A hazard warning helps to protect people. It provides important information and initial recommendations on how those affected can best react in the event of a sudden emergency. In Germany, warnings are implemented through a mix of warning media - and thus via as many channels as possible. Mapping and analyzing data about warnings, warning app usage, and warning media support the ongoing improvement of the warning system in Germany. The data to be analyzed sometimes comprises several million data records, which requires efficient processing of the data. In this talk, we will give a few tips and considerations on performance handling in FME so that colleagues do not have to wait several days for the analysis results of the warning data.

1. Emergency Warnings for the Population:
A Technical Review on Number
Crunching with FME

2. Presenter
The
Peak
of
Data
Integration
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Pia
Sperber
Data Analyst
German Federal Office of
Civil Protection and
Disaster Assistance

3. The
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Agenda
1. Introduction
2. Warning for the Population
3. Tipps and Tricks for Structure,
Performance and Reusability in FME

4. The
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Introduction

5. The
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How a structured and
thoughtful workbench saves
time and nerves.

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Scenarios
• You have to identify errors in your
workflow.
(structure needed)
• You need to change parts of your
workflow.
(needs to be easily modified)
• There is a lot of traffic on FME Server Flow.
(fast performance needed)

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Case Study:
Analysis of Warning Media

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Warning media
Wall GmbH
Images:
BBK

9. The
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FME
Wall GmbH
Images: BBK
Dataset
Dataset
Dataset
Dataset
Dataset
Dataset
Read
Calculate
statistics
Write
Visualize in
Dashboard
© GeoBasis-DE / BKG
2019

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Reading Data from ArcGIS Feature
Service
Link portal and credentials to user parameter.
Rename readers.

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Reading Data (Exposing
Attributes)
Read only attributes that are actually needed.

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Reading Data (Exposing
Attributes)
Instead of manually determining the type
of data expose fme_feature_type.

13. The
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Reading Data (Filter Attributes)
Filter data right from the source,
e.g. XML Filter Expression for WFS

14. The
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Reading Data from Esri FileGDB
• For simple Features use Esri Geodatabase (File Geodatabase Open API) reader
🡪 avoids the overhead of the ArcGIS Geodb API.
• Directly query database in Reader

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Split Workspaces
• Workspaces may be run simultaneously.
• Smaller workspaces are easier to understand.

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Use Feature Cache to avoid manual
typing
Manual Testfilter vs. AttributeFilter (Import from Cache)

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InlineQuerier instead of multiple
FeatureJoiners
Join data with InlineQuerier.

18. The
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Use CustomTransformers to avoid
redundancy
CustomTransformers instead of manual repitition of tasks

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Speed up with alternative
Transformers
Using Clipper instead of AreOnAreaOverlayer

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Backend – The invisible backbone of
Frontend

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Conclusion

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Revising workspaces saves
time and nerves.

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Call to Action
1. Try to implement a clear and logical
structure.
2. Think about (faster) alternative
Transformers.
3. Stay flexible with CustomTransformers and
Parameters.

24. The
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Resources
● https://community.safe.com/s/article/feature-caching-and-pe
rformance
● https://community.safe.com/s/article/performance-tuning-fm
e
● https://community.safe.com/s/article/2-Performance-Tuning-
FME-Workspace-Authoring-Design-Patterns
● https://www.bbk.bund.de/SharedDocs/Downloads/EN/Media
thek/Publikationen/ratgeber-englisch-disasters-alarm.pdf?__
blob=publicationFile&v=6

25. ThankYou!
Pia.Sperber@bbk.bund.de