Message-ID: <445410377.3362.1495467122295.JavaMail.confluence@ece-vmapps> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_3361_1912407518.1495467122293" ------=_Part_3361_1912407518.1495467122293 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html _II. Information in a statistical organization

_II. Information in a statistical organization

Note: To tran= slate this paper into over 50 languages, please see the Automatic translati= on option at the top of the screen

II. Information in a statis= tical organization

A. I= ntroduction


4. There is a widespread interest ac= ross statistical organizations in being able to trace how statistical infor= mation (for example, data and metadata) "flow" through statistica= l business processes (into processes and out of processes). Interested part= ies include broad statistical systems (like the European Statistical System= ), National Statistical Systems (both centralized and decentralized) and sm= aller task teams working inside National Statistical Offices.

5. In the description of the GSIM Business group, it is seen that GSIM c= overs the whole statistical process and is designed to support both current= and new ways of producing statistics.

6. Achieving standards-based modernization of the production of official= statistics places an emphasis on being able to share and reuse processes, = methods, components and data repositories. Achieving reuse of processes, me= thods and components will require that process designers are readily able t= o discover what is available for reuse and whether it may be relevant to th= eir particular purposes and needs. The case for reuse will be challenged if= , in practice, discovering potentially reusable business resources, and ass= essing whether those resources are actually suitable for the designer's spe= cific purpose, takes more time than creating new design elements.

7. GSIM was designed to enable an explicit separation between the design= and execution of statistical processes. The description of the GSIM Produc= tion group shows how this has been modelled.

8. There is an increasing business need to record reliable, structured i= nformation about the processes used to produce specific statistical outputs= . In order to maximize transparency and reproducibility of results, it is i= mportant for a statistical organization to understand the process and its i= nputs and outputs. The GSIM Concepts and Structures Groups contain the conc= eptual and structural metadata objects that are used as inputs and outputs = in a statistical business process.

9. The GSIM Base Group consists of several objects that can be seen as t= he fundamental building blocks that support many of the other objects and r= elationships in the model. These objects form the nucleus for the applicati= on of GSIM objects. They provide features which are reusable by other objec= ts to support horizontal functionality such as identity, versioning etc. Fo= r these reasons, many of these objects are rather abstract in nature.

10. Note: GSIM information objects have been given in italics in the des= criptions that follow. The diagrams included in this section are stylized r= epresentations of the model. The colours of the boxes in diagrams represent= which group the information object belongs to (Blue for Business Group, Re= d for Production Group, Green for Concepts Group, Yellow for Structures Gro= up and Orange for the Base Group). In many cases there is more detail to be= found in the UML. Detailed information on each information object in the m= odel, including a glossary and UML class diagrams can be found in Annexes C= and D of this document.

B. Business Group


11. The Business group is used to ca= pture the designs and plans of Statistical Programs. This includes= the identification of a Statistical Need, the Acquisition, Production and Dissemination Activities that comprise = the Statistical Programs and the evaluations of them. 

12. An organization will react and change due to a variety of needs. In = simple terms, these may be divided into at least two types of Statistic= al Needs: an Information Request and an Environment Chang= e.

13. Where an organization receives an Information Request this = will identify the information that a person or organization in the user com= munity This community may include users within the organization as well as = external to it. For example, a the team responsible for compiling National = Accounts may need a new Statistical Activity to be initiated to produce new= inputs to their compilation process. requires for a particular purpose. Th= is request will commonly be defined in terms of a Concept or S= ubject Field that defines what the user wants to measure and the P= opulation that the user wants data about.

14. When an Information Request is received it will be discusse= d and clarified with the user. This will be described by a Process Step= . Once clarified, a search will be done to check if the data already e= xist. Discovering these Data Sets may be enabled by searching for = Concepts and Classifications

15. Where an organization identifies an = Environment Change this indicates that there needs to be an externally= motivated change. This may be specific to the organization in the form of = reduced budget or new demands from stakeholders or may be a broader change = such as the availability of new methodology or technology. A Statistica= l Need can be both internally and externally driven. For example, a st= atistical organization may realize that their existing Products an= d services must be improved. This may be in response to an Assessment of those Products and services.





