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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: '''检索值retrieving values'''、'''查找数据点finding data points'''和'''排列数据点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>
 
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: '''检索值retrieving values'''、'''查找数据点finding data points'''和'''排列数据点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>
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{| class="wikitable" border="1"
      
{| class="wikitable" border="1"
 
{| class="wikitable" border="1"
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{ | 类“ wikitable”边框“1”
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! align="center" | # !! width="160" | Task !! General<br />Description !! Pro Forma<br />Abstract !! width="35%" | Examples
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! 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
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!align="center" | # !! width="160" | Task !! General<br />Description !! Pro Forma<br />Abstract !! width="35%" | Examples
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|-
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|-
 
|-
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|-
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| align="center" | 1
 
| align="center" | 1
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| align="center" | 1
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| align="center" | 1
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| '''Retrieve Value'''
 
| '''Retrieve Value'''
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| Retrieve Value
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|  '''<font color='#ff8000'>检索值Retrieve Value</font>'''
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| Given a set of specific cases, find attributes of those cases.
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| Given a set of specific cases, find attributes of those cases.
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| 给定特定的一组案例,找出这些案例的属性。
 
| 给定特定的一组案例,找出这些案例的属性。
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| What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?
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| What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?
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| 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?
 
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| “-每加仑汽油在福特Mondeo车上的行驶里程是多少?”
| ''- What is the mileage per gallon of the Ford Mondeo?''
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| - What is the mileage per gallon of the Ford Mondeo?
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“-每加仑汽油在福特Mondeo车上的行驶里程是多少?”
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''- How long is the movie Gone with the Wind?''
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- How long is the movie Gone with the Wind?
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“-电影《乱世佳人》有多长?”
 
“-电影《乱世佳人》有多长?”
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| align="center" | 2
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| ''' 过滤Filter'''
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|  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。
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|哪些数据案例满足条件{A, B, C... } ?
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| “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。)”
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“哪些喜剧获了奖?”
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“-哪些基金的表现不如 SP-500?”
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| align="center" | 3
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| '''计算派生值Compute Derived Value'''
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| 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。
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| '''<font color='#ff8000'>聚合函数aggregation function </font>'''F 在给定数据集 S 上的值是多少?
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|  “-'''<font color='#ff8000'>波斯特谷物Post cereals</font>'''的平均热量是多少?”
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“-所有商店的总收入是多少?”
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“-有多少汽车制造商?”
 
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|-
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| align="center" | 4
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| '''Find Extremum'''
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| Find data cases possessing an extreme value of an attribute over its range within the data set.
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| What are the top/bottom N data cases with respect to attribute A?
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| ''- What is the car with the highest MPG?''
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''- What director/film has won the most awards?''
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''- What Marvel Studios film has the most recent release date?''
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| align="center" | 5
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| '''Sort'''
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| Given a set of data cases, rank them according to some ordinal metric.
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| What is the sorted order of a set S of data cases according to their value of attribute A?
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| ''- Order the cars by weight.''
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''- Rank the cereals by calories.''
 
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| align="center" | 6
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| '''Determine Range'''
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| Given a set of data cases and an attribute of interest, find the span of values within the set.
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| What is the range of values of attribute A in a set S of data cases?
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| ''- What is the range of film lengths?''
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''- What is the range of car horsepowers?''
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''- What actresses are in the data set?''
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| align="center" | 7
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| '''Characterize Distribution'''
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| Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.
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| What is the distribution of values of attribute A in a set S of data cases?
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| ''- What is the distribution of carbohydrates in cereals?''
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''- What is the age distribution of shoppers?''
 
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|-
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| align="center" | 8
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| '''Find Anomalies'''
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| Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.
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| Which data cases in a set S of data cases have unexpected/exceptional values?
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| ''- Are there exceptions to the relationship between horsepower and acceleration?''
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''- Are there any outliers in protein?''
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| align="center" | 9
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| '''Cluster'''
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| Given a set of data cases, find clusters of similar attribute values.
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| Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?
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| ''- Are there groups of cereals w/ similar fat/calories/sugar?''
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''- Is there a cluster of typical film lengths?''
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| align="center" | 10
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| '''Correlate'''
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| Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.
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| What is the correlation between attributes X and Y over a given set S of data cases?
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| ''- Is there a correlation between carbohydrates and fat?''
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''- Is there a correlation between country of origin and MPG?''
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| align="center" | 2
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''- Do different genders have a preferred payment method?''
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| align="center" | 2
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''- Is there a trend of increasing film length over the years?''
 
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| align="center" | 2
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| align="center" | 11
 
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| ''' [[Contextualization (computer science)|Contextualization]]<ref name="ConTaaS"/>'''
| ''' Filter'''
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| Given a set of data cases, find contextual relevancy of the data to the users.
 
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| Which data cases in a set S of data cases are relevant to the current users' context?
|  Filter
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| ''- Are there groups of restaurants that have foods based on my current caloric intake?''
 
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|   '''<font color='#ff8000'>过滤Filter </font>'''
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|}
 
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| Given some concrete conditions on attribute values, find data cases satisfying those conditions.
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| Given some concrete conditions on attribute values, find data cases satisfying those conditions.
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| 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。
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| Which data cases satisfy conditions {A, B, C...}?
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| Which data cases satisfy conditions {A, B, C...}?
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| 哪些数据案例满足条件{A, B, C... } ?
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| ''- What Kellogg's cereals have high fiber?''
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| - What Kellogg's cereals have high fiber?
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|-“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。)”
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''- What comedies have won awards?''
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- What comedies have won awards?
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- “哪些喜剧获了奖?”
       

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