James Ellis

James Ellis


Vue3.0 background management system framework construction

The importance of vue can be imagined, learning vue3.0 in advance, there is nothing wrong with it. This article will take you to understand the knowledge points of vue3.0 construction project, antd introduction, life cycle, data two-way data binding use, method use, calculation properties, monitoring, component introduction, component value transfer, routing value transfer, etc., I am a rookie, A lot of guidance. Don’t talk nonsense, make a login, list, and details interface directly on the code through actual operations.

github source code:  https://github.com/parchments/vue3-admin-template

Effect preview: login page, home page, list page, details page



Details page

1. Project construction

  1. Not to mention installing node, it is necessary. In addition, you need to install the latest scaffolding of vue-cli 4th generation and above. You can install/update the scaffolding version by executing the following npm command
脚手架升级: npm update @vue/cli
初次安装:   npm i @vue/cli -g

Choose the plug-ins that need to be pre-installed according to your needs

vue create \[projectName\]

How do you know that you have installed vue3.0 correctly? Please refer to the main.js of vue3.0. The introduction of vue2.0 into vue is import Vue from’vue’, and vue3.0 is the introduction of deconstruction. Seeing that the introduction of deconstruction means this project is vue3 .0, good job, continue. . .

Vue3.0 global mount http, api, config

By the way, if vue3.0+ts is developed, the global mount needs to be mounted as follows

Add the code with + sign in the file main.ts

//  main.ts
import { createApp } from "vue";
import App from "./App.vue";
\+ import axios from "axios";

const app = createApp(App);
\+ app.config.globalProperties.$http = axios;
在文件 shims-vue.d.ts添加 带有 + 符号后代码

//  shims-vue.d.ts

declare module "*.vue" {
  import { defineComponent } from "vue";
  const component: ReturnType<typeof defineComponent>;
  export default component;

\+ declare module "@vue/runtime-core" {
\+  import { AxiosInstance } from "axios";
\+  interface ComponentCustomProperties {
\+    $http: AxiosInstance;
\+  }
\+ }

2. Install and introduce antd

$ npm i --save ant-design-vue@next

//If you need to import on demand, install the plug-in babel-plugin-import and then configure babel.config.js$ npm i --save babel-plugin-import

Full introduction

import { createApp } from 'vue';
import Antd from 'ant-design-vue';
import App from './App';
import 'ant-design-vue/dist/antd.css';

const app = createApp();
app.config.productionTip = false;


以上代码便完成了 Antd 的引入。需要注意的是,样式文件需要单独引入。


import { createApp } from 'vue';
import { Button, message } from 'ant-design-vue';
import App from './App';

const app = createApp();
app.config.productionTip = false;

/\* Automatically register components under Button, such as Button.Group */

app.config.globalProperties.$message = message;



module.exports = {
  presets: \["@vue/cli-plugin-babel/preset"\],
  plugins: \[
        libraryName: "ant-design-vue", 
        libraryDirectory: "es", 
        style: "css"//为true时加载的是less文件

At present, I introduce antd in a global way, and you can introduce it according to your needs.

3. Page layout

app.vue page

  <div id="app">

<style lang="less">
#app {
  text-align: center;

Login page: involves data definition, responsive data, life cycle, method invocation, instance acquisition (that is, this of vue2.x)

	<div class="box">
		<a-form class="form" layout="inline">
			<a-form-item label="账号">
				<a-input v-model:value="account" type="text" placeholder="请输入账号"/>
			<a-form-item label="密码">
				<a-input v-model:value="password" type="password" placeholder="请输入密码"/>
			<h3>输入账号密码,{{count}} 秒后登录</h3>
			<a-button @click="login()" block :disabled="account === '' || password === ''" type="primary">登录</a-button>

