ProRail is a large organization responsible for the maintenance and extension of the Dutch railway network infrastructure. In this organisation the collection and use of asset data is becoming more and more critical part of the business operation. Information regarding the geographic location, physical condition or function of assets within the rail infrastructure is allocated among different data silos with information models and guidelines. Within the organization many applications make use of data collected from a combination of sources which is very inefficient and undesirable, because the quality and accuracy of the data can differ strongly. Therefore gluing these datasets together will always result in a downgrade of data quality. To solve this, ProRail decided to develop one data model that would fit all the required information. For the Dutch railway sector this change was a very big challenge and meant an adjustment of their entire way-of-working. For this reason ProRail needed to built a solution for the interim period. This solution had to unify data from the schematic world and the geographic world into an integrated output and was named Harmony. In this presentation you will learn why this project was the perfect fit to be built in FME, why it was a big development challenge and how major FME (Server) releases allowed it to mature over the years.

1. Harmony
Unifying data silos with FME

2. Presenter
The
Peak
of
Data
Integration
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Stefan
van den Berg
FME Technology
specialist

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A data silo is a group of data that is
accessible by one department, but
isolated from the rest of the
organization.

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Geographic world

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Schematic world

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Spatial Data Warehouse: Naiade

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“We need an application which can combine the schematic
and geographic world to be able to feed Naiade”
HARMONY

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11. Perfect fit for FME
Many data formats
Automations
Debugging functionality
Endless transformers

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Dynamic reading… …and writing

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Pre-processing

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Collection of 57 workspaces

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FME Feature Store (FFS) format for
intermediate steps and data outages
Dynamic naming convention

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Step 1: Validate data sources

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Step 2: Combine schematic and geographic
● Overlapping information (non-spatial)
● Object type
● Name
● Junctions
● Regions
ProRail Unique Identifier (PUIC)

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Example: BufferStop

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Step 3: Map to Naiade data model
● Convert attributes
● Build aggregated data objects

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Aggregated object: PPC-Track
Signal Signal
Signal
Switch
Switch
Track
Track
Track
“Location on the track where a train can safely stand still”

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Aggregated object: PPC-Track
Signal Signal
Signal
Switch
Switch
Track
Track
Track
“Location on the track where a train can safely stand still”

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Aggregated object: PPC-Track

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Step 4: Validation
Topology
Geometry
Data model
Relationships

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PPC-Track data outage

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Step 5: Create the exchange format

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Step 5: Automation

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Step 5: automate the proces
● Pre-automation era: Python executable + REST API

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Step 6: automation
1. Python executable + REST API
2.

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Triggered from workspace in
Workspace app (HTTPCaller)

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Step 6: Automation
1. Pre-automation era: Python executable + REST API
2. Server app + automation
3.

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Step 7: Regression testing and reporting

34. ThankYou!
Contact me at:
svandenberg@tensing.com