Figure 1. Statistical Need

16. As shown in Figure 1, once an organization has identified a Stat= istical Need, it will be further specified in the form of a Change= Definition. This identifies the specific nature of the change in term= s of its impacts on the organization or specific Statistical Programs. This Change Definition is used as an input into a Busines= s Case. A successful outcome will either initiate a new Statistica= l Program or create a new Statistical Program Design that red= efines the way an existing Statistical Program is carried out= .



Figure 2. Evaluation

17. At any point in the statistical business process, an organization ma= y undertake an evaluation to determine utility or effectiveness of the busi= ness process or its inputs and outputs. An Assessment will be unde= rtaken to evaluate any resources, processes or outputs and may refer toany = object described in the model.

18. An Assessment may be of several types depending on the purp= ose. A Gap Analysis may be undertaken often in the context of a Business Case. An Evaluation Assessment is undertaken to de= termine whether a statistical output meets the need for which it was first = created through analysis of:

(a) any information object that can be considered a Process Output;<= /em> and

(b) in light of the original Statistical Need.
Statis= tical Program

19. A Statistical Program is the overarching, ongoing activity = that an organization undertakes to produce statistics (for example, a retai= l trade survey). Each Statistical Program includes one or more Statistical Program Cycles. The Statistical Program Cycle is= a repeating activity to produce statistics at a particular point in time (= for example, the retail trade survey for March 2012).



Figu= re 3. Statistical Programs

20. A Statistical Program (Figure 3) has an associated set of <= em>Statistical Program Designs that identify the methodology = (the methods used to acquire, process and disseminate the data) used f= or the Statistical Program. Only one Statistical Program Desig= n is valid for, and is identified as being used by, a particular S= tatistical Program Cycle. Changes to the methodology result in new Statistical Program Designs so over time each Statistical Program= will have a series of designs that provide a history of changes to th= e Statistical Program. The Statistical Program Design ide= ntifies the set of processes that are intended to be used to undertake the = activity (Process Step Design), the resources required for the pro= cesses and a description of the methodology and context.

21. Each Statistical Program Cycle consists of one or more = Statistical Activities. A Statistical Activity is the set of = executed processes and the actual resources required as inputs and produced= as outputs. It is analogous to the Statistical Program Design but= represents the execution rather than design. The same information that is = identified in the Statistical Program Design and intended to be us= ed to undertake an activity, is identified here as the actual information u= sed. For example in the design, a dataset of a particular type may be ident= ified as an input whereas in the Statistical Activity the filename= and location of the actual input dataset would be identified.

22. The model identifies different types of activities that represent th= e major steps in the statistical production process (Figure 4). Three types= have been specifically identified in the model but other types could be de= fined. The distinction between different types of activities and distinctio= n of a Statistical Activity from a Statistical Program Cycle means that each iteration can be made up of multiple activities of the = same or different types and these may or may not represent the sequence of = collection through to dissemination. This model supports both the tradition= al approach of collecting data for a particular need, and the emerging and = future approach of collecting data and producing new outputs based on exist= ing data sources that are maintained and added to over time.




Figure 4. Stati= stical Activity

23. A possible future approach relates to a continuous collection proces= s. In the age of 'big data', the cost of collecting and storing data (for e= xample, a statistical register) is low. An organization can collect data on= a continuous basis without a particular Dissemination Activity, Produc= t or Dissemination Service in mind. In this case the= organization has a Statistical Program with a Statistical Pro= gram Cycle that consists of an Acquisition Activity that gath= ers data and adds to a Data Resource. Any Statistical Program<= /em> (consisting of only Production or Dissemination Activitie= s) may then use this Data Resource in the future.

Acquisition Activity

24. For an activity where the purpose is to acquire data a = ;Collection Description (Figure 5) provides a description of = the activity and the associated contextual information. The Acquisition= Activity identifies the means by which the data is collected and= where it is collected from by identifying a Data Channel.




Figure 5. Acqui= sition Activity

25. A Data Channel identifies the Instrument used to c= ollect data. An Instrument is the description of the tool that wil= l be used to collect data. Examples of it may include a questionnaire or a = set of requirements to develop software for gathering data. The In= strument includes an Instrument Control and may have Ques= tion Blocks, Questions, Statements and Interviewer Instructions.

