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Integrated metadata systems History Status Vision Roadmap. [email protected]. Integrated Metadata Systems. Stove-piped statistical production (systems) with no, or at the best, encapsulated metainformation, represents our remains from the IT stone - age. - PowerPoint PPT Presentation
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Integrated Metadata Systems Stove-piped statistical production (systems) with no, or
at the best, encapsulated metainformation, represents our remains from the IT stone-age.
First steps towards the consciousness of metadata(structures) were taken some 20 years ago: metadatadriven on-line systems file description- and other archives of structured
documentation The technological evolution has been the driving force
towards a vision of a coherent statistical (IT) system However, the state-of-the-art-technology has also at
all times represented one of the most important obstacles to success
in addition to the human- and organisational barriers that we also discuss
Technological barriers Lack of processor speed and data storage capacity lack of access possibilities across different IT
systems Lack of database functionality and flexibility Lack of awareness of metainformation as a whole in
the IT industry (handling of technical meta-information at the most), i.e the kind of metainformation that was handled in the first datawarehouse solutions
Lack of (IT) standards, but anyhow; why didn’t we achieve more when we
had all our information systems within one mainframe ? due to the human and organisational barriers ?
Our current advantages
WWW Open standards on
connectivity LAN/WAN communication database connectivity
standardised exchange of data on protocol level syntactic level
Object orientation Web services !
But what about the semantic level ?
A vision for a coherent statistical system The basic architecture of a coherent
statistical system is formed by the structure, content and handling of metainformation
The IT system will never reflect anything else but the level of standardisation and coordination of the statistical production within the organisation
NSI’s must take into account all statistical IT systems currently running, having been developed over the past 20 years, which would need to fit into a new or upgraded system
A coherent statistical system based on integration of what you already have, or convert everything to a new (gigantic) system ?
ObjectiveContent
Design and planning
PopulationSample
Collectionmethods
Processmethods
Dissemi-nation
Evaluation
Operation
Establishpopulation& sample
Data collect-ion & Edit
PresentationDissemination
EstimationAggregation
Expert knowledge:-Guidelines-Articles-Methods-People
Inputdata
Inputdata
Stat.data
Knowledge base
Local metadata
Global metadata
ClassificationsStandards
-Datadoc-Stat.Activities-Stat.doc-Quality decl.-Structured metadata
Datawarehouse
Populations
Localprod.data
Observationregister
Localprod.data
Disseminationdatabase
Source: Bo Sundgren
A vision for a coherent statistical system
Know-ledge
Metadata
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata Question-
nairerepository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Metadata
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata Question-
nairerepository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Census/Survey
Metadata
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata Question-
nairerepository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
What information is neededto establish consistent linksbetween the components of your(structured) metainformation system ?
Metadata
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata Question-
nairerepository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Metadata
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata Question-
nairerepository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
XML
Metadata components
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata
Question-naire
repository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Metadata components
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata
Question-naire
repository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Link
ing/
Map
ping
Metamodel
Metadata
Data
Three layeredmodel
Metadata components
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata
Question-naire
repository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Link
ing/
Map
ping
Collection
Aggregation
Estimation
Data Editing
Dissemination
Process
Metadata components
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata
Question-naire
repository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Link
ing/
Map
ping
Differentdomains
Dom
ain
2D
omai
n 1
Dom
ain
n
Metadata components
Filedescript.
Classifications
Macrodatabase
Variabledefinitions
Localmetadata
Question-naire
repository
Content(Quality)
declaration
Statisticalactivities
LocalmetadataLocal
metadata
Acc
ess
End userneeds
Non-structured metainformation
Text Text-mining Knowledge systems
Challenge, and upcoming reality:
How shall we be able to store, retrieve and maintain the knowledge of the organisation much more independent of their (shifting) staff ?
Metadata in the statistical production
Data input Data throughput Data dissemination
I
Data collection
BS
CRDS
NSI OCR
ELQ
www.ssb.no
Metadata
PInternalBusinessSystems
Mapping betweenstatistical and in-housedata definitions
ElectronicQuestionnaires
PaperQuestionnaires
Subject mattersystems
Optical char.recognition,intrepretationverifiying
Data DefinitionsQuestionsRules/Checks Questionnaires
CentralRaw DataStorage
XML Questionnairegeneration
Links to a (national) repositoryof Data definitions/Questionnaires
Linked to BusinessRegister
DisseminationInternet
Data collectionAdministrativeregisters etc.
Macro databases
Mikrodatabases
Raw data
Metadata
Disseminationdatabase(s)
Observationregisters
Data editing and imputation
Estimation
Question-naires
Aggregation
Stat.base
EDI
Internationalreporting
IT in
the
Off
ice
Adm.routines
Correspondence
Papers
Research
Guidelines&rules
CRM
Stat.bank
