Data flow, conversion and management within an INSPIRE Infrastructure – A Norwegian Hydrographic Case Study

Data flow, conversion and management within an INSPIRE Infrastructure – A Norwegian Hydrographic Case Study

Tom Ellett von Brasch

INSPIRE Data availabilityINSPIRE Data availability

2 options:

1. Collect new data using INSPIRE data schemas – 20122. Restructure existing data - 2017

ChallengesChallenges

Technical Organisational

Complexity• Data• Data storage

Heterogeneous to homogenous

Scalability

Understanding of options

On-the-fly or offline

Production AND distribution

Cross department process

Inter organisational process

Multiple ETL service providers

Coherent understanding of issues

Data Source

Data Source

Data Source

Target

Data

Structure

Dest. Storage

DistributionStorage

WMS Service

DistributionStorage

WFS Service

General principlesGeneral principles

Norwegian Hydrographic ModelNorwegian Hydrographic Model

Geovekst

Dataset

NMA

Dataset

Polar Inst.

Dataset

NVE

Dataset

4 20

Discovery

Download

View Service

Network Services

Transformation

DecisionsDecisions

Data source facts

Continuous, Often, sporadic

Single database –Distributed databases

Large/Heavy –Small/Light

a1-a7 Data and services both withdata owner

a8-a12 Data sits with data owner, services lay with NMA

a13-a15 Data sits with data owner and with NMA, services lay with

NMAa16-a17 Data sits just with NMA,

managed by data ownerthrough a management client

Data Services

DecisionsDecisions

http://geostandards.geonovum.nl/index.php/6.4.2_Interoperability_of_spatial_data

Updated infrequently

Large datasets

Distributed systems

On-The-Fly Vs Offline

Norwegian hydrographic data:

DecisionsDecisions

NMA INSPIRE DB1

2

3

NMA

3

1

2 copyNetworkServices

Produksjon database acc. internal database schema

Database views acc./like INSPIRE-schema

Web Feature Service

GML acc. Inspire schema

Distribution database acc./like INSPIRE-schema

Transform

ation

of content

TransformationSoftware

XSLT

Inspireapplications

Configuration of the WFS server

Offline transformation

1

2

3

4

DecisionsDecisions

• Large dataset, infrequent updates = offline transformation

• Obtain as many facts as possible Dataset/Organisations/infrastructure

• Consider implications on network services

• There is no single best practice

• There is no right way or wrong way

• Data flow processes can not just be different for each dataset orTheme, but for indivudal featuretypes.

• There is a plethora of options

• Good cooperation and communication between data owners will becrucial

ConclusionsConclusions