26. Once the Instrument has been designed, it must be implement= ed in the form of one or more Instrument Implementations.= These could be printed forms, software programs, etc. The Data Channel= uses the Instrument Implementation to request data = and describes the technique used to do it by means of a Mode. Once= the Data Channel receives the data, it sends the data to an ident= ified Data Resource (thus populating it with Data Sets).

27. The Mode represents the way t= he information collection process is going to be conducted and in this way,= 'how' the Data Channel is going to be used, the following table (= Table 1) represents some examples of Data Channel, Instrument<= /em>, Instrument Implementation and Mode.

Table 1. Examples of Data Channel, = Instrument, Instrument Implementation and Mode


Data Channel

Instrument

Instrument Implementation

Mode

Physical presence

Questionnaire

 

<= p> 

 

 

 

Paper Form

 

&= nbsp;

Traditional interview

Traditional mail

Self-administered

 <= /p>

Direct deposit

Computer

Software Program

 

 

CAPI interview

Phone

CATI interview

Internet

Self-administered

Data scanner device

Set of Requirements

 = ;

 

 

 

Data Scanner Program

Data collector

Internet

 

Web Scraping Robot

 =

Web queries

Agents

Internet

Web Service Consumer Program

Applications interconnection

Secondary transfer of data

Data Transfer

Data Medium, File Transfer, Web Sphere Applic= ation


Pr= oduction Activity

28. GSIM includes the notion of a Production Activity. More inf= ormation about how GSIM expands on this activity can be found in the Produc= tion Group section.

Dissemination Activity

29. GSIM includes the notion of a Dissemination Activity. More = information about how GSIM expands on this activity can be found in the&nbs= p;Structures Group section.
 

C. Production Group


30. The Production group is used to = describe each step in the statistical process, with a particular focus on d= escribing the inputs and outputs of these steps. A business process can be = specified in terms of:

  • The Process Steps which need to be undertaken during that proc= ess, and
  • The sequence in which Process Steps need to be undertaken duri= ng that process.

 

31. A Statistical Activity puts into effect a statistical busin= ess process which has been designed previously (it has a Statistical Pr= ogram Design) and which spans one or more phases of the business proce= ss (for example, the Collect, Process, Analyze, and/or Disseminate phases o= f the GSBPM).

32. At the heart of the Production Group is the description of the P= rocess Steps within the statistical business process and the use of st= atistical information as inputs to, and outputs from, each Process Step= . Each Process Step can be as "large scale" or &quo= t;small scale" as the designer of a particular business process choose= s (see Figure 6). Steps can contain "sub-steps", those "sub-= steps" can contain "sub-steps" within them and so on indefin= itely.


Figure 6. P= rocess Steps can be as large or small as needed

33. In line with the GSIM design principle of separating design and prod= uction, the Production Group (see Figure 7) assumes that each Process Step will be designed during a design phase. Having divided a = planned statistical business process into Process Steps, the next = requirement is to specify a Process Step Design for each step. The= Process Step Design identifies how each Process Step wil= l be performed.

34. The sequencing of Process Steps within a business process i= s addressed through the concept of Process Control. When crea= ting a Process Step Design, a Process Control that provid= es information on "what should happen next" is specified. Sometim= es one Process Step will be followed by the same step under all ci= rcumstances. In such cases the Process Control simply records what= Process Step comes next. However, sometimes there will be a choic= e of which Process Step will be executed next. In this case, the d= esign of the Process Control will detail the set of possible "= ;next steps" and the criteria to be applied in order to identify which= Process Step(s) should be performed next.

35. During the production phase, as part of a Statistical Activity,<= /em> Process Steps are executed in accordance with their design. A= n agent (person or system) initiates execution of the relevant Process = Steps based on the following information:

  • Process Step Design to determine how the current Process S= tep should be executed.
  • Process Control to determine which Process Step to ex= ecute next.

36. A Process Step Execution Record= should be recorded for each Process Step which is executed. The <= em>Process Step Execution Record is the information object which recor= ds the action. The action itself is a real world event, where Process S= tep Execution Record records that real world event.




Figure 7. Simpl= ified view of Production Group objects

37. As shown in Figure 7, a Statistical Program Design is assoc= iated with a top level Process Step whose Process Step Design<= /em> contains all the sub-steps and process flows required to put that stat= istical program into effect. Each Process Step in a statistical bu= siness process has been included to serve some purpose. This is captured as= the Business Function associated with the Process Step. The B= usiness Function, for example, might be 'impute missing values in the = data'.

