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Integrated metadata systems History Status Vision Roadmap Rune.Gloersen@ssb.no
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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
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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 ?
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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 ?
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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 ?
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Objective Content Design and planning Population Sample Collection methods Process methods Dissemi- nation Evaluation Operation Establish population & sample Data collect- ion & Edit Presentation Dissemination Estimation Aggregation Expert knowledge: -Guidelines -Articles -Methods -People Input data Input data Stat. data Knowledge base Local metadata Global metadata Classifications Standards -Datadoc -Stat.Activities -Stat.doc -Quality decl. -Structured metadata Datawarehouse Populations Local prod.data Observation register Local prod.data Dissemination database Source: Bo Sundgren A vision for a coherent statistical system Know- ledge
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Metadata File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata
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Metadata File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata Census/ Survey
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Metadata File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata What information is needed to establish consistent links between the components of your (structured) metainformation system ?
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Metadata File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata
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Metadata File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata XML
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Metadata components File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata
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Metadata components File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata Linking/Mapping Metamodel Metadata Data Three layered model
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Metadata components File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata Linking/Mapping Collection Aggregation Estimation Data Editing Dissemination Process
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Metadata components File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata Linking/Mapping Different domains Domain 2 Domain 1 Domain n
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Metadata components File descript. Classifications Macro database Variable definitions Local metadata Question- naire repository Content (Quality) declaration Statistical activities Local metadata Local metadata Access End user needs
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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 ?
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Metadata in the statistical production Data input Data throughput Data dissemination
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I Data collection BS CRDS NSI OCR ELQ www.ssb.no Metadata P Internal Business Systems Mapping between statistical and in-house data definitions Electronic Questionnaires Paper Questionnaires Subject matter systems Optical char. recognition, intrepretation verifiying Data Definitions Questions Rules/Checks Questionnaires Central Raw Data Storage XML Questionnaire generation Links to a (national) repository of Data definitions/Questionnaires Linked to Business Register
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