Skip to Content
Last modified by Artur K. on 2026/05/29 14:28
From version 1.2
edited by Helena K.
on 2026/01/15 12:12
Change comment: There is no comment for this version
To version 1.10
edited by Helena K.
on 2026/01/15 12:40
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -65,7 +65,9 @@
65 65  
66 66  ==== 2.1.3.1 Irrelevant concepts ====
67 67  
68 -Two options exist to deal with the situation of only a subset of dimensions being relevant[[(% class="wikiinternallink" %)^^~[1~]^^>>path:#_ftn1]](%%):
68 +Two options exist to deal with the situation of only a subset of dimensions being relevant{{footnote}}Technically speaking, a third possibility exists. A structure map can be used to define the reduced DSD. The
69 +structure map establishes a mapping between a source structure and a target structure. In this special case, the
70 +aim of the structure map is simply to get rid of irrelevant dimensions. To this end the DSD is mapped to itself, and any unmapped dimensions will not be part of the target structure. The original DSD is not affected by the structure map. The reduced DSD can be derived from the structure map, but from a technical point of view there is no need to actually create the reduced DSD as an artefact. It can exist as a “virtual” DSD that is merely defined by the Structure Map.{{/footnote}}:
69 69  
70 70  1. define a new, reduced concept scheme that includes only the relevant concepts and code lists by reference and a new DSD that uses the reduced concept scheme;
71 71  1. reuse concept scheme, code lists, and DSD, but add constraints to the data flow definition (or to the DSD, but this would also make it a new, derived DSD) that set the irrelevant dimensions to whatever applies from the following:
... ... @@ -73,7 +73,7 @@
73 73  11. If a concept is obsolete because only total values aggregated over the corresponding dimension are relevant, the dimension (or code list) should be restricted to a “total” item. For instance, an existing DSD on bilateral trade contains “Partner Country” as dimension, since data are collected with a breakdown by country of trade counterpart. The new data exchange disseminates similar data, but only the trade totals “vis-à-vis all countries”.
74 74  11. If a concept is not needed because it cannot even be relevant for the data at all or because an additional breakdown is just not available, the concept should be restricted to “not applicable” or “unknown” via constraints. For example, a financial instrument breakdown was not collected and it is unclear whether data for all or only for some financial instruments were included, that is whether the “total” value can be used. In this case, the dimension would be restricted to “unknown”. Consider another simple example of a DSD that contains, amongst others, the two dimensions “Unit of Measure” and “Base Period”. The code list for “Unit of Measure” consists of percent per annum, percentage, and index with base year=100. “Base Period” can contain dates, years, months, etc. If the new data exchange restricts “Unit of Measure” to percent per annum, “Base Period” becomes obsolete and should be constrained to “not applicable”.
75 75  
76 -**2.1.3.2 Missing concepts**
78 +==== 2.1.3.2 Missing concepts ====
77 77  
78 78  In this case additional concepts (and possibly code lists) are required, for example to accommodate an additional cross-classification. One option is to adapt the existing DSD to satisfy the new needs, i.e., create a new version of the DSD by adding the concepts, dimensions/attributes, and code lists. The feasibility of this solution depends on the relation between the organization requiring the new data structure and the organization maintaining the existing DSD, and the relation between the two usage contexts. The original, restricted model needed for the existing data flows can be specified by means of constraints, as described above for irrelevant concepts, or by referring to the original version of the DSD.
79 79  
... ... @@ -83,27 +83,22 @@
83 83  1. A code list similar to what is needed is available somewhere else.
84 84  11. If only a subset of the existing code list is relevant, the code list can be reused with a constraint imposed either on the code list, or in the DSD, or in the data flow definition (or in the data provisioning agreement). It is also possible to use the entire code list but only report data for the subset.
85 85  11. In case a (different) hierarchy is needed, the underlying flat code list can be referenced and a new hierarchical code list introduced. This means that a flat code list (i.e. without an explicitly defined hierarchy) is available that meets the coverage requirements, but that the existing hierarchy defined on top of the flat code list deviates from the required hierarchy. Hence, the suitable flat code list can be reused, but a new hierarchical code list needs to be defined. Consider for instance the “Reference Area” code list as recommended by the SDMX Content-Oriented Guidelines (COG), i.e. containing ISO-2-character codes for countries. Different groupings of these countries are relevant in different contexts, for example, regional aggregates by continent, by income level, or by membership in certain international groups (e.g. monetary unions). A flat code list can be defined that contains all these country groups in addition to the individual countries. This list does not specify parent-child relationships between groups and countries, as this would entail repeating countries for each group they belong to. It basically provides the value domain for a geographic dimension, but not the semantics of the values in terms of the group composition.