38. The Process Step Design associated with that Process St= ep will then identify the Process Method that will be used to= perform the Business Function associated with the Process Ste= p. For example, if the Business Function is 'impute missing v= alues in the data', the Process Method might be 'nearest neighbour= imputation'.

39. A Process Method specifies the method to be used, and is as= sociated with a set of Rules to be applied. For example, any use o= f the Process Method 'nearest neighbour imputation' will be associ= ated with a (parameterized) Rule for determining the 'nearest neig= hbour'. In that example the Rule will be mathematical (for example= , based on a formula). Rules can also be logical (for example, if = Condition 1 is 'false' and Condition 2 is 'false' then set the 'requires im= putation' flag to 'true', else set the 'requires imputation flag' to 'false= ').

40. At the time the Process Step Design is executed someone or = something needs to apply the designated method and rules. The Process S= tep Design can designate the Business Service that will imple= ment the Process Method at the time of execution. A Business S= ervice represents a service delivered by a person or a piece of softwa= re. Putting a publication on the statistical institute's website or putting= collected response forms in a shared data source for further processing ar= e both examples of Business Services.

41. A Process consists of a set of Process Steps, incl= uding their associated process flow information. This enables the particula= r set of Process Steps to be named, and potentially catalogued and= reused, as a Process. Process Steps need not be grouped = into named Processes unless business benefits (for example, opport= unities for reuse) are likely to result from doing so.

42. A Statistical Activity initiates the execution of a top lev= el Process Step which will result in all sub-steps being executed = which are relevant to that instance of the Statistical Activity. E= xecuting the top level Process Step should start populating a = Process Step Execution Record associated with that Statistical Act= ivity.

43. The Process Step Execution Record (see Figure 9) will recor= d the inputs provided when executing the top level Process Step. I= t will then record information which allows the actual flow of execution fo= r that instance of the Statistical Activity to be traced. This inc= ludes recording the actual inputs to, and outputs from, each sub-step as we= ll as the evaluation of each Process Control (which, in turn, dete= rmines the specific sequence of Process Steps performed during exe= cution).


Figure 8. P= rocess Step Design

Design

44. A Process Step Design (Figure 8) has a Process Input Sp= ecification that identifies the types of the Process Inputs required at the time of e= xecution. An example might be a Process Input Specification that r= equires a Dimensional Dataset to be provided at the time = of execution.

45. A Process Step Design may also identify Process Inputs<= /em>. These refer to specific i= nstances of inputs, rather than specifying a type of input. For exam= ple, a Process Step Design may specify that a particular Code = Set will be used to provide a list of valid values.

46. Process Input Specifications and Process Inputs ar= e often determined by the input requirements of the Business Service, Process Method and Rules associated with the Proc= ess Step Design.

47. Process Output Specifications play an analogous role to Process Input Specifications but describe the types of Process Outputs to be produce= d at the time of execution of the Process Step.

48. Process Control specifies what process flow should occur fr= om one Process Step to the next at the time of execution. In some = cases it may simply record the next Process Step to be executed on= a fixed/constant basis. Alternatively, a Process Control may set = out conditions to be evaluated at the time of execution to determine which = Process Step(s) to execute next.

49. An example of the latter might be testing a Process Output = against a quality criterion and initiating one course of action if the outp= ut meets the standard and another if it does not. It is not until the time = of execution of the Process Step that it is possible to determine = whether the standard has been met or not.

50. The specification and evaluation of cond= itional Process Controls refer to Rules. In the case of <= em>Process Controls, the Rules guide the process flow. (In th= e case of Process Step Designs, Rules guide the work done= by the Process Step to produce Process Outputs).


Figure 9. Process Step Execution


Execution

51. A Process Step Execution Record (Figure 9) records the exec= ution of activities according to a Process Step Design.

52. Execution of a Process Step uses Process Inputs in= accordance with the Process Input Specification specified in the = Process Step Design (Figure 8).

