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... ... @@ -247,22 +247,24 @@
247 247  
248 248  **Table 4. General comparison of data structuring approaches**
249 249  
250 -|(% style="width:416px" %)**Many pure concepts**|(% style="width:1199px" %)**Few composite concepts**
251 -|(% style="width:416px" %)cleaner data structure|(% style="width:1199px" %)(((
250 +|(% style="width:360px" %)**Many pure concepts**|(% style="width:1255px" %)**Few composite concepts**
251 +|(% style="width:360px" %)cleaner data structure|(% style="width:1255px" %)(((
252 252  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.
253 253  
254 254  [[image:1768469652632-803.png||height="106" width="352"]]
255 +
256 +
255 255  )))
256 -|(% style="width:416px" %)shorter and simpler code lists|(% style="width:1199px" %)code lists longer and more complex, may require hierarchy to be “readable”
257 -|(% 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
258 -|(% 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
259 -|(% style="width:416px" %)longer (i.e. more complex) observation keys|(% style="width:1199px" %)shorter keys
260 -|(% 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
261 -|(% style="width:416px" %)creates sparse data if many observations use “not applicable”|(% style="width:1199px" %)way to avoid sparseness
262 -|(% style="width:416px" %)many constraints may be necessary due to sparseness|(% style="width:1199px" %)typically fewer constraints required because data are less sparse
263 -|(% 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
264 -|(% style="width:416px" %)more difficult to handle by an end user|(% style="width:1199px" %)presumably more easily comprehensible and manageable by an end user
265 -|(% 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
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
266 266  
267 267  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.
268 268  
... ... @@ -296,14 +296,15 @@
296 296  
297 297  **Table 6. Data structuring approaches by role in data exchange**
298 298  
299 -|(% style="width:215px" %)**Role in data exchange**|(% style="width:1400px" %)**Pure vs. composite concepts approach**
300 -|(% style="width:215px" %)**Data provider**|(% style="width:1400px" %)(((
301 +|**Role in data exchange**|**Pure vs. composite concepts approach**
302 +|**Data provider**|(((
301 301  If the composition of the concepts in the data provider's production system largely differs from the one in the DSD, mapping it to a few composite concepts may be more complex than mapping it to many pure concepts. (Mapping to just one mixed concept is straightforward, though.) This is due to the need to decompose and recombine concepts in case of a “mixed concepts” DSD. If the data provider’s internal data structure is very granular or very similar to the DSD, it does not make a huge difference if the concepts in that DSD are pure or not.
304 +
302 302  For a “final” data provider disseminating data to the public, the flexibility offered by a pure data structure in terms of defining different output formats may be beneficial.
303 303  )))
304 -|(% style="width:215px" %)**Data collector**|(% style="width:1400px" %)Defining constraints for data validation is more complex for a highdimensional, pure DSD. But such a DSD provides more flexibility in terms of consumption and reuse, i.e. mapping to the data collector’s internal data model mapping easier.
305 -|(% style="width:215px" %)**DSD maintenance**|(% style="width:1400px" %)Pure concepts usually have shorter, less complex code lists and are thus easier to maintain. In contrast, the maintenance of constraints, hierarchical code lists, and derived, composite concepts (e.g. for dissemination) requires more effort.
306 -|(% style="width:215px" %)**End user (“the public”)**|(% style="width:1400px" %)Consumption and reuse are more flexible in a pure data structure, but it is more difficult to identify observation keys that actually have data because of the created sparseness. (Constraints may help in this respect.) Frequent occurrences of “non applicable” values may also make data usage cumbersome.
307 +|**Data collector**|Defining constraints for data validation is more complex for a highdimensional, pure DSD. But such a DSD provides more flexibility in terms of consumption and reuse, i.e. mapping to the data collector’s internal data model mapping easier.
308 +|**DSD maintenance**|Pure concepts usually have shorter, less complex code lists and are thus easier to maintain. In contrast, the maintenance of constraints, hierarchical code lists, and derived, composite concepts (e.g. for dissemination) requires more effort.