	//vue-cli 4.5.4创建的3.0
	// import { reactive } from '@vue/composition-api' //会提示报错 export 'default' (imported as 'Vue') was not found in 'vue'
	import { reactive, toRefs, getCurrentInstance, onBeforeMount, onMounted, onBeforeUpdate, onUpdated, onBeforeUnmount, onUnmounted, onErrorCaptured} from 'vue' //vue-cli 4.5.4 之后vue3.0集成了@vue/composition-api
	export default {
		name: 'login',
		components: {


		setup() {
			//setup(props,context)函数  默认两个属性props,context 属性和上下文   setup函数中无法访问到this   
			//创建处理响应式数据对象 类似date  需要先导入
			const state = reactive({
				count: 3,
				name: '我是响应式数据name',
				account: '',//账号
				password: ''//密码
			//Vue 3.0 中通过 getCurrentInstance 方法获取当前组件的实例,然后通过 ctx 属性获得当前上下文,ctx.$router 是 Vue Router 实例,里面包含了 currentRoute 可以获取到当前的路由信息
			const { ctx } = getCurrentInstance();
			const login = () => {
				// state.count++;

				let {account,password} = state;//对象解构
				if(account=== "" || password===""){
				// return

			onBeforeMount( () => {
			onMounted( () => {
			onBeforeUpdate( () => {
			onUpdated( () => {
				console.log('onUpdated,响应式数据变化完成 ,相当于updated')
			onBeforeUnmount( () => {
				console.log('onBeforeUnmount,实例销毁前 ,相当于beforeDestroy')
			onUnmounted( () => {
				console.log('onUnmounted,实例已销毁 ,相当于destroyed')
			onErrorCaptured( () => {
			//setup函数的返回值 导出
			return {
				//...state,  //如果没有用toRefs方法的话,是不能把reactive创建出来的数据变成响应式数据的  需要响应式就是要加上toRefs 否则不需要
<style lang="less" scoped>
		width: 50%;
		margin: 0 auto;
		padding-top: 200px;
			width: 60%;
			margin: 0 auto;
			padding-top: 30px;

It can be seen that the structure of vue2.x is very different, such as setup, method usage, etc. Setup is the entrance to Composition API. The new properties provided by Vue3.0 can use Composition API in setup. In the code, we initialized a reactive data through reactive in setup, and then returned an object through return. The object contains the declared reactive data and a method.

The setup function has two parameters, props and context, setup(props,context);

Properties passed in from outside the props component

export default {
  props: {
    title: {
      type: String
  setup(props) {

Context is an object, which contains three attributes, namely attrs, slots, emit

attrs is the same as this.$attrs of Vue2.0, that is, attributes passed in from outside that are not defined in props. For attrs and props, we cannot use es6 deconstruction for attrs, we must use attrs.name.

Slots correspond to the slots of the components, which correspond to this.$slots of Vue2.0. Like props and attrs, slots cannot be deconstructed.

Emit corresponds to this.$emit of Vue2.0, that is, the external exposure event.

home.vue page


		<Nav />
		<a-button @click="back()">退出登录</a-button>

	// import { reactive } from '@vue/composition-api' //会提示报错 export 'default' (imported as 'Vue') was not found in 'vue'
	import { reactive,toRefs,getCurrentInstance } from 'vue' //vue-cli 4.5.4 之后vue3.0集成了@vue/composition-api
	import Nav from '../../components/nav.vue'
	export default {
		name: 'home',
		components: {

		setup(props,context) {
			//函数  默认两个属性props,context 属性和上下文   setup函数中无法访问到this   
			//创建处理响应式数据对象 类似date  需要先导入
			const state = reactive({

			//Vue 3.0 中通过 getCurrentInstance 方法获取当前组件的实例,然后通过 ctx 属性获得当前上下文,ctx.$router 是 Vue Router 实例,里面包含了 currentRoute 可以获取到当前的路由信息
			const { ctx } = getCurrentInstance();
			const back =() => {
			//setup函数的返回值 导出
			return {

index->index.vue page

	<div style="padding-top: 100px;">
		<img src="../../../assets/logo.png" alt="">

	import { reactive, toRefs, getCurrentInstance } from 'vue'
	export default {
		name: 'index',
		// components: {

		// },
		setup() {
			const state = reactive({

			const { ctx } = getCurrentInstance();
			//setup函数的返回值 导出
			return {

accountList.vue list page (calculated attributes)