86 -
87 87  On top of this flat code list, different hierarchical code lists can be defined that may use the complete set of codes or just a subset thereof. The flat code list can be referenced by any DSD with a geographical reference, and different DSDs can build their own hierarchical code lists based on the flat list.
88 -
89 -1.
90 90  11. If additional items are needed, a derived code list can be specified by including each element from the existing code list by reference and adding the new elements as required. The current versions of the SDMX Technical Standard do not allow combining existing code lists into one or referencing an entire code list and adding a few elements to be managed in the new code list. Often, simply a copy of the existing list is introduced as new code list with the new items included. This is not optimal, as conceptually identical items have to be managed in multiple code lists. At least in theory it is also possible to just create a new version of the existing code list with the additional items. Existing data flows would then either use the original version of the code list or the new version with constraints, whereas the new version of the code list would be used in the new data flow. Again, this option depends on the organizational background.
91 -
92 92  Consider as an example the inclusion of “Currency” into a DSD with a need for codes for “Domestic currency” and “Foreign currency” in addition to the codes specified in the code list recommended by the SDMX COG. In the first option, the currencies from the recommended code list are included by reference and the two new items added to a new code list. This is superior to the common practice of including copies of the existing codes (the currencies) instead of references. This makes the new code list more independent of the existing one, but it increases the maintenance cost and the risk of inconsistencies. Another option is to extend the existing code list by creating a new code list version. In the currency example, the SDMX consortium as the owner of the recommended code list would need to decide whether this new version should be created or not.
93 -
94 94  1. No appropriate code lists are available. New code lists have to be defined based on the guidelines for the development of code lists. This may often be the case for domain-specific code lists, especially in new areas of investigation.
95 95  
96 -**2.1.3.3 Concepts in different roles**
93 +==== 2.1.3.3 Concepts in different roles ====
97 97  
98 98  In this case concepts are available in other roles than required, for example what needs to be a dimension is merely an attribute or vice versa. This case is already briefly discussed above as part of the first case (“irrelevant concepts”). Basically, a new DSD has to be defined. It can reuse the concept scheme and code lists, but specifies the concepts in the new DSD as dimensions or attributes as required. In case an attribute needs to become a dimension, it may be necessary to define a new code list for that dimension in case it did not exist previously.
99 99  
100 100  An example for an attribute having to be redefined as a dimension may be the “Unit of measure” that is frequently just specified as an attribute. If certain indicators are presented in different units in the same data flow, the corresponding DSD must contain “Unit of measure” as dimension, though.
101 101  
102 -**2.1.3.4 Different code lists**
99 +==== 2.1.3.4 Different code lists ====
103 103  
104 104  In this case the new requirements differ from the existing DSD with respect to some of the code lists, either by only a subset of codes being relevant, by a deviating hierarchical structure, or by necessitating additional codes. These three scenarios are discussed above as special cases of the “missing concepts” case. In theory, just defining a new code list whenever an existing one is not completely appropriate is also possible (but not desirable). However, this means that the overlapping items have to be managed in multiple code lists unless they are included by reference. Also, different DSDs have to be maintained. If the constraints are neither imposed at the code list nor at the DSD level, but at the data flow level, the DSD is simply reused. This is highly recommended. The cost of maintaining multiple DSDs or multiple, largely overlapping code lists can be high. The lack of harmonization has one advantage, though: the increased maintenance and versioning flexibility. For global data exchange, this is not regarded as a reasonable solution.
105 105  
106 -**2.1.3.5 Pure vs. mixed dimensions**
103 +==== 2.1.3.5 Pure vs. mixed dimensions ====
107 107  
108 108  The design principle of pure dimensions is explained in more detail in subsections 3.3 and section 4 on data structuring approaches. If an existing DSD does not have the desired degree of dimension purity, it is necessary to further decompose and/or combine dimensions of that DSD. This will lead to a new derived DSD and also requires the definition of new (combined or split) code lists, unless they are available from elsewhere.
109 109  
... ... @@ -131,9 +131,9 @@
131 131  
132 132  === 2.3.4 Density and sparseness ===
133 133  
134 -The //density// of a DSD is closely related to its simplicity whereas //sparseness// often comes along with purity. For a dense DSD, a data flow provides data for all (or the large majority of) cells defined by the Cartesian product[[(% class="wikiinternallink" %)^^~[2~]^^>>path:#_ftn2]](%%) of the DSD dimensions. This is typically the case for simple DSDs. For pure DSDs with many dimensions, it is usually not feasible to share data 338 for the entire data space created by the combination of all dimensions.