309 +|**End user (“the public”)**|Consumption and reuse are more flexible in a pure data structure, but it is more difficult to identify observation keys that actually have data because of the created sparseness. (Constraints may help in this respect.) Frequent occurrences of “non applicable” values may also make data usage cumbersome.
307 307  
308 308  == 4.2 Number and relations of DSDs ==
309 309  
... ... @@ -325,22 +325,36 @@
325 325  
326 326  **Table 7. Data structuring approaches by level of data exchange**
327 327  
328 -|(% colspan="1" rowspan="2" %)**Level of data exchange**|(% colspan="4" rowspan="1" %)**Data structuring approach**
329 -|**one DSD**|(% colspan="2" %)**master + satellite DSDs**|**multiple, indep. DSDs**
331 +|**Level of data exchange**|**Data structuring approa one DSD**|(% colspan="2" %)(((
332 +**ch**
333 +
334 +**master + satellite DSDs**
335 +)))|**multiple, indep. DSDs**
330 330  |**within organization**|(((
331 -best for single-domain, single-purpose can be created on the fly from structured databases
337 +best for single-domain, single-purpose can be created on the
338 +
339 +fly from structured databases
332 332  )))|(% colspan="2" %)use if harmonization is important in covered domains or purposes or if such a set of DSDs is already available at international level|easier to do than master + satellite approach each domain/purpose can maintain DSDs independently can be created on the fly from structured databases
333 333  |**between national organizations**|(% colspan="4" %)the same applies as to the “within organization” scenario
342 +|**Level of data exchange**|(% colspan="3" %)(((
343 +**Data structuring approach**
344 +
345 +**one DSD master + satellite DSDs**
346 +)))|**multiple, indep. DSDs**
334 334  |**between int. organization and national organizations**|(% colspan="2" %)best for single domain, single purpose scenarios that are usually rather restricted with very clear specification of what needs to be exchanged|preferable over multiDSD approach in case of multi-domain and/or multi-purpose scenarios with highly correlated data flows for maintenance reasons|(((
335 -for multi-domain and/or multipurpose scenarios; only recommended if overlap of domains/purposes is minor (e.g. just w.r.t. cross-domain concepts) equivalent to multiple “one DSD” solutions, one for each domain / purpose
348 +for multi-domain and/or multipurpose scenarios; only recommended if overlap of domains/purposes is minor (e.g. just w.r.t. cross-domain concepts)
349 +
350 +equivalent to multiple “one DSD” solutions, one for each domain / purpose
336 336  )))
337 337  |**between international organizations**|(% colspan="3" %)comparable to “national to international” scenario|
338 338  |**dissemination to public**|(% colspan="2" %)for single-domain, single-purpose cases in more complex cases this may be the preferable approach for data discovery tools (one data structure to find and access all data)|(% colspan="2" %)(((
339 339  in multi-purpose or –domain scenarios:
340 340  
341 -* if it is relevant for the public to see the relationship between the data structures: use master + satellites approach
342 -* otherwise the multi-DSD option is preferable, although with the highest possible degree of re-use of code lists and concepts
343 -* in both cases: important to include only concepts, code lists, and codes actually available / used by the data
356 +if it is relevant for the public to see the relationship between the data structures: use master + satellites approach
357 +
358 +otherwise the multi-DSD option is preferable, although with the highest possible degree of re-use of code lists and concepts
359 +
360 +in both cases: important to include only concepts, code lists, and codes actually available / used by the data
344 344  )))
345 345  
346 346  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.
... ... @@ -349,17 +349,20 @@
349 349  
350 350  **Table 8. Data structuring approaches by role in data exchange**
351 351  
352 -|(% style="width:216px" %)**Role in data exchange**|(% style="width:1399px" %)**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
353 -|(% style="width:216px" %)**Data provider**|(% style="width:1399px" %)It is easier to set up a data submission process against a single DSD (= less initial costs) than against multiple DSDs.
