Transcript MetaPlus Klas Blomqvist Statistics Sweden Research and Development – Central Methods [email protected] Agenda • The VHS-project – Background • MetaPlus – The concepts, the product • KMI-group – Support and.

MetaPlus
Klas Blomqvist
Statistics Sweden
Research and Development – Central Methods
[email protected]
Agenda
• The VHS-project
– Background
• MetaPlus
– The concepts, the product
• KMI-group
– Support and management for classifications, metadata and
content harmonisation
• The Lotta project, process reengineering at Statistics
Sweden
– A more effective production process
– MetaPlus, future development
• MetaPlus and the production process
Why do we need metadata?
Why do we need metadata?
Metadata at Statistics Sweden
• SCBDOK
– Word template, standardised structure free text description
• MetaPlus (Metadok)
– Formalized metadata, coded information
• KDB, the classification database
– Statistical standards and classifications
• Other documentation
– Description of the statistics – the quality declaration
– Product database
– Private production documentation
The VHS-project
• The VHS-project started 2004
– Compilation of requirements: Metadok, new
requirements
• Systems design from 2005
– Use cases, modeling
• System development 2006
– Application, data base
• Production 2007
– From January 2
MetaPlus
• Replace Metadok
• Scope:
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Documentation tool with improved quality
Better overview over variables and data
Tool for standardisation and harmonisation
Possibilities for reuse and coordinated use
Consecutive documentation
• Requirement:
– That the documented material in Metadok can be
migrated
Information
• User groups
• The Metadata group (reference group)
• The methods council
• The IT council
• The register council
• The board of directors
• The scientific council
• Seminars
The plus with MetaPlus
• Support for the production
– Cooperation with other metadata systems, Archiving, the
Personal Data Act, connection to data
• User support
– Search the microdata
• Improved quality
– Contents, accessibility and comparability
• More cost-effective
– Increased use of collected data
• Reduced respondent burden
– No new collection if data already exists
Easier to document
• Starting point in already existing metadata
– Classifications and standards
– Documentations made by others
– Search the metadata repository
• Document once - reuse
• Input data in the form of tables
– Document the table at once instead of one cell at a time
• Consecutive documentation
• Effects:
– Higher metadata quality
– Automatic harmonisation
Using MetaPlus
• New requests (commissions), new surveys
– Examine if the issue can be solved with already existing
data.
• Information for facilitating coordinated use and
harmonisation
• Search for variables
– Look at value domains
• Find information on populations
– Shows possibilities for matching data
– Highlights differences in data materials
Content
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Standard variables
Variables
Object classes
Classifications and value domains
The survey’s registers
Survey population and register population
The model
Register
Object
class
Variable
Conceptual
value
domain
Register
variant
Population
Object
variable
Value
domain
Register
version
Context
Population
Context
variable
Value
The application structure
Register
Object
class
Register
variant
Population
Register
version
Variable
Can be
reused
Unique for
the survey
round
Database/
file
Column
Value
domain
Content
oriented
IToriented
MetaPlus functionality, some
examples
• Documentation
• Advanced search
• Reuse
• Longitudinal registers
• Harmonisation
• Time series
• Administration
• Historical information
• Classifications
• Archiving
• Variable groups
• Web prototype
• Personal Data Act
Administration
System
The KMI-group
• Research and Development department, all units
represented
– Management, Central Methodology, Register coordination
and microdata and Central IT units
• Responsibilities
– Classifications
– Metadata
– Content harmonisation
• MetaPlus support
– Migration, training, helpdesk
The Lotta project
• Standardised production and tools
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Process orientation
Customer focus
Efficiency
Standardisation
Quality control
• New organisation after summer
– Internal review during summer
The Statistical Production Process
The colours relate to potential areas for process ownership
L
e
v
e
l
Target,cust.dem
Frame and
sample
Data
collection
Data
preparation
Statistical
computation
Dissemination
and
communication
Customer contacts
Compilation
of frame
Administrative
registers
Coding
Estimations
Dissemination
Customer
reactions
Identification of
data sources
Sampling
Direct data
collection
Editing
Production
of tables and
diagrammes
Data dellivery
To customers
Other primary
statistics
Corrections
Analysis of
Process data
and customer
reactions
Cont.,predesign
Evaluation/
cust.satisf.
1
L
e
Assessment
v
Data
delivery
Survey design
Archiving
Statistical
analysis
Feed-back to
the production
process
Prognosis
e
Tender/agr.
Simulation
models
l
Technical
preparations
2
Compilation
of results
Documentation
preparations
General for all processes:
Internal
evaluation,
quality control
Deliveries
between
processes
Process data
Meta data
System architect.
Keeping of reg.
Treatment of
Time series
breaks
MetaPlus in the Statistical
Production Process
Target,cust.dem
Frame and
sample
Data collection
Data
preparation
Statistical
computation
Cont.,predesign
Dissemination
and
communication
Evaluation/
cust.satisf.
General for all processes:
Internal
evaluation,
quality control
Deliveries
between
processes
MetaPlus
Process data
Meta data
System architect.
Keeping of reg.
Treatment of
Time series
breaks
Conclusions
• Reuse
• Harmonisation and standardisation tool
• MetaPlus 1.2 in production
The End!
• Organization
(the KMI-group)
• Content – slow progress