131 +The //density// of a DSD is closely related to its simplicity whereas //sparseness// often comes along with purity. For a dense DSD, a data flow provides data for all (or the large majority of) cells defined by the Cartesian product{{footnote}}A Cartesian product (or product set) is a mathematical construct that builds a new set out of a number of given sets. Each member of the Cartesian product corresponds to the selection of one element each in every one of the original sets.{{/footnote}} of the DSD dimensions. This is typically the case for simple DSDs. For pure DSDs with many dimensions, it is usually not feasible to share data 338 for the entire data space created by the combination of all dimensions.
135 135  
136 -For example, a breakdown by “Institutional Sector” or “Gender” may only make sense for a subset of the “Indicators” provided. The sparseness may be measured in terms of the number of dimensions requiring a “not applicable” value or the number of observations that take at least one “not applicable” or “total” value (both as shares of the total number of dimension or the total number of observations, respectively)[[(% class="wikiinternallink" %)^^~[3~]^^>>path:#_ftn3]](%%). An even more precise measure of sparseness is the proportion of theoretically possible key combinations that are irrelevant or not feasible or do not carry data.
133 +For example, a breakdown by “Institutional Sector” or “Gender” may only make sense for a subset of the “Indicators” provided. The sparseness may be measured in terms of the number of dimensions requiring a “not applicable” value or the number of observations that take at least one “not applicable” or “total” value (both as shares of the total number of dimension or the total number of observations, respectively){{footnote}}In case a structure map is used to define reduced versions of the DSD, the number of unmapped dimensions is the equivalent measure of sparseness.{{/footnote}}. An even more precise measure of sparseness is the proportion of theoretically possible key combinations that are irrelevant or not feasible or do not carry data.
137 137  
138 138  === 2.3.5 Unambiguousness ===
139 139  
... ... @@ -141,6 +141,8 @@
141 141  
142 142  **Table 1. Unambiguousness example – dimensions**
143 143  
141 +[[image:1768469016538-287.png]]
142 +
144 144  How would an observation of “Gross domestic product, volume, US dollars, reference year = 2005, millions” for the United States be represented with these dimensions? Table 2 provides three different possible representations (there may be even more).
145 145  
146 146  **Table 2. Unambiguousness example – ambiguous representations**
... ... @@ -192,7 +192,7 @@
192 192  
193 193  The global BOP DSD that is currently being developed may serve as a more specific example for a multi-purpose DSD. It is supposed to support, amongst others, exchange of the ECB's Balance of Payments (BOP) and International Reserves Template (IRT) data, Eurostat's International Investment Position (IIP) and Trade in Services (TS) data, the OECD's BOP data, and the IMF's Coordinated Portfolio Investment (CPIS) and Coordinated Direct Investment (CDIS) data.
194 194  
195 -Table 3 below shows some of the concepts considered relevant for some or all of these related data exchange exercises.[[(% class="wikiinternallink" %)^^~[4~]^^>>path:#_ftn4]](%%) Reporting Country and Unit of Measure are required by all data exchanges; the other concepts listed are only necessary (marked by an “X”) for a subset of the data exchanges. For instance, Eurostat's TS and IMF’s CDIS data do not require the distinction of flows and stocks, different maturities, or valuations (indicated by an “O”). Still, there is value in defining one master DSD that covers all concepts required for all of the data exchanges.
194 +Table 3 below shows some of the concepts considered relevant for some or all of these related data exchange exercises.{{footnote}}Please note that the example is taken from the development status of the BOP DSD at the time of writing this document. The concepts and their relevance for certain data exchanges (represented as data flows or derived DSDs) may be different in the final version of the DSD.{{/footnote}} Reporting Country and Unit of Measure are required by all data exchanges; the other concepts listed are only necessary (marked by an “X”) for a subset of the data exchanges. For instance, Eurostat's TS and IMF’s CDIS data do not require the distinction of flows and stocks, different maturities, or valuations (indicated by an “O”). Still, there is value in defining one master DSD that covers all concepts required for all of the data exchanges.
196 196  
197 197  If that approach is pursued, satellite DSDs for the individual purposes (or exchange exercises) can be created via constraints (and/or structure maps). Each exchange exercise may also be represented as a data flow (the constraints may also be defined in the data flow instead of the DSD). So there would be one data flow defined for each column in the table below. For instance, the IMF CPIS data flow would restrict “Flows and stocks indicator” and “Valuation” to certain values from the respective code lists. Data provision agreements may then be set up for each data flow with each reporting country. Constraints can be used to restrict the contribution of each country to its own data, so “Reporting country” would be set to the respective value. If the constraints are defined in the data flow and/or structure maps are used to exclude irrelevant dimensions, the satellite DSDs do not materialize; they are “virtual” DSDs.
