Multimedia Pivot Table Experiment
Name:
Instructions
For this exercise you should place yourself in the role of an expert end-user in particular a biologist or
a social scientist, in this case studying photographs on Flickr. The tool is supporting the experts in
their task. Concept scores/ranking should in this case also be assumed as supporting the user and not
be evaluated as such.
Data
For this task you are supplied with a dataset of almost 17.000 images from Flickr with their tags,
various metadata and a collection of approximately 150 results of visual concept detectors for
various semantic concepts. You will be using the MediaTable tool with Multimedia Pivot Tables.
Before starting the experiment you should probably just play around a little with the tool to
familiarize yourself with its functionality.
TASK: Try to derive at least 5 interesting observations different topics e.g. animals or vehicles from
this image collection by using the MediaTable to perform filtering and to fill useful categories and
then use the Multimedia Pivot Tables to make observations. Try to cover the different insight levels
defined in the lectures:


A new category:
o
Sub-category of an existing category
o
Grouping / generalization of categories
o
Other
Data Characteristics
o
Individual elements
o
Between individual elements
o
Individual category
o
Between categories
o
Of specific metadata values
For each observation provide the information as explained on the subsequent pages.
Note1: that you can use the buckets/categories to collect images with certain characteristics.
Note2: although you cannot transfer selections from the pivottable to the mediatable, you can of
courese use the pivot table to define the right filters to set.
Whenever you have made an observation of interest (i.e. after filling one page in the document) and
have put the corresponding elements in the buckets


Use save bucket (in the file menu) to store the results in a file (named bucket1.csv for
observation 1 etc.).
After that quit the system. In your working directory there will be a file explog.csv . Rename
that file to explog1.csv etc.
After you have made and documented all observations in the above manner, answer the questions
on the final page and then please send the document and all of the above files to m.worring@uva.nl.
EXPLANATION OF THE TABLE (use the ones on the next sheets)
Observation
Write down here a single interesting observation that you have derived by interacting with the data.
Be complete and concise.
Basic pivotcolumns versus aggregation
Observation is based on aggregation?
Observation is based on …. of the pivot table
One cell: the observation can be found in one cell of the pivot table for example the
row of images corresponding to a concept.
Single column: the observation can be found in one column of the pivot table for
example in the tag column.
Single row: the observation is from one row e.g. observations on one image or all
images and their concept values corresponding to one tag.
Distribution of elements in one column: looking at the overall distribution of
elements in columns (by looking at the totals)
Distribution of elements in one row: looking at the overall distribution of elements
in rows (by looking at the totals) e.g. the max concept column
Multiple row observations: observations made on the similarities or differences
between different rows (which might include comparing it to the total of the row).
Multiple column observations: observations made on the similarities or differences
between different columns (which might include comparing it to the total of the
column)
Which
variable?
Variable
Which
column?
Which
column?
Which row?
Which row?
Which row?
Columns?
Which rows?
Columns?
Which
columns?
Rows?
Observation was based on the following pre-categorization (leave empty if none)
Explain what you have placed in the different
Type: predefined by you / discovered during your
buckets.
exploration: subcategory/generalization/ Other.
B1: category 1
If other specify what it is
B2: category 2
B3: category 3
B4: category 4
B5: category 5
Observation
Observation
Basic pivotcolumns versus aggregation
Observation is based on aggregation?
Variable
Observation is based on …. of the pivot table
One cell
Single column
Single row
Distribution of elements in one column
Distribution of elements in one row
Multiple row observations
Multiple column observations
Observation was based on the following
categorizations
B1
B2
B3
B4
B5
Type
Observation
Observation
Basic pivotcolumns versus aggregation
Observation is based on aggregation?
Variable
Observation is based on …. of the pivot table
One cell
Single column
Single row
Distribution of elements in one column
Distribution of elements in one row
Multiple row observations
Multiple column observations
Observation was based on the following
categorizations
B1
B2
B3
B4
B5
Type
Observation
Observation
Basic pivotcolumns versus aggregation
Observation is based on aggregation?
Variable
Observation is based on …. of the pivot table
One cell
Single column
Single row
Distribution of elements in one column
Distribution of elements in one row
Multiple row observations
Multiple column observations
Observation was based on the following
categorizations
B1
B2
B3
B4
B5
Type
Observation
Observation
Basic pivotcolumns versus aggregation
Observation is based on aggregation?
Variable
Observation is based on …. of the pivot table
One cell
Single column
Single row
Distribution of elements in one column
Distribution of elements in one row
Multiple row observations
Multiple column observations
Observation was based on the following
categorizations
B1
B2
B3
B4
B5
Type
Observation
Observation
Basic pivotcolumns versus aggregation
Observation is based on aggregation?
Variable
Observation is based on …. of the pivot table
One cell
Single column
Single row
Distribution of elements in one column
Distribution of elements in one row
Multiple row observations
Multiple column observations
Observation was based on the following
categorizations
B1
B2
B3
B4
B5
Type
Final questions
Strong points of the tool
Weak points of the tool
Suggestions for improvement
The value of the system: what can you (as an expert) AFTER you have used the tool?