354 -|(% style="width:216px" %)**Data collector**|(% style="width:1399px" %)(((
369 +|**Role in data exchange**|**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
370 +|**Data provider**|It is easier to set up a data submission process against a single DSD (= less initial costs) than against multiple DSDs.
371 +|**Data collector**|(((
355 355  Data validation is easier with DSDs that only cover what needs to be collected. This is achieved via constraints in the master + satellites approach or via tailor-made independent DSDs. If a single DSD is used in a multi-domain or –purpose scenario, necessary constraints can be specified in the data flow definition or data provision agreement.
373 +
356 356  Further processing of collected data is more flexible and easier if relations are transparent and code lists are shared as in the one DSD or master + satellite DSDs approaches. The “shared context” created through the master DSD increases harmonization and standardization and this way facilitates combined usage of data.
357 357  )))
358 -|(% style="width:216px" %)**DSD maintenance**|(% style="width:1399px" %)(((
376 +|**Role in data exchange**|**One DSD vs. master + satellite DSDs vs. multiple, indep. DSDs**
377 +|**DSD maintenance**|(((
359 359  The complexity and initial costs for developing and maintaining master + satellite DSDs are higher than for independent DSDs as this involves managing constraints and managing impacts of changes in shared code lists to all DSDs.
379 +
360 360  In the multiple independent DSDs approach, development and maintenance efforts may be distributed. This can be seen as an advantage, but on the other hand requires coordination in case the DSDs are only partially independent (i.e. share some code lists).
361 361  )))
362 -|(% style="width:216px" %)**End user (“the public”)**|(% style="width:1399px" %)For data discovery and retrieval the user needs to know what data is actually available (instead of what might be collected/disseminated with a certain data structure). This means that the potential sparseness should be hidden from the user. A reduced DSD derived from the data structure used in the background is more useful in most cases. Whether this is done via one DSD and constraints, master + satellite DSDs, or independent DSDs does not matter that much for the user.
382 +|**End user (“the public”)**|For data discovery and retrieval the user needs to know what data is actually available (instead of what might be collected/disseminated with a certain data structure). This means that the potential sparseness should be hidden from the user. A reduced DSD derived from the data structure used in the background is more useful in most cases. Whether this is done via one DSD and constraints, master + satellite DSDs, or independent DSDs does not matter that much for the user.
363 363  
364 364  = 5 MINIMUM STRUCTURAL AND SEMANTIC REQUIREMENTS =
365 365  
... ... @@ -389,19 +389,19 @@
389 389  
390 390  **Table 9. Minimum requirements for DSDs~*~***
391 391  
392 -|(% style="width:205px" %)**Question**|(% style="width:272px" %)**Concept**|(% style="width:178px" %)**COG**|(% style="width:270px" %)**Code list**|(% style="width:690px" %)**Time series Cross-section**
393 -|(% style="width:205px" %)Where?|(% style="width:272px" %)reference area|(% style="width:178px" %)X|(% style="width:270px" %)revision|(% style="width:690px" %)mand. attribute or dimension
394 -|(% style="width:205px" %)What?|(% style="width:272px" %)“indicator”|(% style="width:178px" %)-|(% style="width:270px" %)domain|(% style="width:690px" %)one or multiple dimensions
395 -|(% style="width:205px" %)How?|(% style="width:272px" %)unit of measure|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:690px" %)mand. attribute or dimension
396 -|(% style="width:205px" %)How?|(% style="width:272px" %)unit multiplier|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mandatory attribute
397 -|(% style="width:205px" %)How?|(% style="width:272px" %)decimals|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mandatory attribute
398 -|(% style="width:205px" %)How?|(% style="width:272px" %)//adjustment//|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:690px" %)mand. att. not relevant
399 -|(% style="width:205px" %)When?|(% style="width:272px" %)time period|(% style="width:178px" %)X|(% style="width:270px" %)format|(% style="width:690px" %)dimension mand. att.
400 -|(% style="width:205px" %)When?|(% style="width:272px" %)time format|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mandatory attribute
401 -|(% style="width:205px" %)When?|(% style="width:272px" %)time period – collection|(% style="width:178px" %)X|(% style="width:270px" %)development|(% style="width:690px" %)mand. att. cond. att.