198 198  
... ... @@ -200,13 +200,13 @@
200 200  
201 201  |(% rowspan="2" %)**Concept**|(% colspan="2" %)**ECB**|(% colspan="2" %)**Eurostat**|**OECD**|(% colspan="2" %)**IMF**
202 202  |**IRT**|**BOP**|**IIP**|**TS**|**BOP**|**CPIS**|**CDIS**
203 -|**Reporting country or area**|X|X|X|X|X|X|X
204 -|**Unit of measure**|X|X|X|X|X|X|X
205 -|**Flows and stocks indicator**|X|O|O|O|O|O|O
206 -|**Reporting sector**|X|X|X|O|X|X|X
207 -|**Financial instrument**|X|X|X|O|X|X|X
208 -|**Maturity**|X|X|X|O|X|X|O
209 -|**Valuation**|X|O|X|O|O|O|O
202 +|Reporting country or area|X|X|X|X|X|X|X
203 +|Unit of measure|X|X|X|X|X|X|X
204 +|Flows and stocks indicator|X|O|O|O|O|O|O
205 +|Reporting sector|X|X|X|O|X|X|X
206 +|Financial instrument|X|X|X|O|X|X|X
207 +|Maturity|X|X|X|O|X|X|O
208 +|Valuation|X|O|X|O|O|O|O
210 210  
211 211  == 3.4 Type of data exchange and recipient ==
212 212  
... ... @@ -246,50 +246,31 @@
246 246  
247 247  The range of options between the “//just one//”// //(mixed) and “//all component//” subject-matter dimensions approaches is subject to the comprehensiveness (i.e. size, coverage) of the data exchange that the DSD is being developed for. If using a “mixed dimensions” approach, rules for the composition of the mixed dimension(s) may be specified (e.g. concatenate concepts A, B, and C to get mixed dimension X), allowing their easy re-decomposition. In general composite dimensions should be avoided as previously recommended by the SDMX Technical Notes, but there are cases that suggest the usage of composite dimensions. Table 4 juxtaposes general pros and cons of the “//many pure concepts//” and “//fewer composite concepts//” approaches.
248 248  
249 -|**Many pure concepts**|**Few composite concepts**
250 -|cleaner data structure|(((
251 -Mixed dimensions may be composed inconsistently making the decomposition into purer concepts and code lists difficult
248 +**Table 4. General comparison of data structuring approaches**
252 252  
253 -(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.
254 -)))
250 +|(% style="width:360px" %)**Many pure concepts**|(% style="width:1255px" %)**Few composite concepts**
251 +|(% style="width:360px" %)cleaner data structure|(% style="width:1255px" %)(((
252 +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.
255 255  
256 -**Table 4. General comparison of data structuring approaches**
254 +[[image:1768469652632-803.png||height="106" width="352"]]
257 257  
258 -|(% rowspan="3" %)(((
259 259  
257 +)))
258 +|(% style="width:360px" %)shorter and simpler code lists|(% style="width:1255px" %)code lists longer and more complex, may require hierarchy to be “readable”
259 +|(% 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
260 +|(% 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
261 +|(% style="width:360px" %)longer (i.e. more complex) observation keys|(% style="width:1255px" %)shorter keys
262 +|(% 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
263 +|(% style="width:360px" %)creates sparse data if many observations use “not applicable”|(% style="width:1255px" %)way to avoid sparseness
264 +|(% style="width:360px" %)many constraints may be necessary due to sparseness|(% style="width:1255px" %)typically fewer constraints required because data are less sparse
265 +|(% 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
266 +|(% style="width:360px" %)more difficult to handle by an end user|(% style="width:1255px" %)presumably more easily comprehensible and manageable by an end user
267 +|(% 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
260 260  
261 -
262 -)))|**Economic Indicator**|**Unit**
263 -|Industrial production (2000=100)|Index
264 -|GDP real|US Dollars at 2005 prices
265 -
266 -shorter and simpler code lists code lists longer and more complex, may
267 -
268 -require hierarchy to be “readable”
269 -
270 -
271 -**Many pure concepts Few composite concepts**
272 -
273 -more flexible in terms of defining constraints, simpler constraints, but some constraints may
274 -
275 -but constraints more complex be difficult to be represented because of mixed
276 -
277 -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
278 -
279 -|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|“mixed” dimensions make data structure less flexible in these respects
280 -|longer (i.e. more complex) observation keys|shorter keys
281 -|special values of code lists such as “not applicable”, “total” may be rather heavily used|less usage of these special values
282 -|creates sparse data if many observations use “not applicable”|way to avoid sparseness
283 -|many constraints may be necessary due to sparseness|typically fewer constraints required because data are less sparse
284 -|many dimensions are tantamount to many attachment levels for attributes (i.e. DSD more flexible in terms of attribute attachment)|less dimensions = less possible attribute attachment levels
285 -|more difficult to handle by an end user|presumably more easily comprehensible and manageable by an end user
286 -|more flexible in terms of defining queries; can be mapped to any “mixed” representation|less flexible in terms of search and retrieval
287 -
288 -
289 -
290 290  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.
