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第394行： − 与上面概述的一般消息传递相反，用户可能对数据集中的特定数据点感兴趣。下表介绍了这种低层次的用户分析活动。分类还可以由三个活动极点组织: 检索值、查找数据点和排列数据点。+ 第406行：
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第432行： − 给定一组特定的案例，找出这些案例的属性。+ 第436行：
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第450行： − 电影《乱世佳人》有多长？+ 第460行：
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第536行： − 波斯特谷物的平均热量是多少？+ 第540行：
第542行： − 所有商店的总收入是多少？+ 第548行：
第550行： − 有多少汽车制造商？+ 第560行：
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第625行： − 给定一组数据案例，根据某种序数度量对它们进行排序。+ 第628行：
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第685行： − 汽车的马力范围是多少？+ 第690行：
第693行： − 数据库里有哪些女演员？+ 第702行：
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第735行： − 购物者的年龄分布情况如何？+ 第744行：
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第753行： − 寻找异常点+ 第756行：
第759行： − 识别给定数据集中与给定关系或期望有关的任何异常，例如:。统计异常值。+ 第762行：
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→Analytical activities of data users
当分析师试图确定自变量 X 在多大程度上允许变量 Y 的出现时，可以使用 https://www.erim.eur.nl/centres/Necessary-condition-analysis/ '''<font color='#ff8000'>必要条件分析Necessary condition analysis(NCA)</font>'''（例如，“某个失业率(X)在多大程度上对某个通货膨胀率(Y)是必要的? ”）。 （多重）回归分析分析使用'''<font color='#ff8000'>加法逻辑additive logic</font>'''，其中每个 X 变量可以产生结果，X 之间可以相互补偿（这些X都是充分的，但不是必要的） ，然而必要条件分析使用'''<font color='#ff8000'>必要逻辑necessity logic</font>'''，其中一个或多个 X 变量允许结果的存在，但也可能不产生这个结果（它们是必要的，但不是充分的）。每个单一的必要条件都必须存在，变量之间不允许补偿。
当分析师试图确定自变量 X 在多大程度上允许变量 Y 的出现时，可以使用 https://www.erim.eur.nl/centres/Necessary-condition-analysis/ '''<font color='#ff8000'>必要条件分析Necessary condition analysis(NCA)</font>'''（例如，“某个失业率(X)在多大程度上对某个通货膨胀率(Y)是必要的? ”）。 （多重）回归分析分析使用'''<font color='#ff8000'>加法逻辑additive logic</font>'''，其中每个 X 变量可以产生结果，X 之间可以相互补偿（这些X都是充分的，但不是必要的） ，然而必要条件分析使用'''<font color='#ff8000'>必要逻辑necessity logic</font>'''，其中一个或多个 X 变量允许结果的存在，但也可能不产生这个结果（它们是必要的，但不是充分的）。每个单一的必要条件都必须存在，变量之间不允许补偿。
==Analytical activities of data users==
==Analytical activities of data users 数据用户的分析活动==
Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.<ref>Robert Amar, James Eagan, and John Stasko (2005) [http://www.cc.gatech.edu/~stasko/papers/infovis05.pdf "Low-Level Components of Analytic Activity in Information Visualization"]</ref><ref>William Newman (1994) [http://www.mdnpress.com/wmn/pdfs/chi94-pro-formas-2.pdf "A Preliminary Analysis of the Products of HCI Research, Using Pro Forma Abstracts"]</ref><ref>Mary Shaw (2002) [https://www.cs.cmu.edu/~Compose/ftp/shaw-fin-etaps.pdf "What Makes Good Research in Software Engineering?"]</ref><ref name="ConTaaS">{{cite web|title=ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications|url=https://scholarspace.manoa.hawaii.edu/handle/10125/41879|website=ScholarSpace|publisher=HICSS50|accessdate=May 24, 2017}}</ref>
Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.<ref>Robert Amar, James Eagan, and John Stasko (2005) [http://www.cc.gatech.edu/~stasko/papers/infovis05.pdf "Low-Level Components of Analytic Activity in Information Visualization"]</ref><ref>William Newman (1994) [http://www.mdnpress.com/wmn/pdfs/chi94-pro-formas-2.pdf "A Preliminary Analysis of the Products of HCI Research, Using Pro Forma Abstracts"]</ref><ref>Mary Shaw (2002) [https://www.cs.cmu.edu/~Compose/ftp/shaw-fin-etaps.pdf "What Makes Good Research in Software Engineering?"]</ref><ref name="ConTaaS">{{cite web|title=ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications|url=https://scholarspace.manoa.hawaii.edu/handle/10125/41879|website=ScholarSpace|publisher=HICSS50|accessdate=May 24, 2017}}</ref>
Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.
Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.
与上面概述的一般的消息传递不同，用户可能对数据集当中的特定数据点感兴趣。下表介绍了这种低层次的用户分析活动。分类可以由活动的三个极来组织: '''<font color='#ff8000'>检索值retrieving values</font>'''、'''<font color='#ff8000'>查找数据点finding data points</font>'''和'''<font color='#ff8000'>排列数据点arranging data points</font>'''。
! align="center" | # !! width="160" | Task !! General<br />Description !! Pro Forma<br />Abstract !! width="35%" | Examples
! align="center" | # !! width="160" | Task !! General<br />Description !! Pro Forma<br />Abstract !! width="35%" | Examples
!对齐“中心” ！宽度160 | 任务! ！一般介绍！形式 / 摘要！宽度“35% ” | 例子
!align="center" | # !! width="160" | Task !! General<br />Description !! Pro Forma<br />Abstract !! width="35%" | Examples
|-
|-
| align="center" | 1
| align="center" | 1
| align="center" | 1
| '''Retrieve Value'''
| '''Retrieve Value'''
| Retrieve Value
| Retrieve Value
| 检索价值
| '''<font color='#ff8000'>检索值Retrieve Value</font>'''
| Given a set of specific cases, find attributes of those cases.
| Given a set of specific cases, find attributes of those cases.
| Given a set of specific cases, find attributes of those cases.
| Given a set of specific cases, find attributes of those cases.
| 给定特定的一组案例，找出这些案例的属性。
| What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?
| What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?
| What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?
| What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?
| 数据案例{ a，b，c，... }中属性{ x，y，z，... }的值是什么？
| 数据案例{A, B, C, ... }中属性{ X, Y, Z, ... }的值是什么？
| ''- What is the mileage per gallon of the Ford Mondeo?''
| ''- What is the mileage per gallon of the Ford Mondeo?''
| - What is the mileage per gallon of the Ford Mondeo?
| - What is the mileage per gallon of the Ford Mondeo?
|-福特蒙迪欧每加仑汽油行驶里程是多少？
| “-每加仑汽油在福特Mondeo车上的行驶里程是多少？”
''- How long is the movie Gone with the Wind?''
''- How long is the movie Gone with the Wind?''
- How long is the movie Gone with the Wind?
- How long is the movie Gone with the Wind?
“-电影《乱世佳人》有多长？”
|-
|-
| align="center" | 2
| align="center" | 2
| align="center" | 2
| ''' Filter'''
| ''' Filter'''
| Filter
| Filter
| '''<font color='#ff8000'>过滤Filter </font>'''
| Given some concrete conditions on attribute values, find data cases satisfying those conditions.
| Given some concrete conditions on attribute values, find data cases satisfying those conditions.
| Given some concrete conditions on attribute values, find data cases satisfying those conditions.
| Given some concrete conditions on attribute values, find data cases satisfying those conditions.
