-1"><a class="header-anchor" href="#ptnsym" aria-hidden="true">#</a> PtnSym</h2><h3 id="函数简介-3" tabindex="-1"><a class="header-anchor" href="#函数简介-3" aria-hidden="true">#</a> 函数简介</h3><p>本函数用于寻找序列中所有对称度小于阈值的对称子序列。对称度通过 DTW 计算，值越小代表序列对称性越高。</p><p><strong>函数名：</strong> PTNSYM</p><p><strong>输入序列：</strong> 仅支持一个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>参数：</strong></p><ul><li><code>window</code>：对称子序列的长度，是一个正整数，默认值为 10。</li><li><code>threshold</code>：对称度阈值，是一个非负数，只有对称度小于等于该值的对称子序列才会被输出。在缺省情况下，所有的子序列都会被输出。</li></ul><p><strong>输出序列：</strong> 输出单个序列，类型为 DOUBLE。序列中的每一个数据点对应于一个对称子序列，时间戳为子序列的起始时刻，值为对称度。</p><h3 id="使用示例-3" tabindex="-1"><a class="header-anchor" href="#使用示例-3" aria-hidden="true">#</a> 使用示例</h3><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d1.s4| |
| +-----------------------------+---------------+ |
| |2021-01-01T12:00:00.000+08:00| 1.0| |
| |2021-01-01T12:00:01.000+08:00| 2.0| |
| |2021-01-01T12:00:02.000+08:00| 3.0| |
| |2021-01-01T12:00:03.000+08:00| 2.0| |
| |2021-01-01T12:00:04.000+08:00| 1.0| |
| |2021-01-01T12:00:05.000+08:00| 1.0| |
| |2021-01-01T12:00:06.000+08:00| 1.0| |
| |2021-01-01T12:00:07.000+08:00| 1.0| |
| |2021-01-01T12:00:08.000+08:00| 2.0| |
| |2021-01-01T12:00:09.000+08:00| 3.0| |
| |2021-01-01T12:00:10.000+08:00| 2.0| |
| |2021-01-01T12:00:11.000+08:00| 1.0| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> ptnsym<span class="token punctuation">(</span>s4<span class="token punctuation">,</span> <span class="token string">'window'</span><span class="token operator">=</span><span class="token string">'5'</span><span class="token punctuation">,</span> <span class="token string">'threshold'</span><span class="token operator">=</span><span class="token string">'0'</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d1 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+------------------------------------------------------+ |
| | Time|ptnsym(root.test.d1.s4, "window"="5", "threshold"="0")| |
| +-----------------------------+------------------------------------------------------+ |
| |2021-01-01T12:00:00.000+08:00| 0.0| |
| |2021-01-01T12:00:07.000+08:00| 0.0| |
| +-----------------------------+------------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h2 id="xcorr" tabindex="-1"><a class="header-anchor" href="#xcorr" aria-hidden="true">#</a> XCorr</h2><h3 id="函数简介-4" tabindex="-1"><a class="header-anchor" href="#函数简介-4" aria-hidden="true">#</a> 函数简介</h3><p>本函数用于计算两条时间序列的互相关函数值，<br> 对离散序列而言，互相关函数可以表示为<br> $$CR(n) = \\frac{1}{N} \\sum_{m=1}^N S_1[m]S_2[m+n]$$<br> 常用于表征两条序列在不同对齐条件下的相似度。</p><p><strong>函数名：</strong> XCORR</p><p><strong>输入序列：</strong> 仅支持两个输入序列，类型均为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>输出序列：</strong> 输出单个序列，类型为 DOUBLE。序列中共包含$2N-1$个数据点，<br> 其中正中心的值为两条序列按照预先对齐的结果计算的互相关系数（即等于以上公式的$CR(0)$），<br> 前半部分的值表示将后一条输入序列向前平移时计算的互相关系数，<br> 直至两条序列没有重合的数据点（不包含完全分离时的结果$CR(-N)=0.0$），<br> 后半部分类似。<br> 用公式可表示为（所有序列的索引从1开始计数）：<br> $$OS[i] = CR(-N+i) = \\frac{1}{N} \\sum_{m=1}^{i} S_1[m]S_2[N-i+m],\\ if\\ i <= N$$<br> $$OS[i] = CR(i-N) = \\frac{1}{N} \\sum_{m=1}^{2N-i} S_1[i-N+m]S_2[m],\\ if\\ i > N$$</p><p><strong>提示：</strong></p><ul><li>两条序列中的<code>null</code> 和<code>NaN</code> 值会被忽略，在计算中表现为 0。</li></ul><h3 id="使用示例-4" tabindex="-1"><a class="header-anchor" href="#使用示例-4" aria-hidden="true">#</a> 使用示例</h3><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+---------------+ |
| | Time|root.test.d1.s1|root.test.d1.s2| |
| +-----------------------------+---------------+---------------+ |
| |2020-01-01T00:00:01.000+08:00| null| 6| |
| |2020-01-01T00:00:02.000+08:00| 2| 7| |
| |2020-01-01T00:00:03.000+08:00| 3| NaN| |
| |2020-01-01T00:00:04.000+08:00| 4| 9| |
| |2020-01-01T00:00:05.000+08:00| 5| 10| |
| +-----------------------------+---------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> xcorr<span class="token punctuation">(</span>s1<span class="token punctuation">,</span> s2<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d1 <span class="token keyword">where</span> <span class="token keyword">time</span> <span class="token operator"><=</span> <span class="token number">2020</span><span class="token operator">-</span><span class="token number">01</span><span class="token operator">-</span><span class="token number">01</span> <span class="token number">00</span>:<span class="token number">00</span>:<span class="token number">05</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------------------------------+ |
| | Time|xcorr(root.test.d1.s1, root.test.d1.s2)| |
| +-----------------------------+---------------------------------------+ |
| |1970-01-01T08:00:00.001+08:00| 0.0| |
| |1970-01-01T08:00:00.002+08:00| 4.0| |
| |1970-01-01T08:00:00.003+08:00| 9.6| |
| |1970-01-01T08:00:00.004+08:00| 13.4| |
| |1970-01-01T08:00:00.005+08:00| 20.0| |
| |1970-01-01T08:00:00.006+08:00| 15.6| |
| |1970-01-01T08:00:00.007+08:00| 9.2| |
| |1970-01-01T08:00:00.008+08:00| 11.8| |
| |1970-01-01T08:00:00.009+08:00| 6.0| |
| +-----------------------------+---------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div>`,76);function r(t,c){return e(),s("div",null,[i(` |
| |
| Licensed to the Apache Software Foundation (ASF) under one |
| or more contributor license agreements. See the NOTICE file |
| distributed with this work for additional information |
| regarding copyright ownership. The ASF licenses this file |
| to you under the Apache License, Version 2.0 (the |
| "License"); you may not use this file except in compliance |
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| |
| http://www.apache.org/licenses/LICENSE-2.0 |
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| software distributed under the License is distributed on an |
| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| KIND, either express or implied. See the License for the |
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| |
| `),l])}const u=n(d,[["render",r],["__file","Data-Matching.html.vue"]]);export{u as default}; |
