Changes for page Guidelines for SDMX Data Structure Definitions

Last modified by Artur K. on 2026/05/29 14:28

From 1.9 to 1.10 From 1.11 to 1.12

From version 1.10

edited by Helena K.
on 2026/01/15 12:40

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To version 1.11

edited by Helena K.
on 2026/01/15 12:40

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@@ -247,24 +247,22 @@
  **Table 4. General comparison of data structuring approaches**
--|(% style="width:360px" %)**Many pure concepts**|(% style="width:1255px" %)**Few composite concepts**
--|(% style="width:360px" %)cleaner data structure|(% style="width:1255px" %)(((
++|(% style="width:416px" %)**Many pure concepts**|(% style="width:1199px" %)**Few composite concepts**
++|(% style="width:416px" %)cleaner data structure|(% style="width:1199px" %)(((
  Mixed dimensions may be composed inconsistently making the decomposition into purer concepts and code lists difficult (requiring complex mapping etc.). Information that corresponds to the same concept may be included in different dimensions, e.g. reference year is contained in the indicator dimension in the first example but in the unit in the second example below. The optimal common data structure would consist of Economic Indicator, Unit, and Base period.
  [[image:1768469652632-803.png||height="106" width="352"]]
--
--
  )))
--|(% style="width:360px" %)shorter and simpler code lists|(% style="width:1255px" %)code lists longer and more complex, may require hierarchy to be “readable”
--|(% style="width:360px" %)more flexible in terms of defining constraints, but constraints more complex|(% style="width:1255px" %)simpler constraints, but some constraints may be difficult to be represented because of mixed dimensions. Consider for instance a constraint “Base period = 1995” in the above example, where some observations include the base period in the Economic Indicator dimension, others in the Unit dimension. Instead of specifying a constraint on a pure Base Period dimension, the constraints may have to be specified at observation (or time series) level
--|(% style="width:360px" %)more flexible in terms of mapping to other data structures (used by other systems), further processing and analysis (e.g. tabulation, dissemination format), and future needs|(% style="width:1255px" %)“mixed” dimensions make data structure less flexible in these respects
--|(% style="width:360px" %)longer (i.e. more complex) observation keys|(% style="width:1255px" %)shorter keys
--|(% style="width:360px" %)special values of code lists such as “not applicable”, “total” may be rather heavily used|(% style="width:1255px" %)less usage of these special values
--|(% style="width:360px" %)creates sparse data if many observations use “not applicable”|(% style="width:1255px" %)way to avoid sparseness
--|(% style="width:360px" %)many constraints may be necessary due to sparseness|(% style="width:1255px" %)typically fewer constraints required because data are less sparse
--|(% style="width:360px" %)many dimensions are tantamount to many attachment levels for attributes (i.e. DSD more flexible in terms of attribute attachment)|(% style="width:1255px" %)less dimensions = less possible attribute attachment levels
--|(% style="width:360px" %)more difficult to handle by an end user|(% style="width:1255px" %)presumably more easily comprehensible and manageable by an end user
--|(% style="width:360px" %)more flexible in terms of defining queries; can be mapped to any “mixed” representation|(% style="width:1255px" %)less flexible in terms of search and retrieval
++|(% style="width:416px" %)shorter and simpler code lists|(% style="width:1199px" %)code lists longer and more complex, may require hierarchy to be “readable”
++|(% style="width:416px" %)more flexible in terms of defining constraints, but constraints more complex|(% style="width:1199px" %)simpler constraints, but some constraints may be difficult to be represented because of mixed dimensions. Consider for instance a constraint “Base period = 1995” in the above example, where some observations include the base period in the Economic Indicator dimension, others in the Unit dimension. Instead of specifying a constraint on a pure Base Period dimension, the constraints may have to be specified at observation (or time series) level
++|(% style="width:416px" %)more flexible in terms of mapping to other data structures (used by other systems), further processing and analysis (e.g. tabulation, dissemination format), and future needs|(% style="width:1199px" %)“mixed” dimensions make data structure less flexible in these respects
++|(% style="width:416px" %)longer (i.e. more complex) observation keys|(% style="width:1199px" %)shorter keys
++|(% style="width:416px" %)special values of code lists such as “not applicable”, “total” may be rather heavily used|(% style="width:1199px" %)less usage of these special values
++|(% style="width:416px" %)creates sparse data if many observations use “not applicable”|(% style="width:1199px" %)way to avoid sparseness
++|(% style="width:416px" %)many constraints may be necessary due to sparseness|(% style="width:1199px" %)typically fewer constraints required because data are less sparse
++|(% style="width:416px" %)many dimensions are tantamount to many attachment levels for attributes (i.e. DSD more flexible in terms of attribute attachment)|(% style="width:1199px" %)less dimensions = less possible attribute attachment levels
++|(% style="width:416px" %)more difficult to handle by an end user|(% style="width:1199px" %)presumably more easily comprehensible and manageable by an end user
++|(% style="width:416px" %)more flexible in terms of defining queries; can be mapped to any “mixed” representation|(% style="width:1199px" %)less flexible in terms of search and retrieval
  The latter two aspects mentioned in the table could be summarized as the “many pure dimensions” approach being more difficult to handle for a “basic” user, but providing fewer options for an “advanced” user. When it comes to dissemination to end users, a purer data structure is the appropriate format for consumption by applications and advanced users. For less advanced user groups it makes sense to hide the (for them: unnecessary) complexity by means of concatenating dimensions, for instance to create a time series view.