给定属性值的一些具体条件，找出满足这些条件的数据案例。
| 给定属性值的一些具体条件，找出满足这些条件的数据案例。
| Which data cases satisfy conditions {A, B, C...}?
| Which data cases satisfy conditions {A, B, C...}?
| Which data cases satisfy conditions {A, B, C...}?
| Which data cases satisfy conditions {A, B, C...}?
| 哪些数据案例满足条件{ a，b，c. . . } ？
| 哪些数据案例满足条件{A, B, C... } ？
| ''- What Kellogg's cereals have high fiber?''
| ''- What Kellogg's cereals have high fiber?''
| - What Kellogg's cereals have high fiber?
| - What Kellogg's cereals have high fiber?
|-什么家乐氏麦片含有高纤维？
|-“什么家乐氏麦片含有高纤维？（注：Kellogg‘s （家乐氏） 公司为全球知名谷物早餐和零食制造商。）”
''- What comedies have won awards?''
''- What comedies have won awards?''
- What comedies have won awards?
- What comedies have won awards?
- 哪些喜剧获奖？
- “哪些喜剧获了奖？”
- Which funds underperformed the SP-500?
- Which funds underperformed the SP-500?
“-哪些基金的表现不如 SP-500？”
|-
|-
| align="center" | 3
| align="center" | 3
| align="center" | 3
| '''Compute Derived Value'''
| '''Compute Derived Value'''
| Compute Derived Value
| Compute Derived Value
| 计算派生值
| '''<font color='#ff8000'>计算派生值Compute Derived Value</font>'''
| Given a set of data cases, compute an aggregate numeric representation of those data cases.
| Given a set of data cases, compute an aggregate numeric representation of those data cases.
| Given a set of data cases, compute an aggregate numeric representation of those data cases.
| Given a set of data cases, compute an aggregate numeric representation of those data cases.
| 给定一组数据用例，计算这些数据用例的聚合数值表示形式。
| 给定一组数据案例，计算这些数据案例以聚合形式表示的数值。
| What is the value of aggregation function F over a given set S of data cases?
| What is the value of aggregation function F over a given set S of data cases?
| What is the value of aggregation function F over a given set S of data cases?
| What is the value of aggregation function F over a given set S of data cases?
| 聚合函数 f 在给定数据集 s 上的值是多少？
| '''<font color='#ff8000'>聚合函数aggregation function </font>'''F 在给定数据集 S 上的值是多少？
| ''- What is the average calorie content of Post cereals?''
| ''- What is the average calorie content of Post cereals?''
| - What is the average calorie content of Post cereals?
| - What is the average calorie content of Post cereals?
| “-'''<font color='#ff8000'>波斯特谷物Post cereals</font>'''的平均热量是多少？”
''- What is the gross income of all stores combined?''
''- What is the gross income of all stores combined?''
- What is the gross income of all stores combined?
- What is the gross income of all stores combined?
“-所有商店的总收入是多少？”
- How many manufacturers of cars are there?
- How many manufacturers of cars are there?
“-有多少汽车制造商？”
|-
|-
| align="center" | 4
| align="center" | 4
| align="center" | 4
| '''Find Extremum'''
| '''Find Extremum'''
| Find Extremum
| Find Extremum
| 寻找极端情况
| '''<font color='#ff8000'>寻找极值Find Extremum</font>'''
| Find data cases possessing an extreme value of an attribute over its range within the data set.
| Find data cases possessing an extreme value of an attribute over its range within the data set.
| Find data cases possessing an extreme value of an attribute over its range within the data set.
| Find data cases possessing an extreme value of an attribute over its range within the data set.
| 查找数据集中具有属性在其范围内的极值的数据案例。
| 查找数据集中在某属性的某范围内具有极端取值的数据案例。
| What are the top/bottom N data cases with respect to attribute A?
| What are the top/bottom N data cases with respect to attribute A?
| What are the top/bottom N data cases with respect to attribute A?
| What are the top/bottom N data cases with respect to attribute A?
| 关于属性 a 的顶部 / 底部 n 个数据用例是什么？
| 属性 A 的顶部或底部的 N 个数据案例是什么？
| ''- What is the car with the highest MPG?''
| ''- What is the car with the highest MPG?''
| - What is the car with the highest MPG?
| - What is the car with the highest MPG?
|“- 有最高 MPG 的汽车是什么？”
''- What director/film has won the most awards?''