402 -|(% style="width:205px" %)When?|(% style="width:272px" %)data update – last update|(% style="width:178px" %)X|(% style="width:270px" %)time stamp|(% style="width:690px" %)mandatory attribute
403 -|(% style="width:205px" %)How often?|(% style="width:272px" %)//frequency//|(% style="width:178px" %)X|(% style="width:270px" %)available|(% style="width:690px" %)mand. att. or not relevant
404 -|(% colspan="2" style="width:477px" %)How much? observation value|(% style="width:178px" %)-|(% style="width:270px" %)numeric|(% style="width:690px" %)dimension measure
412 +|**Question**|**Concept**|**COG**|**Code list**|**Time series Cross-section**
413 +|Where?|reference area|X|revision|mand. attribute or dimension
414 +|What?|“indicator”|-|domain|one or multiple dimensions
415 +|How?|unit of measure|X|development|mand. attribute or dimension
416 +|How?|unit multiplier|X|available|mandatory attribute
417 +|How?|decimals|X|available|mandatory attribute
418 +|How?|//adjustment//|X|development|mand. att. not relevant
419 +|When?|time period|X|format|dimension mand. att.
420 +|When?|time format|X|available|mandatory attribute
421 +|When?|time period – collection|X|development|mand. att. cond. att.
422 +|When?|data update – last update|X|time stamp|mandatory attribute
423 +|How often?|//frequency//|X|available|mand. att. or not relevant
424 +|(% colspan="2" %)How much? observation value|-|numeric|dimension measure
405 405  
406 406  ~*~*Concepts in //italics// are only relevant for time series DSDs. An “X” in the COG column means the concept is defined in the COG. Code list “development” means that the SWG will develop a code list to be recommended in the COG; “revision” means that the code list is recommended by the COG and under revision by the SWG; “format” means that a format is defined by another concept; “text”, “time stamp”, and “numeric” provide data types used for uncoded concepts.
407 407  
... ... @@ -409,19 +409,25 @@
409 409  
410 410  **Table 10. Suggested additional concepts for certain scenarios~*~***
411 411  
412 -|**Question**|**Concept**|**COG**|**Code list**|**TS**|**CS**|**Scenario**
432 +|**Question**|**Concept**|**COG**|**Code list**|**TS CS**|**Scenario**
413 413  |Who?|compiling agency|X|development|(((
414 -conditional (sibling)
415 -)))|conditional (obs. level)|data provider different from data compiler
434 +conditional conditional
435 +
436 + (sibling) (obs. level)
437 +)))|data provider different from data compiler
416 416  |Who?|(((
417 -confidentiality status – observation
418 -)))|X|available|(% colspan="2" rowspan="1" %)mandatory (obs. level)|except dissemination
419 -|How?|observation status|X|available|(% colspan="2" rowspan="1" %)conditional (obs. level)|except orig. collection
439 +confidentiality
440 +
441 +status – observation
442 +)))|X|available|mandatory (obs. level)|except dissemination
443 +|How?|observation status|X|available|conditional (obs. level)|except orig. collection
420 420  |How much?|(((
421 -//observation pre-break value//
422 -)))|-|numeric|cond. (obs.)|not relevant|except orig. collection
423 -|What and how?|//time series title//|X|text|cond. (TS)|not relevant|dissemination
445 +//observation pre-//
424 424  
447 +//break value//
448 +)))|-|numeric|cond. (obs.) not relevant|except orig. collection
449 +|What and how?|//time series title//|X|text|cond. (TS) not relevant|dissemination
450 +
425 425  ~** The legend of Table 9 applies to Table 10 as well. The suggested attachment level of attributes (if any) is provided in parentheses in the TS (time series) or CS (cross-section) columns. In case an attribute does not vary at that level in a certain use case, it should be attached at the highest possible level.