53. When execution takes place a particular instance of a Dimensiona= l Dataset (for example, "Turnover of retail trade establ= ishments by employment size, industry class and state, for November 2012&qu= ot;) will be provided as a Process Input. The identity (instance) = of the particular Dimensional Dataset may be different fo= r each example of execution. The specific Process Inputs associate= d with an instance of executing a Process Step are recorded in the= Process Step Execution Record.

54. Parameter Inputs are a form of Process Input used = to specify which configuration should be used for a specific execution of a= Process Step. For example, a set of parameters like the statistic= al period concerned or a sample size.

55. A Process Input may be provided to a Process Step = in order for the Process Step to 'add value' to that input by prod= ucing an output which represents a transformed version of the input. Such a= Process Input is classed as a Transformable Input. Usual= ly this represents the main dataflow within the statistical process (like m= icrodata, aggregated data, and disseminated data). It is, in short, the dat= a transformed by the statistical process.

56. A Process Support Input influences the work performed by th= e Process Step, and therefore influences its outcome, but does not= correspond to a Parameter Input or a Transformable Input. Examples could include:

  • A Code List which will be used to check whether the codes= recorded in one dimension of a dataset are valid.
  • An auxiliary Data Set which will influence imputation for, or = editing of, a primary dataset which has been submitted to the process step = as the Transformable Input.

57. A Process Output is any instance of an information object w= hich is produced by a Process Step as a result of its execution. <= em>Process Outputs are subtyped as part of the Process Output Spec= ification.

58. A Transformed Output is the result which provides the 'reas= on for existence' of the Process Step. If that output were no long= er required then there would be no need for the Process Step in it= s current form. Typically, a Transformed Output produced by a part= icular Process Step will either be provided as a Process Input= to a subsequent Process Step or it represents the final prod= uct from a statistical business process.

59. A Process Metric records information about the execution of= a Process Step. For example, how long it took to complete executi= on of the Process Step; or what percentage of records in the T= ransformable Input were updated by the Process Step to produc= e the Transformed Output.

60. Process Outputs associated wi= th execution of the current Process Step may be evaluated as part = of Process Control in determining which process step to execute ne= xt.

=

Figure 10. Conceptual and Stru= ctural information objects can be Process Inputs and Outputs<= /p>

61. The execution of a Process Step will supply Process Inp= uts and result in Process Outputs. The specific Process I= nputs and Process Outputs associated with the particular exec= ution will be recorded in the Process Step Execution Record. Throu= gh Process Input Specification and Process Output Specificatio= n the Process Step Design defines the types of Process In= puts to be supplied, and the types of Process Outputs to be p= roduced at the time of execution (See Figure 10). In many cases, these = Process Inputs and Outputs are the conceptual and structural = information objects that are described in the GSIM Concepts and Structures = Groups (See Sections D and E). The same instance of an information object m= ay perform different roles in different process steps.

D. Concepts Group


62. The GSIM Concepts Group contains= sets of information objects that describe and define the terms used when t= alking about real-world phenomena that the statistics measure in their prac= tical implementation.

63. The information objects in this group are used as Process Inputs= and are often referred to in Products and Representation= s to provide information that helps users understand results.

64. At an abstract level, a Concept is defined in GSIM as 'unit= of thought differentiated by characteristics'. Concepts are used = in these situations:

(a) As a Population. To describe the set of objects it is wante= d to obtain information about in a statistical survey. For example, the Population of adults in Netherlands.
(b) As a characteristic.&n= bsp;A particular Concept about a Population is described = by a Variable. The data are linked to a concept via a variable. Fo= r example, the Concept of gender in the Populat= ion of adults in Netherlands is collected by a Variable. At t= he representation level, there are data with Codes.
(c) As a= Category to further define details about a Concept. For = example, Male and Female for the Concept of Gender. Codes= are linked to a Category via a Classification Scheme, fo= r use within a Classification.

Population



Figure 11. Populations and Units

65. As part of a Statistical Activity there is a Population= (see Figure 11). There are several kinds of Populations: Target, Survey, Frame, and Analysis. = The objects of interest are Units (for example, persons or busines= ses). Data are collected about Units. There are two kinds of = Unit specified in the model. These are Observation Unit and Analysis Unit. A Unit is associated with a Po= pulation.

Variable

66. When used as part of a Statistical Activity, a Population is associated with a characteristic. The associati= on of Population and a Concept playing the role of a char= acteristic is called a Variable (see Figure 11). For exam= ple, if the Population is adults in Netherlands, then a = relevant Variable might be educational attainment.

