Database Reference
In-Depth Information
Personal Data:
Personal data encompass all types of organized information
collections that are of personal interest to a particular user, but less interesting
to a broader community. This may involve data on user-owned media (such as
DVD collections or playlists), data on life organization (financial data or address
books) or data related to hobbies and general interests (photo collections, fitness
schedules or coin collections). Visualizing personal data might not always lead to
deep new insights about the data itself. In such cases, the visualization instead
may serve more as a compact visual artifact that can be used to remember
certain events in ones life and serve as a visual representation of self [95]. These
visual representations of self may then be used as online avatars, or simply as
catalysts for storytelling, much like photo albums.
Community Data:
By community data we mean data that might be relevant
to a broad community of users due to similar interests or general appeal. Exam-
ples of community data include the content of political speeches, the number of
users online in a World of Warcraft realm,orvotingresultspercounty.Often
this type of data has a social component associated with it: data might be related
to a social application such as Facebook or MySpace [45], contain statistics on
a large population as with census data [43], or may be related to current events
[104]. Precisely because community data has a lot of general appeal it will often
generate a lot of discussion.
Scientific Data:
Scientific data is data that is of interest to a (relatively) small
number of specialists. Traditionally, information visualization has focused on the
sciences, because they generate a wealth of structured and often numerical data
in ready need of analysis. This makes them very suitable to mathematical analy-
sis techniques and visual mapping. In the humanities, however, most information
comes in unstructured raw text format. If we want visualization to be applied in
domains such as literature and political science, we will need to define suitable
pre-processing techniques that can extract meaningful information from a body
of text. This will often require some amount of natural language processing or
expert input. While there are a few applications of information visualization to
data from the humanities (e. g., [101]), the area remains largely untapped despite
substantial promise to yield many useful techniques with applicability to many
different areas of everyday life.
Interplay of Data Types:
Note that the distinction between types of data
is not always clear cut and many data sets could fall into different categories
depending on their use. For example, a community data set on World of Warcraft
users and their interactions might be considered a scientific data set by social
scientists, while the personal data of celebrities might have a broad general
appeal. Visualizations of all these types of data can be shared, albeit for different
purposes. Personal data might be shared with other users as a means of personal
expression. Community data is often shared to spark broad discussion, while
scientific data often needs to be shared because it is too complex for one person to
analyze on their own or because it requires multiple specialized skills to analyze.
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