Chapter 2: Data Abstraction

Question 1

Describe the five basic data types discussed in the lectures.

Answer 1

1. Items, which defines an individual entity that is discrete, such as a row in a table or a node in a network.
2. Attributes, which defines a specific property that can be measured or logged. Usually a column in a table.
3. Links, which defines relationships two items. An example would be an edge or arc in a network.
4. Positions, providing location in a space.
5. Grids, A grid specifies the strategy for sampling continuous data in terms of both geometric and topological relationships between its cells.

Question 2

Describe the dataset types discussed in the lecture.

Answer 2

1. Tables: Consists of rows and columns (items & attributes). Tables can be multidimensional with multiple keys. A key acts as an index that is used to look up attributes for a specific item
2. Networks (Graphs): suited for specifying relationships between items. An item in a network is called a node. A link is a relationship between nodes (edges). Trees are a subtype of a network.
3. Fields: Contains attributes associated with cells. Each cell in a field contains measurements or calculations from a continuous domain. (not that important for the exam)
4. Geometry: specifies information about the shape of items with explicit spatial positions. Examples of items could be points or curved, 2D surfaces or regions, or 3D volumes.

Question 3

In which three ways can items be grouped?

Answer 3

Lists: a group of objects with a specified order.
Clusters: Grouping based on attribute similarity.
Sets: An unordered group of items

Question 4

In which two ways can attribute types be categorized?

Answer 4

1. Categorical attribute types: Values are discrete, but without order.
2. Ordered:
   - Ordinal attribute types are discrete and have some order associated to it. Performing arithmetic on these does not provide value. (e.g. for shirt sizes L + M = ??)
   - Quantitative: Values are numerical and also have some order. Performing arithmetic on these values has some meaning. (e.g. numbers can be added or subtracted)

Question 5

Name three ways in which ordered data can be ordered.

Answer 5

• Sequential ordering: the possible values fall within a homogeneous range with a minimum and maximum values.
• Diverging: the range of values can be split into two sequences pointing in opposite directions and meet at a 0 point. (degrees Celsius for example, but Kelvin is not diverging)
• Cyclic: values wrap around back to a starting point, rather than continuing to increase indefinitely. (season is an example of ordinal data that is cyclic.)

Other examples:
- Mountain heigh is sequential when measured from sea level, while an elevation dataset might be diverging, where the middle point is sea level. It often comes down to how data is measured.

Question 6

What type of attribute is an ID of an item?

Answer 6

Categorical, it does not have a meaningful order and it is discrete.

Question 7

What type of attribute is a a date, like an order date?

Answer 7

Quantitative. Don’t ask me why, this is in the slides, but it is good to keep in mind. Probably because dates are time, time is continuous and time can be added and subtracted. E.g. if you use the UNIX way of defining time or dates, you can use it to perform arithmetic. Though, this is my own interpretation.

A UNIX timestamp is a numerical representation of the number of seconds that have elapsed since January 1, 1970, at 00:00:00 UTC. It is often used to represent a point in time in a computer system.

Question 8

Which two types of dataset availability were discussed in the lectures for the use in visualizations?

Answer 8

Static (Offline): You have a single file and it is visualized in one go
Dynamic (Online): Data trickles in during a vis session. Values can be updated, added or removed over time