	<div style="margin-top: 100px;">
		<ti :title="title"></ti>
		<a-table :data-source="list" :pagination="false" style="width: 60%; margin: 0 auto 30px;">
			<a-table-column key="account" title="account" data-index="account" />
			<a-table-column key="password" title="password" data-index="password" />
			<a-table-column key="action" data-index="action">
				<template v-slot="{record}">
						<a @click="goToLink(record.id)">详情</a>
		<a-input type="text" v-model:value="leftValue" placeholder="leftValue" style="width: 100px;"/>
		<a-input type="text" v-model:value="rightValue" placeholder="rightValue" style="width: 100px;"/>
		<!\-\- <div>
			<h2>Clicked {{ count }} times</h2>
			<h2>Watch Count is {{ watchCount }}</h2>
			<button @click="increase">Click</button>
		</div> -->

	import {
	} from 'vue'
	import title from './component/title.vue'
	export default {
		name: 'accountList',
		components: {
			'ti': title
		setup(props, context) {
			console.log('propss,context', props, context)
			const state = reactive({
				title: '我是子组件的值', //子组件
				list: \[\],
				count: 3,
				leftValue: 0,
				rightValue: 0,
				resultValue: computed(() => {
					return Number(state.leftValue) + Number(state.rightValue);
			// const count1 = ref(0);
			// const watchCount = ref(0);
			// function increase () {
			//   count1.value++;
			// };
			// watch( () => count1.value,
			// 	(val) => {
			// 		watchCount.value = val;
			// 	}
			// );
			const {
			} = getCurrentInstance();
			console.log('列表实例', ctx)

			function goToLink(index) {
					path: '/accountList/detail',
					query: {
						id: index

			function getList() {
				// axios请求
				let timer1 = setInterval(() => {
					if (state.count < 1) {
				}, 1000)
				let timer2 = setTimeout(() => {
					state.list = \[{
							id: 1,
							account: 'admin',
							password: '111111'
							id: 2,
							account: 'chushi',
							password: 'chushi-111'
							id: 3,
							account: 'six',
							password: '666'
				}, 2000)

				// async getList(params) {
				// 	state.loading = true;
				// 	try{
				// 		let res = await ctx.$api.systemManage.getList(params);
				// 		if (res.data.retcode === ctx.$config.RET\_CODE.SUCCESS\_CODE) {
				// 			let data = res.data.data;
				// 			if (data.length > 0) {
				// 				state.tableData = data;
				// 				state.paginationParams.pageSize = data.pageSize; //每页的数量
				// 				state.paginationParams.size = data.size; //当前页的数量
				// 				state.paginationParams.total = data.total; //总条数
				// 				state.paginationParams.pages = data.pages; //总页码数
				// 			} else {
				// 				state.tableData = \[\];
				// 			}
				// 		} else {
				// 			ctx.$Message.error('请求成功,暂无数据');
				// 		}
				// 		state.loading = false;
				// 	}catch(e){
				// 		state.loading = false;
				// 		ctx.$Message.error('暂无数据');
				// 		console.log(e);
				// 	}
				// }
			onMounted(() => {
			//setup函数的返回值 导出
			return {

detail page (routing parameters)

		<router-link to="/accountList">返回列表</router-link>

	import { reactive, toRefs, getCurrentInstance } from 'vue'
	import { useRoute } from 'vue-router'
	export default {
		name: 'detail',
		components: {

		setup(props,context) {
			const state = reactive({
				id: ''
			const { ctx } = getCurrentInstance();//获取实例
			const { query } = useRoute();//获取路由参数
			state.id = query.id;
			//setup函数的返回值 导出
			return {

I believe everyone has a preliminary understanding of the basic usage of vue3.0. As a rookie, I am also constantly learning and updating

Official website document: https://v3.cn.vuejs.org/api/application-config.html

Download Details:

Author: parchments

Demo: https://juejin.im/post/6875124592852074503

Source Code: https://github.com/parchments/vue3-admin-template

#vue #vuejs #javascript

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Buddha Community

Vue3.0 background management system framework construction

Semantic Similarity Framework for Knowledge Graph


Sematch is an integrated framework for the development, evaluation, and application of semantic similarity for Knowledge Graphs (KGs). It is easy to use Sematch to compute semantic similarity scores of concepts, words and entities. Sematch focuses on specific knowledge-based semantic similarity metrics that rely on structural knowledge in taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents (corpus-IC and graph-IC). Knowledge-based approaches differ from their counterpart corpus-based approaches relying on co-occurrence (e.g. Pointwise Mutual Information) or distributional similarity (Latent Semantic Analysis, Word2Vec, GLOVE and etc). Knowledge-based approaches are usually used for structural KGs, while corpus-based approaches are normally applied in textual corpora.

In text analysis applications, a common pipeline is adopted in using semantic similarity from concept level, to word and sentence level. For example, word similarity is first computed based on similarity scores of WordNet concepts, and sentence similarity is computed by composing word similarity scores. Finally, document similarity could be computed by identifying important sentences, e.g. TextRank.


KG based applications also meet similar pipeline in using semantic similarity, from concept similarity (e.g. http://dbpedia.org/class/yago/Actor109765278) to entity similarity (e.g. http://dbpedia.org/resource/Madrid). Furthermore, in computing document similarity, entities are extracted and document similarity is computed by composing entity similarity scores.


In KGs, concepts usually denote ontology classes while entities refer to ontology instances. Moreover, those concepts are usually constructed into hierarchical taxonomies, such as DBpedia ontology class, thus quantifying concept similarity in KG relies on similar semantic information (e.g. path length, depth, least common subsumer, information content) and semantic similarity metrics (e.g. Path, Wu & Palmer,Li, Resnik, Lin, Jiang & Conrad and WPath). In consequence, Sematch provides an integrated framework to develop and evaluate semantic similarity metrics for concepts, words, entities and their applications.

Getting started: 20 minutes to Sematch

Install Sematch

You need to install scientific computing libraries numpy and scipy first. An example of installing them with pip is shown below.

pip install numpy scipy

Depending on different OS, you can use different ways to install them. After sucessful installation of numpy and scipy, you can install sematch with following commands.

pip install sematch
python -m sematch.download

Alternatively, you can use the development version to clone and install Sematch with setuptools. We recommend you to update your pip and setuptools.

git clone https://github.com/gsi-upm/sematch.git
cd sematch
python setup.py install

We also provide a Sematch-Demo Server. You can use it for experimenting with main functionalities or take it as an example for using Sematch to develop applications. Please check our Documentation for more details.

Computing Word Similarity

The core module of Sematch is measuring semantic similarity between concepts that are represented as concept taxonomies. Word similarity is computed based on the maximum semantic similarity of WordNet concepts. You can use Sematch to compute multi-lingual word similarity based on WordNet with various of semantic similarity metrics.

from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()

# Computing English word similarity using Li method
wns.word_similarity('dog', 'cat', 'li') # 0.449327301063
# Computing Spanish word similarity using Lin method
wns.monol_word_similarity('perro', 'gato', 'spa', 'lin') #0.876800984373
# Computing Chinese word similarity using  Wu & Palmer method
wns.monol_word_similarity('狗', '猫', 'cmn', 'wup') # 0.857142857143
# Computing Spanish and English word similarity using Resnik method
wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng', 'res') #7.91166650904
# Computing Spanish and Chinese word similarity using Jiang & Conrad method
wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn', 'jcn') #0.31023804699
# Computing Chinese and English word similarity using WPath method
wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng', 'wpath')#0.593666388463

Computing semantic similarity of YAGO concepts.

from sematch.semantic.similarity import YagoTypeSimilarity
sim = YagoTypeSimilarity()

#Measuring YAGO concept similarity through WordNet taxonomy and corpus based information content
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Actor109765278', 'wpath') #0.642
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Singer110599806', 'wpath') #0.544
#Measuring YAGO concept similarity based on graph-based IC
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Actor109765278', 'wpath_graph') #0.423
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Singer110599806', 'wpath_graph') #0.328

Computing semantic similarity of DBpedia concepts.

