1. Session 7 – Data aggregation and visualisation

2. TUV + external speakers
Agenda
Data aggregation and visualisation
TUV + external speakers
09:00 – 10:30
Presentation of data aggregation approach and visualisation approach
TUV
Practice report on challenges regarding “Quality of Experience” data
Mr. Manner, Netradar (Finland)
Mrs. Teixeira, INRIA (France)
Mr. Rood, Stratix (Netherlands)
Survey and discussion, inter alia:
Aggregation an visualisation rules
Representativeness of data
All

3. Step 1: The process of data aggregation and visualisation
Selection and processing of raw data
Step 2:
Aggregation
Step 3:
Conversion to data model
Step 4:
Data visualisation

4. Step 1: Selection and processing of raw data
What is raw data and which choices have to be made?
What is raw data
Choices to be made
Best case: Information on QoS at a specific location / geo-coordinate
Other cases: Information on QoS linked to a geographical area
What should be the purpose / expressiveness of my aggregated data?
Which information do I want to share?
Which single QoS data sets should be used to build up the aggregated data sets?
NOTE: IP-addresses for single measurements are „personal data“ and cannot be collected within this project.
 Data transformation into geo-coordinates or aggregation to address or grid level before data provision !

5. Step 1: Selection and processing of raw data
Selection / elimination of single data based on
Location accuracy
Plausability of values
Multiple measurements from same user in same area
- 10 Mbit/s
Description in meta data on what has been done

6. Step 1: Selection and processing of raw data
Discussion on who should carry this out?
Option 1: The initiative as owner of data
Option 2: The contractor
Requirement: close cooperation with data suppliers / giving instructions on selection / elimination choices + - + -
Every data supplier carries out individual measures
numerous approaches on processing raw data
Knows context (focus and intention) of collected data best
Data supplier has sovereignty of interpretation
Contractor does not know the context of collected data
 Risk of misinterpretation and data privacy concerns
More homogenous approach on process raw data as only carried out by contractor

7. Step 2: Aggregation
What is aggregated data and which choices have to be made?
What is aggregated data?
Choices to be made
Combination of all values of QoS data sets that derive from the same region and have the same content to an aggregated value (e.g. min, max etc.)
Which aggregated values should be provided? / Which quality criteria have to be fulfilled to provide a value for a region?
Which significance should the values have?

8. Description in meta data on what has been done
Step 2: Aggregation
Definition of aggregation rules and information on which values will be supplied for which regions:
Sample sizes
Spatial distribution of samples
Type of additional information needs be displayed in order to explain values
Rural
Urban
8 samples
1,300 samples
Description in meta data on what has been done

9. Option 1: The initiative as owner of data Option 2: The contractor
Step 2: Aggregation
Discussion on who should carry this out?
Option 1: The initiative as owner of data
Option 2: The contractor
Requirement: close cooperation with data suppliers / giving instructions on selection / elimination choices
Every data supplier carries out individual measures
numerous different aggregation rules -
Data supplier has sovereignty of interpretation;
knows the intended significance of the values, is able to assess which values are crucial +
Contractor does not know the context of collected data
 Risk of misinterpretation -
Homogeneous approach to aggregation rules as only carried out by contractor +

10. Step 3: Conversion to data model
Possible issues that could complicate transfer of data into the data model?
Data provided on
spatial resolution level
which is not compliant to templates
Divergent value categories

11. Step 3: Conversion to data model – Possible issues
Data provided on spatial resolution level which is not compliant to templates
By means of statistical data (e.g. population or number of households) aggregation can be carried out by contractor for data which is supplied in non-grid format
If this statistical data is missing aggregation cannot be carried out by contractor
Missing statistical data
Data is provided for postal codes without geo-reference codes to NUTS levels
Missing geo-data

12. Step 3: Conversion to data model
Divergent value categories
Different technologies clustered into technology group
Different speeds clustered into speed group
Statistical value differences
Max
Min
Average
etc.
Median
>35Mbit/s
>50Mbit/s
16-24Mbit/s
etc.
<1Gbit/s
etc.
NGA
Overall fixed
Mobile
Wired
Wireless

13. Step 4: Data visualisation
General approach
Data will be visualised strictly according to data suppliers‘ intention:
Without modification, or only modified in coordination with data supplier
According to agreement in Memorandum of Understanding
Publication on public version
Publication on expert version
Publication via data feed
Specifications given in meta data will be displayed

14. Step 4: Data visualisation
Visualisation of data sets from different initiatives
Same value combination / different areas
Same value combination / same areas
X = X
X = X ≠ =
If collection approaches are similar, data from different data suppliers (can be visualised in one layer („national initiatives“)
Data suppliers decide
This decision can be indicated in data model „Solitary visualisation“ or „Simultaneous visualisation“
As collection approaches are very heterogeneous, it is not possible to visualise several initiatives on one layer
Challenge to ensure that each data set is represented according to data suppliers‘ intention