CSE 578 DATA VISUALIZATION PRACTICE
SOLUTION 2026 QUESTIONS WITH ANSWERS
GRADED A+
◉ Synchronous/non-elastic distance and similarity measure.
Answer: Euclidean distance, may not work with all similar behavior
(due to shifts or stretches)
◉ Asynchronous/ elastic distance and similarity measures. Answer:
Edit distance, dynamic time warping, feature-based alignment
◉ Similarity / ranked queries. Answer: Not all sub-goals need to be
satisfied, we need to find out how important that subgoal is
◉ Subjective criteria (semantic gap/subjectivity). Answer: get
relevance feedback on how important certain features are
◉ Iceberg. Answer: Add constraint to summary table to further
reduce it
◉ Nominal. Answer: data whose categories have no implied
ordering
, ◉ Ordinal. Answer: data that has a specified order, but no specified
distance metric (S, M, L)
◉ Interval. Answer: data that has measurable distances and is
additive (like temp)
◉ Ratio. Answer: data that is measurable but is multiplicative and
has a 0 point.
◉ Data analysis. Answer: prepare for visualization, smooth,
transform to get prepared data
◉ Filtering. Answer: subset selected for visualization to get focused
data
◉ Mapping. Answer: mapped to geometric primitives to get
geometric data
◉ Rendering. Answer: transform to image data to get the image
◉ What visual variable is the best way to represent quantitative
dimensions?. Answer: Position
SOLUTION 2026 QUESTIONS WITH ANSWERS
GRADED A+
◉ Synchronous/non-elastic distance and similarity measure.
Answer: Euclidean distance, may not work with all similar behavior
(due to shifts or stretches)
◉ Asynchronous/ elastic distance and similarity measures. Answer:
Edit distance, dynamic time warping, feature-based alignment
◉ Similarity / ranked queries. Answer: Not all sub-goals need to be
satisfied, we need to find out how important that subgoal is
◉ Subjective criteria (semantic gap/subjectivity). Answer: get
relevance feedback on how important certain features are
◉ Iceberg. Answer: Add constraint to summary table to further
reduce it
◉ Nominal. Answer: data whose categories have no implied
ordering
, ◉ Ordinal. Answer: data that has a specified order, but no specified
distance metric (S, M, L)
◉ Interval. Answer: data that has measurable distances and is
additive (like temp)
◉ Ratio. Answer: data that is measurable but is multiplicative and
has a 0 point.
◉ Data analysis. Answer: prepare for visualization, smooth,
transform to get prepared data
◉ Filtering. Answer: subset selected for visualization to get focused
data
◉ Mapping. Answer: mapped to geometric primitives to get
geometric data
◉ Rendering. Answer: transform to image data to get the image
◉ What visual variable is the best way to represent quantitative
dimensions?. Answer: Position
