1. What is data abstraction and why is it useful to design a visualization solution?

• Translation from domain-specific language to generic visualization language
• Things to do
  • Idetify dataset type(s), attribute types
  • Identify cardinality
    • How many items in the dataset
    • What is cardinality of each attribute
      • Number of levels for categorical data
      • Range of quantitative data
  • Consider whether to transform data = Guided by understanding of task
• Data Model vs Conceptual Model

Data Model is mathematicl abstraction

• sets with operations
• variable data types in programming languages

Ex: floats (12.1, 24.1, -1.2)

Conceptual model

• Mental construction (semantic)
• Supports reasoning
• Typicallung based on uderstanding of tasks

Ex: temperature

• Description of data in ways that help to decide what options encoding/mappings and vis idioms are available and approriate

2. Which data types can be included in a dataset?

The five basic data types: items, attributes, links, positions, and grids.

• Item: an individuals entity that is discrete such as a row in a table or a node in a network. For example: Items may be people, stocks, coffee shops, genes, or cities
• Attribute: is some specific property that can be measured, observed or logged. For example: attributes could be salary, price, number of sales, protein expression level.
• Link: relationship between items, typlically within networks
• Position: is spatial data, providing location in 2D or 3D space
• Grid: specifies the discrete sampling method for continous data

3. Can you sketch the visualization design process?

1. Domain situation: who is the target user, their domain of interest, their question and their data
2. Abstraction: translate from specifics of domain to vocabulary of visualization
   1. What is shown? Data abstraction
   2. Why is the user looking at it? Task abstraction (Ex: high score movies)
3. Idiom = How is it shown?
   1. Visual encoding idiom: how to draw
   2. Interaction idiom: how to manipulate
4. Algorithm: efficient computation

4. What is task abstraction? What do we mean when talking about actions and targets?

Transforming task description from domain-specific language into abstract form allows to reason about similarities and differences between them for easier analysis, comparision and the application of visualization tools.

Actions = Thing to do

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Three levels of actions: analyze, search, and query.

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1. Analyse = the highest-level actions are to use vis to consume or produce information

   1. Consume tasks are related to
      • Discover = Exploring data to have ideas, generate hypothesis
      • Presentation = Communicate results, teach
      • Enjoy = many vis application are used by general public for pure enjoyment
   2. Produce of data refers to

      • Derived data = decide what the right thing to show

        

        Typically in sci vis = try to derive and visualize low dimensional parameters characterizing the event

        

      • Annotations = key tools to transform semantics to raw data

      • Recordings = saves or captures visualization elements as persistent artifacts

2. Search = at the middle level, search can be classified according to the identify and location of targets are know or not

   

   1. Lookup (tra cứu)
      • word in dictionary
   2. Locate (tìm kiếm)
      • keys in your house
      • node in the network
   3. Browse (duyệt)
      • books in bookstore
   4. Explore (khám phá)
      1. cool neigborhood in new city

3. Query = at the low level, queries can have three scopes: identify one target, compare some targets and summarize all targets

Targets = Selected data

Targets meaning some aspect of the data interesting for the user

1. Three high-level targets are very broadly relevent for all kinds of data
   1. Trends: is a high-level characterization of a pattern in the data. Ex: increase, decrease, peaks, troughs and plateaus
   2. Outliers: some data doesn’t fit well with that backdrop (bối cảnh)
   3. Features: is task dependent, meanng any particular structures of interest
2. The lowest-level target for an attribute is
   1. To find an individual value
      • Find the extremes: the minimum or maximum value across the range
      • High level scope is the distribution of all values for an attributes
   2. Some targets encompass the scope of multiple attributes: dependencies, correlations, and similarities between attributes.
3. Some targets pertain to specific types of datasets.
   1. Network data specifies relationships between nodes as links. The fundamental target is to understand the structure of these interconnections. That’s network’s topology
   2. For spatial data, understanding and comparing the geometric shape is the common target

5. What is a visualization idiom according to Munzner's definition?

A vis idioms is a distinct approach to creating and manipulating visual representations

Two aspects

• Visual encoding idiom: how to draw
• Interactive idiom: how to manipulate

6. Can you define marks and channels? Are all the channels similarly effective?

Marks

Marks are geomatric primitive objects classify according to the number of spatial dimensions they require.

For example:

• a zero-dimensional (0D) mark is a points
• a one-dimensional (1D) mark is a line
• a two-dimensional (2D) mark is an area

Channels

• Parameters control the appearance of marks
• The same information can be mapped onto mutiple channels

→ All channels are not equal: the same data attribute encoded with two different visual channels will result in different information content.

• Channel Types

7. Define the concepts of expressiveness, effectiveness, discriminability, and separability of channels.

8. What are pre-attentive cues and why are they important in visualization? How can we enforce the perception of grouped items? Are grouping methods similarly effective?

Pre-attentive are important in visualization