67. Variable (educational attainment of a= dults in Netherlands) does not include any information on how the= resulting value may be represented. This information is in the Represe= nted Variable. This distinction prevents the duplication of V= ariable information when what is being measured is the same but i= t is represented in a different manner. It promotes the reuse of = a Variable definition.

68. A derived Variable is created b= y a Process Step that applies a Process Method to one or = more Transformable Inputs (Variables). The transforme= d Output of the Process Step is the derived Variable. In GSIM, this is modelled in the Production Group (see Section C).






Figure 12. Variabl= e

69. A Conceptual Domain is associated with Variable. I= t has two subtypes: Described Conceptual Domain and Enumerated= Conceptual Domain. An Enumerated Conceptual Domain, in combi= nation with a Category Set contains information on the semantics o= f the Categories used by the Variable.

Represented Variables

70.GSIM assists users in understanding both = the meaning of the object and the concrete data-representation of the objec= t. Accordingly, GSIM distinguishes between conceptual and representation le= vels in the model, to differentiate between the objects used to conceptuall= y describe information, and those that are representational.




Figure 13. Repr= esented Variable

71. The Represented Variable (see Figure 13) adds information t= hat describes how the resulting values may be represented through associati= on with a Value Domain. While Conceptual Domains are asso= ciated with a Variable, Value Domains are associated with= a Represented Variable. These two domains are distinguished becau= se GSIM wants to be able to talk about the semantic aspect (Conceptual = Domain) separately to the representational aspect (Value Domain).

72. Both the Enumerated Value Domain and the Described Valu= e Domain give information on how the Represented Variable is = represented. The Enumerated Value Domain does this in combination = with a Code List, while the Described Value Domain provid= es a definition of how to form the values, rather than explicitly listing t= hem.

73.The Value Domain is defined by a Data Type. Dat= a Types contain information on the allowed computations one may perfor= m on the Datum (see Figure 15). For example, it is possible to dis= tinguish between nominal-, ordinal-, interval-, and ratio-data as Data = Types. Gender Codes lead to nominal statistical data, whereas= age values lead to interval data.

74. A Unit of Measure refines the Value Domain . It is= the entity by which some quantity is measured. Examples are Tonnes, Count = of_, and Dollars.

Instance Variable



Figure 14. Instance Variable

75. An Instance Variable (see Figure 14) is a particular Re= presented Variable associated with a collection of data (Datum). This corresponds to a column of data in a database. More particularly, = the age of all the US presidents either now (if they are alive) or the age = at their deaths is a column of data described by an Instance Variable, which is a combination of the Represented Variable "Age&= quot; and the Value Domain of "decimal natural numbers (in ye= ars)".

76. A Datum is defined by the measure of a Value Domain combined with the link to a Unit (for example, persons or busine= sses).  A Datum is also associated with a Data T= ype and a Unit of Measure through the Value Domain. =

Classifications

 



Figure 15. Over view of Classification

77. Figure 15 provides an overview of the objects relating to Classi= fications. Classifications describe the Category rol= e of a Concept.

78. A Classification is a categorization of real world objects = so that they may be grouped, by like characteristics, for the purposes of m= easurement, for example ISIC (International Standard Industrial Classificat= ion of All Economic Activities). Classifications can be grouped in= to a Classification Family, such as industrial activity.

79. A Classification such as ISIC is a set of related Class= ification Schemes. It relates Classification Schemes that dif= fer as Classification Versions or Classification Variants= . A Classification Variant is based on a Classification Versio= n. In a Classification Variant, the Categories of th= e Classification Version are split, aggregated or regrouped to pro= vide additions or alternatives to the standard order and structure of the b= ase Classification Version. A Classification Scheme has Categories organized into Levels determined by th= e hierarchy. A Level is a set of Concepts that are m= utually exclusive and exhaustive, for example, section, division, group and= class in ISIC rev 4.

80. A Classification Item combines the meaning, representation = and additional information in order to meet the Classification cri= teria, for example "A - agriculture, forestry and fishing" and ac= companying explanatory text such as information about what is included and = excluded.

