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updated DataQualityAssessment/doc/spec_ZH.md
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albertoabellagarcia committed May 21, 2024
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28 changes: 19 additions & 9 deletions DataQualityAssessment/doc/spec_ZH.md
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<!-- /15-License -->
<!-- 20-Description -->
全局描述:**该实体描述测量数据(如温度)的数据质量属性。
版本: 0.0.2
版本: 0.0.4
<!-- /20-Description -->
<!-- 30-PropertiesList -->

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- `postalCode[string]`: 邮政编码。例如:24004 . Model: [https://schema.org/https://schema.org/postalCode](https://schema.org/https://schema.org/postalCode)
- `streetAddress[string]`: 街道地址 . Model: [https://schema.org/streetAddress](https://schema.org/streetAddress)
- `streetNr[string]`: 标识公共街道上特定房产的编号
- `alternateName[string]`: 该项目的替代名称 - `areaServed[string]`: 提供服务或提供物品的地理区域 . Model: [https://schema.org/Text](https://schema.org/Text)- `completeness[number]`: 完整性量化了特定时间窗口内遗漏的测量或观测数据的数量 - `dataProvider[string]`: 标识统一数据实体提供者的字符序列 - `dateCalculated[date-time]`: 用户定义的计算实体的日期 - `dateCreated[date-time]`: 实体创建时间戳。通常由存储平台分配 - `dateModified[date-time]`: 实体最后一次修改的时间戳。通常由存储平台分配 - `description[string]`: 项目描述 - `id[*]`: 实体的唯一标识符 - `location[*]`: 项目的 Geojson 引用。它可以是点、线条字符串、多边形、多点、多线条字符串或多多边形 - `name[string]`: 该项目的名称 - `outlier[object]`: 包括测量的离群特征信息 - `isOutlier[boolean]`: 确定测量结果是否被视为离群值
- `alternateName[string]`: 该项目的替代名称 - `areaServed[string]`: 提供服务或提供物品的地理区域 . Model: [https://schema.org/Text](https://schema.org/Text)- `completeness[number]`: 完整性量化了特定时间窗口内遗漏的测量或观测数据的数量 - `dataProvider[string]`: 标识统一数据实体提供者的字符序列 - `dateCalculated[date-time]`: 用户定义的计算实体的日期 - `dateCreated[date-time]`: 实体创建时间戳。通常由存储平台分配 - `dateModified[date-time]`: 实体最后一次修改的时间戳。通常由存储平台分配 - `description[string]`: 项目描述 - `duplicate[object]`: 包括有关重复数据的信息 - `foundMatches[array]`: 如果是重复的,请确定测量值是否与以下内容相匹配
- `isDuplicate[boolean]`: 确定测量是否重复
- `id[*]`: 实体的唯一标识符 - `location[*]`: 项目的 Geojson 引用。它可以是点、线条字符串、多边形、多点、多线条字符串或多多边形 - `name[string]`: 该项目的名称 - `outlier[object]`: 包括测量的离群特征信息 - `isOutlier[boolean]`: 确定测量结果是否被视为离群值
- `methodology[*]`: 提及其他实体,包括人工智能方法信息
- `outlierScore[number]`: 表示离群程度的分数
- `owner[array]`: 包含一个 JSON 编码字符序列的列表,其中引用了所有者的唯一 Ids - `precision[number]`: 精度衡量数据集的标准偏差。也就是说,它衡量数据集中各值之间的接近程度 - `seeAlso[*]`: 指向有关该项目的其他资源的 uri 列表 - `source[string]`: 以 URL 形式给出实体数据原始来源的字符串。建议使用源提供者的完全合格域名或源对象的 URL - `synthetic[object]`: 包括测量来源的信息 - `isSynthetic[boolean]`: 确定测量结果是否是合成的
- `methodology[*]`: 提及其他实体,包括人工智能方法信息
- `timeliness[number]`: 数据流的平均及时性 - `type[string]`: NGSI 实体类型。必须是 DataQualityAssessment <!-- /30-PropertiesList -->
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description: Includes information about duplicated data
properties:
foundMatches:
description: 'Property: If this is a duplicate, determine measurements it does match with'
description: 'If this is a duplicate, determine measurements it does match with'
items:
description: Every match found in the data
type: string
x-ngsi:
type: Property
type: array
x-ngsi:
type: Property
isDuplicate:
description: 'Property: Determine whether the measurement is duplicated or not'
description: Determine whether the measurement is duplicated or not
type: boolean
x-ngsi:
type: Property
type: object
x-ngsi:
type: Property
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- type
type: object
x-derived-from: ""
x-disclaimer: 'Redistribution and use in source and binary forms, with or without modification, are permitted provided that the license conditions are met. Copyleft (c) 2023 Contributors to Smart Data Models Program'
x-disclaimer: 'Redistribution and use in source and binary forms, with or without modification, are permitted provided that the license conditions are met. Copyleft (c) 2024 Contributors to Smart Data Models Program'
x-license-url: https://github.com/smart-data-models/dataModel.DataQuality/blob/master/DataQualityAssessment/LICENSE.md
x-model-schema: https://smart-data-models.github.io/dataModel.DataQuality/DataQualityAssessment/schema.json
x-model-tags: ""
x-version: 0.0.3
x-version: 0.0.4
```
</details>
<!-- /60-ModelYaml -->
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"source": "https://salted-project.eu",
"outlier": {
"isOutlier": true,
"outlierScore": 0.7
"outlierScore": 0.7,
"methodology": "urn:ngsi-ld:AI-Methodology:Outlier:Temperature:smartsantander:u7jcfa:f3058"
},
"duplicate": {
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"type": "StructuredValue",
"value": {
"isOutlier": true,
"outlierScore": 0.7
"outlierScore": 0.7,
"methodology": "urn:ngsi-ld:AI-Methodology:Outlier:Temperature:smartsantander:u7jcfa:f3058"
}
},
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"isDuplicate": {
"type": "Property",
"value": true
}
},
"foundMatches": {
"type": "Property",
"value": ["urn:ngsi-ld:DataQualityAssessment:Temperature:smartsantander:u7jcfa:f3062"
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