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Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration for Standards-Based Modernisation, Workshop 5-7 May 2015, Geneva

Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Page 1: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

Implementing the GSIM Statistical Classification model – the Finnish way

Essi Kaukonen / Statistics Finland

UNECE Workshop on International Collaboration for Standards-Based Modernisation, Workshop 5-7 May 2015, Geneva

Page 2: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Implementing the GSIM Statistical Classification Model (GSIM SCM) - the Finnish way

• Where we started from• Implementation as a process

• Changes made to the GSIM SCM in our implementation

• Lessons learned

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Comparing and Testing

Taking into use Exploring Modifying

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Page 3: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Starting point

• Classifications had been centrally managed for two decades• Classifications in the central system were used as SAS formats

and in dissemination, partly also in statistical systems and in data collection

• Many classifications were also maintained in separate statistical systems in different ways and formats

• The Neuchâtel Classification Model had not been used• Classifications from our current classifications system were

transformed into national CoSSI Classification Model used in dissemination since 2005

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Page 4: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Implementation process: Exploring

• The development of the classification system was going on when we paid attention to the GSIM SCM (at the time called Neuchâtel)

• A group of classification experts and IT specialists studied the model carefully element by element

• Translating terms into Finnish was done at the same time, explanatory texts were also partially translated

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Page 5: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Implementation process: Testing and Comparing

• The content of the existing classification system was compared to the GSIM SCM and different kinds of classifications were tested • The objects matched• More attributes in the GSIM SCM – many of them useful• More relationships in the GSIM SCM: for example, Parent

of and Based on were something what we needed

• The model fitted well!

• Mapping with the national CoSSI Classification Dissemination Model was also done

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Page 6: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Classification Index Entry

Classification SeriesClassification Series

Correspondence Table

Map

Classification Index

LevelLevel

Classification ItemClassification Item

Classification Index Entry

Statistical Classification

Statistical Classification

Correspondences

Statistical Classification

Correspondence Table

Level

Classification Item

Classification Index

Classification Index Entry

Classification Series

Map

Based on

GSIM Statistical Classification Model

CoSSI Classification (dissemination) Model

Classification system from the 1990s

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Page 7: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Implementation process: Modifying the GSIM SCM, principles

• Enabling effective classification management• Not to cause too much burden on end users• Information about the users of classifications visible• Support several languages• Classification ”Chains” also in the future system

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Page 8: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Implementation process: Taking into use

• The GSIM SCM included what we needed• The GSIM SCM was already widely used within statistical

organisations• The Neuchâtel Classification model was included in the GSIM as

the GSIM Statistical Classification model• An international model enables international cooperation

The GSIM SCM was adopted into our new SOA-based Classification System

• Code Lists and Statistical Classifications in the same system – Not nice but can be done

The National Classification System?

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Page 9: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Modifying the GSIM SCM: a closer look

Added elements• Creator, Modifier• Dates• Status • Type

• User

Excluded elements• Most of the attributes of

the Level object• Case Laws• Some elements of

Classification and Classification Item (like Subject areas, Copyright, Derived from, Updates, Updates possible)

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Page 10: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Special solutions in the database: Languages

Finnish, Swedish and English are all used in dissemination and data collection

In the database:• A separate language-table• Certain attributes can have several languages• New languages can be added

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Page 11: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Special solutions in the database: Classification ”Chains”

• In statistical systems Classification ”Chains” have been used in yearly macros, etc.

• In the database we have Variant Number, which can be used when referring to these chains of classifications, which are time versions for each other

• Classification Series: Technical Name + Variant Number + Valid From -date = Unique Classification with Classification Id

NACE Rev 1.1Variant Number=45

NACE Rev. 2Variant Number=45

NACE Rev.1Variant Number=45

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Page 12: Implementing the GSIM Statistical Classification model – the Finnish way Essi Kaukonen / Statistics Finland UNECE Workshop on International Collaboration

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Lessons learned

• Takes time, requires exploring and testing• The model is complex as there are plenty of

relationships also in reality challenges in application development careful planning needed

• Cooperation is needed! (both in-house and external)

• Meets our requirements!• Enables cooperation! • Easier to understand the GSIM in general!

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