426 426  
427 427  == 5.2 Attribute attachment levels and definition of groups ==
... ... @@ -443,8 +443,10 @@
443 443  * //ID//: a unique identifier of the message
444 444  * //Test//: a Boolean attribute that indicates whether the message is for test purposes or not
445 445  * //Prepared//: the date the message was prepared
446 -* //Sender//: the identification of the organization that is transmitting the message (recommended: code from the agency code list in the SDMX COG)
472 +* //Sender//: the identification of the organization that is transmitting the message
447 447  
474 +(recommended: code from the agency code list in the SDMX COG)
475 +
448 448  From a business perspective, the inclusion of the //Name// element is highly recommended, as it can help to understand the purpose of the exchange message. Other header elements such as //Receiver// are optional.
449 449  
450 450  = 6 STEP-BY-STEP GUIDE =
... ... @@ -455,15 +455,13 @@
455 455  
456 456  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).
457 457  
458 -(% class="wikigeneratedid" %)
459 -[[image:1768470533088-795.png]]
460 460  
461 461  (% class="wikigeneratedid" id="HFigure1.OverviewoftheDSDdesignprocess" %)
462 462  Figure 1. Overview of the DSD design process
463 463  
490 +
464 464  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.).
465 465  
466 -[[image:1768470575978-226.png]]
467 467  
468 468  (% class="wikigeneratedid" id="HFigure2.Characteristicsofdataexchangecontext" %)
469 469  Figure 2. Characteristics of data exchange context
... ... @@ -470,23 +470,20 @@
470 470  
471 471  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.
472 472  
473 -(% class="wikigeneratedid" %)
474 -[[image:1768470596130-305.png]]
475 475  
476 476  (% class="wikigeneratedid" id="HFigure3.PriorityrankingofexistingDSDsforreuse" %)
477 477  Figure 3. Priority ranking of existing DSDs for reuse
478 478  
503 +
479 479  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.
480 480  
481 -(% class="wikigeneratedid" %)
482 -[[image:1768470626558-321.png]]
483 483  
484 484  (% class="wikigeneratedid" id="HFigure4.AspectsofDSDsuitability" %)
485 485  Figure 4. Aspects of DSD suitability
486 486  
510 +
487 487  Figure 5 lists the most relevant artefacts required in addition to a DSD, its concept scheme, and code lists.
488 488  
489 -[[image:1768470646456-652.png]]
490 490  
491 491  Figure 5. Supporting artefacts
492 492  
... ... @@ -494,83 +494,48 @@
494 494  
495 495  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.
496 496  
497 -[[image:1768470678965-391.png]]
520 +**Figure 6. DSD modification scenarios**
498 498  
499 -Figure 6. DSD modification scenarios
500 -
501 501  == 6.3 Defining new DSDs ==
502 502  
503 503  In case no (suitable) DSD is available, the actual process of specifying a new DSD is started. Figure 7 depicts this process (step 4.3. in Figure 1). It encompasses the specification of concepts, code lists, and data formats. All three specification steps include the identification of already existing artefacts that could be reused or modified to satisfy the requirements at hand and the definition of new artefacts in case no suitable artefacts are detected. Several iterations of steps 1 (specification of concepts; see Figure 8) and 2 (specification of code lists; see Figure°13) may be necessary, including revisions of the decision concerning the data structuring approach. Finally all artefacts defined in the previous steps are put together into a DSD.
504 504  
505 -(% class="wikigeneratedid" %)
506 -[[image:1768470705894-724.png]]
526 +==== Figure 7. New DSD specification process ====
507 507  
508 -(% class="wikigeneratedid" id="HFigure7.NewDSDspecificationprocess" %)
509 -Figure 7. New DSD specification process
510 -
511 511  Figure 8 outlines step 4.3.1, the process of concept specification. It covers the decision on the structuring approach, the identification of relevant concepts and the assessment of their suitability, the definition of new concepts, concept roles, and attribute attachment levels.