''- What director/film has won the most awards?''
- What director/film has won the most awards?
- What director/film has won the most awards?
- 哪部导演 / 电影获奖最多？
“- 哪个导演/哪部电影获奖最多？”
- What Marvel Studios film has the most recent release date?
- What Marvel Studios film has the most recent release date?
- 漫威电影公司的哪部电影最近上映日期？
“- 哪部漫威电影公司的电影具有最近上映的日期？”
|-
|-
| align="center" | 5
| align="center" | 5
| align="center" | 5
| '''Sort'''
| '''Sort'''
| Sort
| Sort
| 排序
| '''<font color='#ff8000'>排序Sort</font>'''
| Given a set of data cases, rank them according to some ordinal metric.
| Given a set of data cases, rank them according to some ordinal metric.
| Given a set of data cases, rank them according to some ordinal metric.
| Given a set of data cases, rank them according to some ordinal metric.
| 给定一组数据案例，根据某种顺序度量对它们进行排序。
| What is the sorted order of a set S of data cases according to their value of attribute A?
| What is the sorted order of a set S of data cases according to their value of attribute A?
| What is the sorted order of a set S of data cases according to their value of attribute A?
| What is the sorted order of a set S of data cases according to their value of attribute A?
| 根据属性 a 的值，一组数据案例的排序顺序是什么？
| 根据属性 A 的值，一组数据案例 S 怎样排序？
| ''- Order the cars by weight.''
| ''- Order the cars by weight.''
| - Order the cars by weight.
| - Order the cars by weight.
|-按重量订购汽车。
| “-按重量给汽车排序。”
''- Rank the cereals by calories.''
''- Rank the cereals by calories.''
- Rank the cereals by calories.
- Rank the cereals by calories.
- 按卡路里排列谷物。
“- 按卡路里排列谷物。”
|-
|-
| align="center" | 6
| align="center" | 6
| align="center" | 6
| '''Determine Range'''
| '''Determine Range'''
| Determine Range
| Determine Range
| 确定范围
| '''<font color='#ff8000'>确定范围Determine Range</font>'''
| Given a set of data cases and an attribute of interest, find the span of values within the set.
| Given a set of data cases and an attribute of interest, find the span of values within the set.
| Given a set of data cases and an attribute of interest, find the span of values within the set.
| Given a set of data cases and an attribute of interest, find the span of values within the set.
| 给定一组数据用例和一个感兴趣的属性，查找该组中的值的范围。
| 给定一组数据案例和一个感兴趣的属性，查找该组中的值的范围。
| What is the range of values of attribute A in a set S of data cases?
| What is the range of values of attribute A in a set S of data cases?
| What is the range of values of attribute A in a set S of data cases?
| What is the range of values of attribute A in a set S of data cases?
| 在一组数据案例中，属性 a 的值范围是多少？
| 在一组数据案例 S 中，属性 A 的值范围是多少？
| ''- What is the range of film lengths?''
| ''- What is the range of film lengths?''
| - What is the range of film lengths?
| - What is the range of film lengths?
|-胶卷的长度范围是多少？
| “-胶卷的长度范围是多少？”
''- What is the range of car horsepowers?''
''- What is the range of car horsepowers?''
- What is the range of car horsepowers?
- What is the range of car horsepowers?
“-汽车的马力范围是多少？”
- What actresses are in the data set?
- What actresses are in the data set?