291 291  
292 -Comparing single-purpose and single-domain exchange scenarios with multi-domain and/or multi-purpose scenarios, pure concepts are typically easier to achieve in the former, whereas composite concepts/dimensions may make life easier in the latter, especially because certain cross-classification concepts may only apply to some domains and/or purposes covered. “Purpose” means either a certain data exchange exercise or data flow, for instance in the BOP DSD endeavor mentioned above each column represents one “purpose”, e.g. ECB IRT or OECD BOP. In multi-domain or –purpose scenarios, pure concepts are more easily obtained by a “many DSDs” approach, no matter if those are independent from each other or linked by a “master DSD”. Although it does not rule out the specification of pure concepts, a “one DSD” approach typically leads to using fewer, composite concepts (dimensions) in those scenarios.
271 +Comparing single-purpose and single-domain exchange scenarios with multi-domain and/or multi-purpose scenarios, pure concepts are typically easier to achieve in the former, whereas composite concepts/dimensions may make life easier in the latter, especially because certain cross-classification concepts may only apply to some domains and/or purposes covered. “Purpose” mean“mixed” dimensions make data structure less
272 +flexible in these respectsither a certain data exchange exercise or data flow, for instance in the BOP DSD endeavor mentioned above each column represents one “purpose”, e.g. ECB IRT or OECD BOP. In multi-domain or –purpose scenarios, pure concepts are more easily obtained by a “many DSDs” approach, no matter if those are independent from each other or linked by a “master DSD”. Although it does not rule out the specification of pure concepts, a “one DSD” approach typically leads to using fewer, composite concepts (dimensions) in those scenarios.
293 293  
294 294  Table 5 provides an overview of the pros and cons of the “many pure concepts" and “fewer composite concepts” approaches in different data exchange settings with respect to the type of organizations involved. In any of these settings it is always possible to use one of the data structures that may already exist at one of the involved parties as DSD for the data exchange. The benefits and drawbacks discussed in the table assume that a new DSD is to be defined. A distinction between two different types of intended recipients is implicitly made. Inter-organizational data exchange is mostly machine-to-machine, whereas dissemination of data to end-users is often machine-to-user.
295 295  
... ... @@ -298,18 +298,13 @@
298 298  |**Level of data exchange**|**Pure vs. composite concepts approach**
299 299  |**within an organization**|(((
300 300  Depends on diversity of systems involved in data exchange.
301 -
302 302  The approach that requires the least mapping (and similar processing) steps between the two communicating data structures is preferable in terms of a “quick win” solution.
303 -
304 304  In general, a more granular model is preferable due to its flexibility that helps support potential future needs (with respect to processing, analysis, exchange, dissemination, etc.).
305 -
306 306  However, an internal exchange should not be made more complex than necessary. If the structures of the communicating systems are comparable, it may not make sense to create an artificial intermediary structure that is more pure, but also more complex than both underlying structures.
307 -
308 308  Still, as a longer-term strategy it seems reasonable to define a set of internal “standard” code lists that all systems can map to. This allows bilateral communication via the shared concepts and code lists meaning that every data structure only has to be mapped once – to the internal standard – to be able to communicate with all other participating (i.e. mapped) systems.
309 309  )))
310 310  |**between organizations at national level**|(((
311 311  The pros and cons at this level of exchange are comparable to those at the “within organization” level. If the data structures of the communicating systems are comparable, there is no need to introduce complexity by a conceptually optimal, pure data structure. However, if the data structures deviate to a greater extent (and they often do), they should both be decomposed to find a “common denominator”, a more granular “exchange vocabulary” which they can be mapped to.
312 -
313 313  If related international or national standards exist, they should be used, even though national labels and/or additional levels of detail may be required in the code lists.
314 314  )))
315 315  |**between international organization and national organizations of member countries**|International organizations should collect data at a level of granularity and purity that is most suitable for the intended (and potential future) analyses. The tradeoff with the higher complexity of constraints required to check structural validity of collected data needs to be taken into account as well. Also it is recommended to consider the burden that a more complex data structure may put on national data providers. However, once a DSD is defined, its lifetime is expected to be a number of years. The main effort of the data provider is to specify the mapping from the production data structure to the DSD. Once this is done the data exchange can be automated and the complexity of the DSD does not matter that much.
