2,443
个编辑
更改
→数据用户的分析活动
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>
{| class="wikitable" border="1"
{| class="wikitable" border="1"
! 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
|-
|-
| align="center" | 1
| align="center" | 1
| '''Retrieve Value'''
| '''Retrieve Value'''
| 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。
| 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？
| “-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?''
“-电影《乱世佳人》有多长？”
“-电影《乱世佳人》有多长？”
|-
| align="center" | 2
| ''' 过滤Filter'''
| 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。
|哪些数据案例满足条件{A, B, C... } ？
| “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。）”
“哪些喜剧获了奖？”
“-哪些基金的表现不如 SP-500？”
|-
| align="center" | 3
| '''计算派生值Compute Derived Value'''
| 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。
| '''<font color='#ff8000'>聚合函数aggregation function </font>'''F 在给定数据集 S 上的值是多少？
| “-'''<font color='#ff8000'>波斯特谷物Post cereals</font>'''的平均热量是多少？”
“-所有商店的总收入是多少？”
“-有多少汽车制造商？”
|-
|-
| align="center" | 4
| '''Find Extremum'''
| Find data cases possessing an extreme value of an attribute over its range within the data set.
| What are the top/bottom N data cases with respect to attribute A?
| ''- What is the car with the highest MPG?''
''- What director/film has won the most awards?''
''- What Marvel Studios film has the most recent release date?''
|-
| align="center" | 5
| '''Sort'''
| Given a set of data cases, rank them according to some ordinal metric.
| What is the sorted order of a set S of data cases according to their value of attribute A?
| ''- Order the cars by weight.''
''- Rank the cereals by calories.''
|-
|-
| align="center" | 6
| '''Determine Range'''
| Given a set of data cases and an attribute of interest, find the span of values within the set.
| What is the range of values of attribute A in a set S of data cases?
| ''- What is the range of film lengths?''
''- What is the range of car horsepowers?''
''- What actresses are in the data set?''
|-
| align="center" | 7
| '''Characterize Distribution'''
| Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.
| What is the distribution of values of attribute A in a set S of data cases?
| ''- What is the distribution of carbohydrates in cereals?''
''- What is the age distribution of shoppers?''
|-
|-
| align="center" | 8
| '''Find Anomalies'''
| Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.
| Which data cases in a set S of data cases have unexpected/exceptional values?
| ''- Are there exceptions to the relationship between horsepower and acceleration?''
''- Are there any outliers in protein?''
|-
| align="center" | 9
| '''Cluster'''
| Given a set of data cases, find clusters of similar attribute values.
| Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?
| ''- Are there groups of cereals w/ similar fat/calories/sugar?''
''- Is there a cluster of typical film lengths?''
|-
| align="center" | 10
| '''Correlate'''
| Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.
| What is the correlation between attributes X and Y over a given set S of data cases?
| ''- Is there a correlation between carbohydrates and fat?''
''- Is there a correlation between country of origin and MPG?''
''- Do different genders have a preferred payment method?''
| align="center" | 2
''- Is there a trend of increasing film length over the years?''
|-
| align="center" | 2
| align="center" | 11
| ''' [[Contextualization (computer science)|Contextualization]]<ref name="ConTaaS"/>'''
| ''' Filter'''
| Given a set of data cases, find contextual relevancy of the data to the users.
| Which data cases in a set S of data cases are relevant to the current users' context?
| ''- Are there groups of restaurants that have foods based on my current caloric intake?''
|-
| '''<font color='#ff8000'>过滤Filter </font>'''
|}
| Given some concrete conditions on attribute values, find data cases satisfying those conditions.
| Which data cases satisfy conditions {A, B, C...}?
| ''- What Kellogg's cereals have high fiber?''