“-数据库里有哪些女演员？”
|-
|-
| align="center" | 7
| align="center" | 7
| align="center" | 7
| '''Characterize Distribution'''
| '''Characterize Distribution'''
| Characterize Distribution
| Characterize Distribution
| 特征分布
| '''<font color='#ff8000'>特征分布Characterize Distribution</font>'''
| Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.
| Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.
| Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.
| Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.
| 给定一组数据用例和一个感兴趣的数量属性，刻画该属性值在该集上的分布情况。
| 给定一组数据案例和一个感兴趣的定量属性，刻画该属性值在该集上的分布情况。
| What is the distribution of values of attribute A in a set S of data cases?
| What is the distribution of values of attribute A in a set S of data cases?
| What is the distribution of values of attribute A in a set S of data cases?
| What is the distribution of values of attribute A in a set S of data cases?
| 属性 a 的值在一组数据案例中的分布情况如何？
| 属性 A 的值在一组数据案例 S 中的分布情况如何？
| ''- What is the distribution of carbohydrates in cereals?''
| ''- What is the distribution of carbohydrates in cereals?''
| - What is the distribution of carbohydrates in cereals?
| - What is the distribution of carbohydrates in cereals?
|-谷物中碳水化合物的分布情况如何？
|“-谷物中碳水化合物的分布情况如何？”
''- What is the age distribution of shoppers?''
''- What is the age distribution of shoppers?''
- What is the age distribution of shoppers?
- What is the age distribution of shoppers?
“-购物者的年龄分布情况如何？”
|-
|-
| align="center" | 8
| align="center" | 8
| align="center" | 8
| '''Find Anomalies'''
| '''Find Anomalies'''
| Find Anomalies
| Find Anomalies
| '''<font color='#ff8000'>寻找异常值Find Anomalies</font>'''
| Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.
| Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.
| Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.
| Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.
| 识别给定数据集中与给定关系或期望有关的任何异常值，例如统计异常值。
| Which data cases in a set S of data cases have unexpected/exceptional values?
| Which data cases in a set S of data cases have unexpected/exceptional values?
| Which data cases in a set S of data cases have unexpected/exceptional values?
| Which data cases in a set S of data cases have unexpected/exceptional values?
| 在一组数据情况中，哪些数据情况具有意外 / 异常值？
| 在一组数据案例中，哪些数据案例具有意外的或异常的取值？
| ''- Are there exceptions to the relationship between horsepower and acceleration?''
| ''- Are there exceptions to the relationship between horsepower and acceleration?''
| - Are there exceptions to the relationship between horsepower and acceleration?
| - Are there exceptions to the relationship between horsepower and acceleration?
|-马力和加速度之间的关系有例外吗？
|“-马力和加速度之间的关系有例外吗？”
''- Are there any outliers in protein?''
''- Are there any outliers in protein?''
- Are there any outliers in protein?
- Are there any outliers in protein?
“-蛋白质是否有异常值？”
|-
|-
| align="center" | 9
| align="center" | 9
| align="center" | 9
| '''Cluster'''
| '''Cluster'''
| Cluster
| Cluster
| Cluster
| '''<font color='#ff8000'>Cluster 集群<font>'''
| Given a set of data cases, find clusters of similar attribute values.
| Given a set of data cases, find clusters of similar attribute values.
| Given a set of data cases, find clusters of similar attribute values.
| Given a set of data cases, find clusters of similar attribute values.
| 给定一组数据用例，找出相似属性值的集群。
| 给定一组数据案例，找出相似属性值的集群。
| Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?
| Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?
| Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?
| Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?
| 一组数据用例中的哪些数据用例在属性{ x，y，z，... }的值上相似？
| 一组数据案例中的哪些数据案例在属性{ X, Y, Z, ... }的值上相似？
| ''- Are there groups of cereals w/ similar fat/calories/sugar?''
| ''- Are there groups of cereals w/ similar fat/calories/sugar?''
| - Are there groups of cereals w/ similar fat/calories/sugar?
| - Are there groups of cereals w/ similar fat/calories/sugar?
|-有没有类似脂肪 / 卡路里 / 糖的谷类食物？
|“-有没有含脂肪 / 卡路里 / 糖类似的谷类食物？”
''- Is there a cluster of typical film lengths?''