... ... @@ -384,7 +384,6 @@
384 384  
385 385  in both cases: important to include only concepts, code lists, and codes actually available / used by the data
386 386  )))
387 -| | | | |
388 388  
389 389  In general, finding the “perfect” data structure is less important for bilateral data exchange. Independent, custom-tailored DSDs may do the job quite well, as harmonization and standardization are typically not of high importance. If the data exchange is just a part of a more comprehensive scenario (e.g. multi-purpose, multi-domain, gateway, or data-sharing scenarios), a master DSD with satellite DSDs is preferable.
390 390  
... ... @@ -409,7 +409,7 @@
409 409  
410 410  = 5 MINIMUM STRUCTURAL AND SEMANTIC REQUIREMENTS =
411 411  
412 -Although each data exchange scenario has specific requirements, especially on whether a concept needs to be a dimension, a mandatory or conditional attribute, on the attachment level of attributes, and on the attributes provided in the header of a DSD, a small set of minimum structural and semantic requirements can be defined for all scenarios.[[(% class="wikiinternallink" %)^^~[5~]^^>>path:#_ftn5]]
386 +Although each data exchange scenario has specific requirements, especially on whether a concept needs to be a dimension, a mandatory or conditional attribute, on the attachment level of attributes, and on the attributes provided in the header of a DSD, a small set of minimum structural and semantic requirements can be defined for all scenarios.{{footnote}}For other more technical requirements such as the admissible characters in a code or label see the SDMX technical documents.{{/footnote}}
413 413  
414 414  Certain concepts can be broadly agreed upon as being relevant in any data exchange, although their roles may differ between scenarios. The SDMX Content-Oriented Guidelines define many of these cross-domain concepts and, thus, should be referred to for further details on their specification.
415 415  
... ... @@ -478,10 +478,8 @@
478 478  
479 479  == 5.2 Attribute attachment levels and definition of groups ==
480 480  
481 -Each concept can only be used once as a dimension or an attribute in one DSD. Each attribute must be explicitly attached to an observation, series, or group. The attachment level
455 +Each concept can only be used once as a dimension or an attribute in one DSD. Each attribute must be explicitly attached to an observation, series, or group. The attachment level depends on whether the value of the attribute changes by observation, observation group, or time series, or is the same for all observations. In the latter case, the attribute has to be specified at the //data flow// or //dataset// level. For some attributes described in the previous section, a certain attachment level applies, for others the attachment level depends on the data. For example, the time series title has to be attached at the time series level and the observation status at the observation level.
482 482  
483 -depends on whether the value of the attribute changes by observation, observation group, or time series, or is the same for all observations. In the latter case, the attribute has to be specified at the //data flow// or //dataset// level. For some attributes described in the previous section, a certain attachment level applies, for others the attachment level depends on the data. For example, the time series title has to be attached at the time series level and the observation status at the observation level.
484 -
485 485  Series and groups are useful groupings of data that allow the specification of attributes for a set of observations instead of having to declare those attributes for every data point thereby. This increases the readability of an SDMX data file, reduces the size of the data file, and (in some cases) even increases the processing efficiency.
486 486  
487 487  Series is relevant for time series data only. It refers to a group of observations that differ only with respect to the time dimension, i.e. all dimensions except time define the series attachment level. The best-known example of a group definition is the sibling group that combines time series with different frequencies. Observations in a sibling group differ with respect to frequency and time; all other dimensions are used to define the sibling group. A sibling group can be regarded as a time series group with the frequency excluded from the group definition. Any other combination of dimensions (or a single dimension) can also be used to define an observation group. An example for a group defined by a single dimension is reporting country. For instance, attributes related to methodology are often the same for all data of a country. In order to attach attributes to a group, a name for that group has to be specified.
... ... @@ -511,25 +511,36 @@
511 511  
512 512  Figure 1 provides an overview of the overall process. As a first step, the context of the data exchange(s) that should be covered by the DSD(s) is defined in terms of purpose, domains, level of exchange, type of data, type of recipient, role of in data exchange, process pattern, and GSBPM phase (see Figure 2). Since reusing existing artefacts is one of the guiding principles, the second step identifies existing DSDs that may be reused (see Figure 3). In case relevant DSDs are available, their suitability in the present context is evaluated in step 3. Aspects to be taken into account are concept coverage, concept roles, attribute attachment levels, and code lists (see Figure 4). Step 4 is subject to the outcome of step 3. In case of a favorable assessment, the DSDs are simply reused. If the DSDs are partly suitable, modified versions can be derived. See section 2. for a summary of possible DSD modification scenarios. If the DSDs are not suitable or if no relevant DSDs are available at all, new DSDs will be defined as described in section 3. Finally, supporting artefacts such as data flow definitions and data provision agreements are defined (see Figure 5).