from sematch.semantic.graph import DBpediaDataTransform, Taxonomy
from sematch.semantic.similarity import ConceptSimilarity
concept = ConceptSimilarity(Taxonomy(DBpediaDataTransform()),'models/dbpedia_type_ic.txt')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'path')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'wup')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'li')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'res')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'lin')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'jcn')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'wpath')

Computing semantic similarity of DBpedia entities.

from sematch.semantic.similarity import EntitySimilarity
sim = EntitySimilarity()
sim.similarity('http://dbpedia.org/resource/Madrid','http://dbpedia.org/resource/Barcelona') #0.409923677282

Evaluate semantic similarity metrics with word similarity datasets

from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
evaluation = WordSimEvaluation()
wns = WordNetSimilarity()
# define similarity metrics
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
# evaluate similarity metrics with SimLex dataset
evaluation.evaluate_metric('wpath', wpath, 'noun_simlex')
# performa Steiger's Z significance Test
evaluation.statistical_test('wpath', 'path', 'noun_simlex')
# define similarity metrics for Spanish words
wpath_es = lambda x, y: wns.monol_word_similarity(x, y, 'spa', 'path')
# define cross-lingual similarity metrics for English-Spanish
wpath_en_es = lambda x, y: wns.crossl_word_similarity(x, y, 'eng', 'spa', 'wpath')
# evaluate metrics in multilingual word similarity datasets
evaluation.evaluate_metric('wpath_es', wpath_es, 'rg65_spanish')
evaluation.evaluate_metric('wpath_en_es', wpath_en_es, 'rg65_EN-ES')

Evaluate semantic similarity metrics with category classification

Although the word similarity correlation measure is the standard way to evaluate the semantic similarity metrics, it relies on human judgements over word pairs which may not have same performance in real applications. Therefore, apart from word similarity evaluation, the Sematch evaluation framework also includes a simple aspect category classification. The task classifies noun concepts such as pasta, noodle, steak, tea into their ontological parent concept FOOD, DRINKS.

from sematch.evaluation import AspectEvaluation
from sematch.application import SimClassifier, SimSVMClassifier
from sematch.semantic.similarity import WordNetSimilarity

# create aspect classification evaluation
evaluation = AspectEvaluation()
# load the dataset
X, y = evaluation.load_dataset()
# define word similarity function
wns = WordNetSimilarity()
word_sim = lambda x, y: wns.word_similarity(x, y)
# Train and evaluate metrics with unsupervised classification model
simclassifier = SimClassifier.train(zip(X,y), word_sim)
evaluation.evaluate(X,y, simclassifier)

macro averge:  (0.65319812882333839, 0.7101245049198579, 0.66317566364913016, None)
micro average:  (0.79210167952791644, 0.79210167952791644, 0.79210167952791644, None)
weighted average:  (0.80842645056024054, 0.79210167952791644, 0.79639496616636352, None)
accuracy:  0.792101679528
             precision    recall  f1-score   support

    SERVICE       0.50      0.43      0.46       519
 RESTAURANT       0.81      0.66      0.73       228
       FOOD       0.95      0.87      0.91      2256
   LOCATION       0.26      0.67      0.37        54
   AMBIENCE       0.60      0.70      0.65       597
     DRINKS       0.81      0.93      0.87       752

avg / total       0.81      0.79      0.80      4406

Matching Entities with type using SPARQL queries

You can use Sematch to download a list of entities having a specific type using different languages. Sematch will generate SPARQL queries and execute them in DBpedia Sparql Endpoint.

from sematch.application import Matcher
matcher = Matcher()
# matching scientist entities from DBpedia
matcher.match_type('científico', 'spa')
matcher.match_type('科学家', 'cmn')
matcher.match_entity_type('movies with Tom Cruise')

Example of automatically generated SPARQL query.

