A general temporal data model and the structured population event history register

At this time there are 37 demographic surveillance system sites active in sub-Saharan Africa, Asia and Central America, and this number is growing continuously. These sites and other longitudinal population and health research projects generate large quantities of complex temporal data in order to...

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Bibliographic Details
Main Author: Samuel J. Clark
Format: Article
Language:English
Published: Max Planck Institute for Demographic Research 2006-10-01
Series:Demographic Research
Subjects:
DSS
Online Access:http://www.demographic-research.org/volumes/vol15/7/
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spelling doaj-610ebeb388da4aa6bbfe5132d73fcc442020-11-24T20:53:11ZengMax Planck Institute for Demographic ResearchDemographic Research1435-98712006-10-01157A general temporal data model and the structured population event history registerSamuel J. ClarkAt this time there are 37 demographic surveillance system sites active in sub-Saharan Africa, Asia and Central America, and this number is growing continuously. These sites and other longitudinal population and health research projects generate large quantities of complex temporal data in order to describe, explain and investigate the event histories of individuals and the populations they constitute. This article presents possible solutions to some of the key data management challenges associated with those data. The fundamental components of a temporal system are identified and both they and their relationships to each other are given simple, standardized definitions. Further, a metadata framework is proposed to endow this abstract generalization with specific meaning and to bind the definitions of the data to the data themselves. The result is a temporal data model that is generalized, conceptually tractable, and inherently contains a full description of the primary data it organizes. Individual databases utilizing this temporal data model can be customized to suit the needs of their operators without modifying the underlying design of the database or sacrificing the potential to transparently share compatible subsets of their data with other similar databases. A practical working relational database design based on this general temporal data model is presented and demonstrated. This work has arisen out of experience with demographic surveillance in the developing world, and although the challenges and their solutions are more general, the discussion is organized around applications in demographic surveillance. An appendix contains detailed examples and working prototype databases that implement the examples discussed in the text.http://www.demographic-research.org/volumes/vol15/7/datadata modeldatabaseDSSeventinfluencelongitudinalmetadatamethodspopulation registerrelationalSPEHRstatesurveillancetemporal
collection DOAJ
language English
format Article
sources DOAJ
author Samuel J. Clark
spellingShingle Samuel J. Clark
A general temporal data model and the structured population event history register
Demographic Research
data
data model
database
DSS
event
influence
longitudinal
metadata
methods
population register
relational
SPEHR
state
surveillance
temporal
author_facet Samuel J. Clark
author_sort Samuel J. Clark
title A general temporal data model and the structured population event history register
title_short A general temporal data model and the structured population event history register
title_full A general temporal data model and the structured population event history register
title_fullStr A general temporal data model and the structured population event history register
title_full_unstemmed A general temporal data model and the structured population event history register
title_sort general temporal data model and the structured population event history register
publisher Max Planck Institute for Demographic Research
series Demographic Research
issn 1435-9871
publishDate 2006-10-01
description At this time there are 37 demographic surveillance system sites active in sub-Saharan Africa, Asia and Central America, and this number is growing continuously. These sites and other longitudinal population and health research projects generate large quantities of complex temporal data in order to describe, explain and investigate the event histories of individuals and the populations they constitute. This article presents possible solutions to some of the key data management challenges associated with those data. The fundamental components of a temporal system are identified and both they and their relationships to each other are given simple, standardized definitions. Further, a metadata framework is proposed to endow this abstract generalization with specific meaning and to bind the definitions of the data to the data themselves. The result is a temporal data model that is generalized, conceptually tractable, and inherently contains a full description of the primary data it organizes. Individual databases utilizing this temporal data model can be customized to suit the needs of their operators without modifying the underlying design of the database or sacrificing the potential to transparently share compatible subsets of their data with other similar databases. A practical working relational database design based on this general temporal data model is presented and demonstrated. This work has arisen out of experience with demographic surveillance in the developing world, and although the challenges and their solutions are more general, the discussion is organized around applications in demographic surveillance. An appendix contains detailed examples and working prototype databases that implement the examples discussed in the text.
topic data
data model
database
DSS
event
influence
longitudinal
metadata
methods
population register
relational
SPEHR
state
surveillance
temporal
url http://www.demographic-research.org/volumes/vol15/7/
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