''- Is there a cluster of typical film lengths?''
- Is there a cluster of typical film lengths?
- Is there a cluster of typical film lengths?
“-是否有一组典型的电影长度？”
|-
|-
| align="center" | 10
| align="center" | 10
| align="center" | 10
| '''Correlate'''
| '''Correlate'''
| Correlate
| Correlate
| Correlate
| '''<font color='#ff8000'>相关Correlate</font>'''
| Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.
| Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.
| Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.
| Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.
| 给定一组数据用例和两个属性，确定这些属性值之间的有用关系。
| 给定一组数据案例和两个属性，确定这些属性值之间的有用关系。
| What is the correlation between attributes X and Y over a given set S of data cases?
| What is the correlation between attributes X and Y over a given set S of data cases?
| What is the correlation between attributes X and Y over a given set S of data cases?
| What is the correlation between attributes X and Y over a given set S of data cases?
| 在给定的数据案例集 s 中，属性 x 和 y 之间的相关性是什么？
| 在给定的数据案例集 S 中，属性 X 和 Y 之间的相关性是什么？
| ''- Is there a correlation between carbohydrates and fat?''
| ''- Is there a correlation between carbohydrates and fat?''
| - Is there a correlation between carbohydrates and fat?
| - Is there a correlation between carbohydrates and fat?
|-碳水化合物和脂肪之间有关系吗？
|“-碳水化合物和脂肪之间有关系吗？”
''- Is there a correlation between country of origin and MPG?''
''- Is there a correlation between country of origin and MPG?''
- Is there a correlation between country of origin and MPG?
- Is there a correlation between country of origin and MPG?
起源国和 MPG 之间有联系吗？
“-起源国和 MPG 之间有联系吗？”
- Do different genders have a preferred payment method?
- Do different genders have a preferred payment method?
- 不同性别是否有首选的付款方式？
“- 不同性别是否倾向不同的付款方式？”
- Is there a trend of increasing film length over the years?
- Is there a trend of increasing film length over the years?
- 电影长度是否有逐年增加的趋势？
“- 电影长度是否有逐年增加的趋势？”
|-
|-
| align="center" | 11
| align="center" | 11
| align="center" | 11
| ''' [[Contextualization (computer science)|Contextualization]]<ref name="ConTaaS"/>'''
| ''' [[Contextualization (computer science)|Contextualization]]<ref name="ConTaaS"/>'''
| Contextualization
| Contextualization
| 语境化
| '''<font color='#ff8000'>语境化Contextualization</font>'''
| Given a set of data cases, find contextual relevancy of the data to the users.
| Given a set of data cases, find contextual relevancy of the data to the users.
| Given a set of data cases, find contextual relevancy of the data to the users.
| Given a set of data cases, find contextual relevancy of the data to the users.
| 给定一组数据案例，找出数据与用户上下文的相关性。
| 给定一组数据案例，找出数据与用户语境化的相关性。
| Which data cases in a set S of data cases are relevant to the current users' context?
| Which data cases in a set S of data cases are relevant to the current users' context?
| Which data cases in a set S of data cases are relevant to the current users' context?
| Which data cases in a set S of data cases are relevant to the current users' context?
| 一组数据用例中的哪些数据用例与当前用户的上下文相关？
| 一组数据案例中的哪些数据案例与当前用户的语境相关？
| ''- Are there groups of restaurants that have foods based on my current caloric intake?''
| ''- Are there groups of restaurants that have foods based on my current caloric intake?''
| - Are there groups of restaurants that have foods based on my current caloric intake?
| - Are there groups of restaurants that have foods based on my current caloric intake?
|-是否有几组餐馆根据我目前摄入的热量来提供食物？
|“-是否有几组餐馆根据我目前摄入的热量来提供食物？”
|-
|-
|}
|}
==Barriers to effective analysis==
==Barriers to effective analysis==