81. A Correspondence Table can be created by a Map tha= t links a Classification Item in a Classification Scheme = with a corresponding Classification Item in another Classifica= tion Scheme via the Category corresponding to both Classi= fication Items. For example, in a table displaying the relationship be= tween ISIC Rev.4 and the North American Industry Classification System (NAI= CS 2007 (US)), 0111 in ISIC Rev.4 is related to 111110 in NAICS.



Figure 16. Concept Systems

82. A Category is typically part of a Category Set, wh= ich is a subtype of Concept System. A Category Set contai= ns one or more Category Items. A Category can be represen= ted in a Category Set, a Code List or a Classificatio= n Scheme. A Category provides meaning to these information ob= jects, for example "agriculture, forestry and fishing" or "f= emale".

83. A Code List is also a type of Concept System. It i= s used for creating a group of Codes and their associated Cate= gories. It can consist of one or more Code Items. A Code<= /em> designates a Category providing representation to the meaning= from the Category. For example in "F - female", the Code is F and the Category is Female. 

E= . Structures Group


84. The GSIM Structures Group contai= ns sets of information objects that describe and define the terms used in r= elation to data and their structure. Like the information objects in t= he Concepts Group, the information objects in this group are used as Pr= ocess Inputs and are often referred to in Products and Re= presentations to provide information that helps users understand the s= tructure of the data.



Figure 17. Data Resource

85. An Acquisition Program (see Figure 4) conducted by a statis= tical organization produces or supplies an Information Resource (F= igure 17). In GSIM, one subtype of an Information Resource has bee= n specified. This is the Data Resource

86. A Data Resource is comprised of Data Sets. These Data Sets are made available as part of:

  • an Acquisition Activity (that is, made available by the data p= roviders for data acquisition or resulting from the Acquisition Activit= y); or
  • a Dissemination Activity.


87. For a Data Resource, th= e Data Set is discovered and provided by means of the Data Loc= ation. The Data Location specifies from where the data can be= retrieved. Either this can be a link to a specific file containing the dat= a or to a Dissemination Service (see Figure 20) that will consume = a query for the data and will return a Data Set. If the link is to= a Dissemination Service then it is probable that the Dissemin= ation Service is able to be queried for many types of data and so can = provide many Data Sets. Each Data Set must be structured = according to a known Data Structure (for example, a known structur= e for Balance of Payments, Demography, Tourism, Education etc.).

88. The Data Location is associated with a specific Provision Agreement which identifies the Data Provider and = the Data Flow. Only one Data Structure can structure data= relating to a Data Flow. A Data Flow can be grouped by <= em>Subject Fields (for example, National Accounts, Balance of Payments= , Demography) which support data discovery.

89. It is mandatory that the Data Set is linked to a Provision Agreement to which it relates (that is= , the union of the Data Provider and the Data Flow).



Figure 18. Data Set=

90. A Data Set has Data Points. A Data Point = is placeholder (for example, an empty cell in a table) in a Data Set for a Datum. The Datum is the value that populates tha= t placeholder (for example, an item of factual information obtained by meas= urement or created by a production process). A Data Structure= describes the structure of a Data Set by means of Data S= tructure Components (Identifier Components, Measure Component= s and Attribute Components). These are all Represent= ed Variables with specific roles.

91. Data Sets come in different forms, for example as Administr= ative Registers, Time Series, Panel Data, or Survival Data, just to name a = few. The type of a Data Set determines the set of specific attribu= tes to be defined, the type of Data Structure required (Unit D= ata Structure or Dimensional Data Structure), a= nd the methods applicable to the data.

92. For instance, an administrative register is characterized by a U= nit Data Structure, with attributes such as its original purpose or th= e last update date of each record. It contains a record identifying variabl= e, and can be used to define a Frame Population, to replace or com= plement existing surveys, or as an auxiliary input to imputation. Record ma= tching is an example of a method specifically relevant for registers.

93. An example for a type of Data Set defined by a Dimensio= nal Data Structure is a time series. It has specific attribut= es such as frequency and type of temporal aggregation and specific methods,= for example, seasonal adjustment, and must contain a temporal variable.