SELECT DISTINCT ?s, ?label, ?abstract WHERE {
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/NuclearPhysicist110364643> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Econometrician110043491> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Sociologist110620758> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Archeologist109804806> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Neurolinguist110354053> . } 
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#Thing> . 
    ?s <http://www.w3.org/2000/01/rdf-schema#label> ?label . 
    FILTER( lang(?label) = "en") . 
    ?s <http://dbpedia.org/ontology/abstract> ?abstract . 
    FILTER( lang(?abstract) = "en") .
} LIMIT 5000

Entity feature extraction with Similarity Graph

Apart from semantic matching of entities from DBpedia, you can also use Sematch to extract features of entities and apply semantic similarity analysis using graph-based ranking algorithms. Given a list of objects (concepts, words, entities), Sematch compute their pairwise semantic similarity and generate similarity graph where nodes denote objects and edges denote similarity scores. An example of using similarity graph for extracting important words from an entity description.

from sematch.semantic.graph import SimGraph
from sematch.semantic.similarity import WordNetSimilarity
from sematch.nlp import Extraction, word_process
from sematch.semantic.sparql import EntityFeatures
from collections import Counter
tom = EntityFeatures().features('http://dbpedia.org/resource/Tom_Cruise')
words = Extraction().extract_nouns(tom['abstract'])
words = word_process(words)
wns = WordNetSimilarity()
word_graph = SimGraph(words, wns.word_similarity)
word_scores = word_graph.page_rank()
words, scores =zip(*Counter(word_scores).most_common(10))
print words
(u'picture', u'action', u'number', u'film', u'post', u'sport', 
u'program', u'men', u'performance', u'motion')


Ganggao Zhu, and Carlos A. Iglesias. "Computing Semantic Similarity of Concepts in Knowledge Graphs." IEEE Transactions on Knowledge and Data Engineering 29.1 (2017): 72-85.

Oscar Araque, Ganggao Zhu, Manuel Garcia-Amado and Carlos A. Iglesias Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis, ICDM sentire, 2016.

Ganggao Zhu, and Carlos Angel Iglesias. "Sematch: Semantic Entity Search from Knowledge Graph." SumPre-HSWI@ ESWC. 2015.


You can post bug reports and feature requests in Github issues. Make sure to read our guidelines first. This project is still under active development approaching to its goals. The project is mainly maintained by Ganggao Zhu. You can contact him via gzhu [at] dit.upm.es

Why this name, Sematch and Logo?

The name of Sematch is composed based on Spanish "se" and English "match". It is also the abbreviation of semantic matching because semantic similarity metrics helps to determine semantic distance of concepts, words, entities, instead of exact matching.

The logo of Sematch is based on Chinese Yin and Yang which is written in I Ching. Somehow, it correlates to 0 and 1 in computer science.

Author: Gsi-upm
Source Code: https://github.com/gsi-upm/sematch 
License: View license

#python #jupyternotebook #graph 

Origin Scale

Origin Scale


Originscale Order Management System

Originscale order management software helps to manage all your orders across channels in a single place. Originscale collects orders across multiple channels in real-time - online, offline, D2C, B2B, and more. View all your orders in one single window and process them with a simple click.

#order management system #ordering management system #order management software #free order management software #purchase order management software #best order management software

Tech Avidus

Tech Avidus


Digital Assets Management Software Solution | AI-based Assets Management System

A Digital Asset Management System makes it easier to store, manage, and share all of your digital assets on cloud-based storage.

We help you to build Digital Asset Management (DAM) systems with your precise business requirements, whether you want one for maintaining management, production management, brand management systems, or implementing with your sales department with the digital assets it needs.

To learn more about how the Digital Asset Management system will help your business, email us at hello@techavidus.com

#digital assets management #assets management solution #digital asset management system #production management #brand management

Ortez Infotech

Ortez Infotech


PMS System | Hotel Management Software | Ortez

Best Hotel Management Software that helps hotels, hostels and resorts grow their business. Automate your operations with Ortez Hotel Solution!

Keep in touch with us👇🏻
🌎Hotel management system Dubai
📲91 94477 34981/ 91484 2428141

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#free hotel management software dubai #hotel management system software dubai #hotel property management systems dubai #property management system dubai #pms software dubai

Ruth  Nabimanya

Ruth Nabimanya


System Databases in SQL Server


In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database