513 513  
514 -==== Figure 1. Overview of the DSD design process ====
515 515  
487 +(% class="wikigeneratedid" id="HFigure1.OverviewoftheDSDdesignprocess" %)
488 +Figure 1. Overview of the DSD design process
489 +
490 +
516 516  Figure 2 summarizes the characteristics of the data exchange context that is defined in step 1. These characteristics affect the decision on the data structuring approach that is part of the process of defining the concepts of a new DSD (step 4.3. in Figure 1; see Figure 7 in section 2.).
517 517  
518 518  
519 -==== Figure 2. Characteristics of data exchange context ====
494 +(% class="wikigeneratedid" id="HFigure2.Characteristicsofdataexchangecontext" %)
495 +Figure 2. Characteristics of data exchange context
520 520  
521 521  Figure 3 recaps the priorities given to different types of existing DSDs when searching for candidates for reuse in step 2. Global DSDs maintained by the SDMX consortium are ranked the highest. They can be found via the Global SDMX Registry.
522 522  
523 -==== Figure 3. Priority ranking of existing DSDs for reuse ====
524 524  
500 +(% class="wikigeneratedid" id="HFigure3.PriorityrankingofexistingDSDsforreuse" %)
501 +Figure 3. Priority ranking of existing DSDs for reuse
502 +
503 +
525 525  Figure 4 summarizes the aspects to be considered in the assessment of the suitability of existing DSDs in step 3. For a detailed description of the cases of partial unsuitability see section 2.1. above.
526 526  
527 -==== Figure 4. Aspects of DSD suitability ====
528 528  
507 +(% class="wikigeneratedid" id="HFigure4.AspectsofDSDsuitability" %)
508 +Figure 4. Aspects of DSD suitability
509 +
510 +
529 529  Figure 5 lists the most relevant artefacts required in addition to a DSD, its concept scheme, and code lists.
530 530  
531 -**Figure 5. Supporting artefacts**
532 532  
514 +Figure 5. Supporting artefacts
515 +
533 533  == 6.2 Defining modified DSDs ==
534 534  
535 535  Figure 6 briefly recapitulates the actions that can be taken to overcome partial unsuitability of DSDs. As far as possible, existing artefacts should be reused in this case. This means that even if a DSD cannot be reused as a whole, concepts and code lists from that DSD can be included in the new DSD by reference.
... ... @@ -559,19 +559,27 @@
559 559  The second step in the process of defining a new DSD is the specification of code lists for all coded concepts. All dimensions must be coded (with time being an exception to this rule); attributes may be coded. For uncoded concepts, a data format has to be specified. Existing formats may be reused or new ones defined. An example is the time format that is specified in the SDMX COG. Figure 13 illustrates the code list specification process. If no relevant and suitable code list exists, a new one will be defined or a partially suitable one will be adapted (see Figure 16). Suitable code lists can simply be reused via reference.
560 560  
561 561  
562 -==== Figure 13. Code list specification process ====
545 +(% class="wikigeneratedid" id="HFigure13.Codelistspecificationprocess" %)
546 +Figure 13. Code list specification process
563 563  
548 +
564 564  Figure 14 recaps the priorities given to different types of existing code lists when searching for candidates for reuse (step 4.3.2.1.). Code lists recommended by the SDMX COG (and maintained by the SDMX consortium) are ranked the highest.
565 565  
566 -==== Figure 14. Priority ranking of existing code lists for reuse ====
567 567  
552 +(% class="wikigeneratedid" id="HFigure14.Priorityrankingofexistingcodelistsforreuse" %)
553 +Figure 14. Priority ranking of existing code lists for reuse
554 +
555 +
568 568  Figure 15 summarizes the aspects to be considered in the evaluation of the suitability of existing code lists (step 4.3.2.2.). Figure 16 summarizes the scenarios of adapting existing code lists that do not fully meet the specified needs (step 4.3.2.3.2). For a detailed description of the cases of partial unsuitability see section 2.1. above.
569 569  
570 570  
571 -==== Figure 15. Aspects of code list suitability ====
559 +(% class="wikigeneratedid" id="HFigure15.Aspectsofcodelistsuitability" %)
560 +Figure 15. Aspects of code list suitability
572 572  
573 -==== Figure 16. Code list modification scenarios ====
574 574  
563 +(% class="wikigeneratedid" id="HFigure16.Codelistmodificationscenarios" %)
564 +Figure 16. Code list modification scenarios
565 +
575 575  If new code lists need to be defined, please refer to the SDMX Guidelines for the creation of code lists. Basically, code, name, and definition need to be provided for each item in the code list. In addition, hierarchical code lists may be defined.
576 576  
577 577  The final step in defining a new DSD covers the assembly of all components specified during the process, i.e. concept scheme(s), code lists, data formats, concept roles, attribute attachment levels, and the assignment of code lists and data formats to concepts.