512 512  
513 -(% class="wikigeneratedid" %)
514 -[[image:1768470729899-225.png]]
530 +==== Figure 8. Concept specification process ====
515 515  
516 -(% class="wikigeneratedid" id="HFigure8.Conceptspecificationprocess" %)
517 -Figure 8. Concept specification process
518 -
519 519  Both, the decision on reuse of existing concepts as well as the definition of new ones, may lead back to a revision of the data structuring approach. For example, it could turn out that a certain concept needs to be broken down further which may lead from a “few composite dimensions” to a “many pure dimensions” approach. Figure 9 provides the design options involved in the decision on a data structuring approach. The options are defined in terms of the number of DSDs and the number of concepts (especially dimensions). The reasonability and feasibility of these options depend on the context of the present data exchange(s) as defined in the first step of the overall design process and on the content of the data exchange with respect to concepts.
520 520  
521 -(% class="wikigeneratedid" %)
522 -[[image:1768470752201-691.png]]
534 +==== Figure 9. DSD design options ====
523 523  
524 -(% class="wikigeneratedid" id="HFigure9.DSDdesignoptions" %)
525 -Figure 9. DSD design options
526 -
527 527  In the second step of new DSD design, relevant existing concepts are identified. Figure 10 indicates potential sources of those concepts such as the SDMX COG for cross-domain concepts, global or other DSDs as already identified earlier in the process, and domain standards such as the UN's System of National Accounts Manual 2008 for domain-specific concepts.
528 528  
529 -(% class="wikigeneratedid" %)
530 -[[image:1768470775109-874.png]]
538 +==== Figure 10. Potential sources of concepts and definitions ====
531 531  
532 -(% class="wikigeneratedid" id="HFigure10.Potentialsourcesofconceptsanddefinitions" %)
533 -Figure 10. Potential sources of concepts and definitions
534 -
535 535  The definition of new concepts (step 4.3.1.4.2.) is necessary if no (suitable) concept can be reused. It entails giving each concept a name, a code, and a definition. Further details about the usage of the concepts in the DSD are specified in steps 4.3.1.5. (concept roles), 4.3.1.6. (dimension groups), and 4.3.1.7. (attribute attachment levels). Figure 11 and 12 summarize the possible concept roles and attribute attachment levels.
536 536  
537 537  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.
538 538  
539 -[[image:1768470796725-270.png]]
540 540  
541 -(% class="wikigeneratedid" %)
542 -Figure 11. Possible concept roles
543 -
544 -(% class="wikigeneratedid" %)
545 -[[image:1768470829131-599.png]]
546 -
547 -(% class="wikigeneratedid" %)
548 -Figure 12. Possible attribute attachment levels
549 -
550 -(% class="wikigeneratedid" %)
551 -[[image:1768470860119-204.png]]
552 -
553 553  (% class="wikigeneratedid" id="HFigure13.Codelistspecificationprocess" %)
554 554  Figure 13. Code list specification process
555 555  
556 -(% class="wikigeneratedid" %)
548 +
557 557  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.
558 558  
559 -[[image:1768470878394-873.png]]
560 560  
561 561  (% class="wikigeneratedid" id="HFigure14.Priorityrankingofexistingcodelistsforreuse" %)
562 562  Figure 14. Priority ranking of existing code lists for reuse
563 563  
564 -(% class="wikigeneratedid" %)
555 +
565 565  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.
566 566  
567 -[[image:1768470896763-366.png]]
568 568  
569 569  (% class="wikigeneratedid" id="HFigure15.Aspectsofcodelistsuitability" %)
570 570  Figure 15. Aspects of code list suitability
571 571  
572 -(% class="wikigeneratedid" %)
573 -[[image:1768470911321-123.png]]
574 574  
575 575  (% class="wikigeneratedid" id="HFigure16.Codelistmodificationscenarios" %)
576 576  Figure 16. Code list modification scenarios
... ... @@ -583,10 +583,8 @@
583 583  
584 584  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.
585 585  
586 -
587 587  Figure 17. DSD design process
588 588  
589 -[[image:1768470939545-136.png]]
590 590  
591 591  Figure 18. Checklist for DSD design process
592 592  
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