... ... @@ -580,10 +580,10 @@
580 580  
581 581  Figure 17 provides an overview of all steps in the DSD design process as described in the previous subsections 1. to 3. Figure 18 compiles those steps into a checklist for DSD designers to help them make sure all aspects are considered.
582 582  
583 -**Figure 17. DSD design process**
574 +Figure 17. DSD design process
584 584  
585 585  
586 -**Figure 18. Checklist for DSD design process**
577 +Figure 18. Checklist for DSD design process
587 587  
588 588  = 7 ANNEX 1. GLOSSARY OF TERMS =
589 589  
... ... @@ -595,7 +595,7 @@
595 595  
596 596  Concepts assume different roles in a data structure definition:
597 597  
598 -* //dimensions// are required to uniquely identify an observation (a data value); e.g., for time series, at least one geographic, one temporal, and one (“mixed") subject-matter dimension are required to identify a data value (for instance: reference area = Mexico, time = 2002, indicator = GDP nominal, US$)[[(% class="wikiinternallink" %)^^~[6~]^^>>path:#_ftn6]](%%);
589 +* //dimensions// are required to uniquely identify an observation (a data value); e.g., for time series, at least one geographic, one temporal, and one (“mixed") subject-matter dimension are required to identify a data value (for instance: reference area = Mexico, time = 2002, indicator = GDP nominal, US$){{footnote}}Please note that this is not a recommendation to always have three dimensions only. This is just a simplified example.{{/footnote}};
599 599  * //measures// are the containers of the actual observation or data values;
600 600  * //attributes// provide additional meta-information required to interpret the data correctly but not to identify the observations; for instance, data for the same observation defined by a value combination of the dimensions (also termed “key”) will usually only be provided for one unit multiplier, e.g. in millions; hence unit multiplier is not necessary to identify an observation, but it is still required for a proper interpretation. Attributes can be defined as mandatory or not mandatory, and they can be attached at different levels, e.g. at observation level or at the level of groups defined by the value combinations of a predefined subset of dimensions (for example reporting currency may be attached at the country level).
601 601  
... ... @@ -635,17 +635,6 @@
635 635  
636 636  METIS: Generic Statistical Business Process Model (GSBPM). Available online at http:~/~/www1.unece.org/stat/platform/display/metis/The+Generic+Statistical+Business+Process+Model. UN's System of National Accounts Manual 2008 (SNA2008). Available online at http:~/~/unstats.un.org/unsd/nationalaccount/sna2008.asp.
637 637  
638 -
639 639  ----
640 640  
641 -[[~[1~]>>path:#_ftnref1]] Technically speaking, a third possibility exists. A structure map can be used to define the reduced DSD. The structure map establishes a mapping between a source structure and a target structure. In this special case, the aim of the structure map is simply to get rid of irrelevant dimensions. To this end the DSD is mapped to itself, and any unmapped dimensions will not be part of the target structure. The original DSD is not affected by the structure map. The reduced DSD can be derived from the structure map, but from a technical point of view there is no need to actually create the reduced DSD as an artefact. It can exist as a “virtual” DSD that is merely defined by the Structure Map.
642 -
643 -[[~[2~]>>path:#_ftnref2]] A Cartesian product (or product set) is a mathematical construct that builds a new set out of a number of given sets. Each member of the Cartesian product corresponds to the selection of one element each in every one of the original sets.
644 -
645 -[[~[3~]>>path:#_ftnref3]] In case a structure map is used to define reduced versions of the DSD, the number of unmapped dimensions is the equivalent measure of sparseness.
646 -
647 -[[~[4~]>>path:#_ftnref4]] Please note that the example is taken from the development status of the BOP DSD at the time of writing this document. The concepts and their relevance for certain data exchanges (represented as data flows or derived DSDs) may be different in the final version of the DSD.
648 -
649 -[[~[5~]>>path:#_ftnref5]] For other more technical requirements such as the admissible characters in a code or label see the SDMX technical documents.
650 -
651 -[[~[6~]>>path:#_ftnref6]] Please note that this is not a recommendation to always have three dimensions only. This is just a simplified example.
631 +{{putFootnotes/}}
1768468989793-901.png
Author
... ... @@ -1,0 +1,1 @@
1 +xwiki:XWiki.helena
Size
... ... @@ -1,0 +1,1 @@
1 +52.9 KB
Content
1768469016538-287.png
Author
... ... @@ -1,0 +1,1 @@
1 +xwiki:XWiki.helena
Size
... ... @@ -1,0 +1,1 @@
1 +42.6 KB
Content
1768469652632-803.png
Author
... ... @@ -1,0 +1,1 @@
1 +xwiki:XWiki.helena
Size
... ... @@ -1,0 +1,1 @@
1 +10.7 KB
Content
© Semantic R&D Group, 2026