94. Unit data and dimensional data are perspectives on data.  Altho= ugh not typically the case, the same set of data could be described both wa= ys.  Sometimes what is considered dimensional data by one organization= (for example, a national statistical office) might be considered unit data= by another (for example, Eurostat where the unit is the member state). &nb= sp;A particular collection of data need not be considered to be intrinsical= ly one or the other. This matter of perspective is conceptual.




Figure 19. Dime= nsional and Unit Data Structures

95. A Dimensional Data Structure describes the st= ructure of a Dimensional Data Set by means of Dimensi= onal Identifier Components, Dimensional Measure = Components and Dimensional Attribute Components. The= se are all Represented Variables with specific roles.

96. The combination of dimensions contained in a Dimensional Data Structure creates a key or identifier of the measured valu= es. For instance, country, indicator, measurement unit, frequency, and time= dimensions together identify the cells in a cross-country time series with= multiple indicators (for example, gross domestic product, gross domestic d= ebt) measured in different units (for example, various currencies, percent = changes) and at different frequencies (for example, annual, quarterly). The= cells in such a multi-dimensional table contain the observation values.

97.A measure is the variable that provides a container for these observa= tion values. It takes its semantics from a subset of the dimensions of the&= nbsp;Dimensional Data Structure. In the previous example,= indicator and measurement unit can be considered as those semantics-provid= ing dimensions, whereas frequency and time are the temporal dimensions and = country the geographic dimension. An example for a measure in addition to t= he plain 'observation value' could be 'pre-break observation value' in the = case of a time series. Dimensions typically refer to Variables wit= h coded Value Domains, measures to Variables with uncoded= Value Domains.

98. A Unit Data Structure describes the structure= of a Unit Data Set by means of Unit Identifier = Components, Unit Measure Components and Unit= Attribute Components. These are all Represented Variables wi= th specific roles.

99. A Unit Data Structure specifies= the structure of unit data. It distinguishes between the logical and physi= cal structure of a Data Set. A Unit Data Set may= contain data on more than one type of Unit, each represented by its own re= cord type.

100. Logical Records describe the s= tructure of such record types, independent of physical features by referrin= g to Represented Variables that may include a unit identification = (for example, household number). A Record Relationship defines sou= rce-target relations between Logical Records.




Figure 20. Diss= emination Activity

101. A Dissemination Service exposes the Data Set= s and other metadata that is contained in the Information Resource= . It  is the mechanism to create and disseminate Represe= ntations to consumers. These Representations a= re created dynamically on the specific request and according to the specifi= c needs of the consumer (the Output Specification). Repre= sentations may contain any type of information, for instance stat= istical data (as a Data Set or visualization) or structu= ral or conceptual metadata like a Data Structure, a = Code Set or a description of a Concept.

102. A Product is the result of a Publicatio= n ActivityProducts are stored for later dissemina= tion through Dissemination Services. Examples of Pro= ducts are publications, press releases, etc. Representat= ions may be used as input to, and as components of, a Pr= oduct.

F. Base Group


103.The GSIM Base Group consists of = several information objects that can be seen as the fundamental building bl= ocks that support many of the other information objects and relationships i= n the model. These information objects form the nucleus for the application= of GSIM information objects. They provide features which are reusable by o= ther information objects to support horizontal functionality such as identi= ty, versioning etc. For these reasons, many of these information objects ar= e rather abstract in nature.



Figure 21. Base Artefacts

104. The only base artefact in GSIM that gives underlying identity and n= aming is the Identifiable Artefact. It can be inherited by any cla= ss in GSIM for which identity, name, description, and additional documentat= ion is required.

105. The Identifiable Artefact has three associations to Co= ntextual String =E2=80=93 one for each of name, description, and docum= entation. The value in the Contextual String is given a context by= the Context Key which can be Type or Language.<= /p>

106. There is no attempt in GSIM to model th= e administration of items in repositories such as the maintenance agency, v= ersioning, repository functions. However, the Identifiable Artefact does have a link to Administrative Details where such details ca= n be added using the GSIM extension methodology.







= Figure 22. Organization


107. An Organization Scheme= comprises Organization Items, each of which can be an Organiz= ation Unit or an Individual. The Organization Unit c= an be in a hierarchic scheme of Organization Items. An Individ= ual or Organization Unit can have a number of different C= ontact Details.


108. The Individual or = Organization Unit can play zero or more recognized roles (Organiza= tion Item Role) in the maintenance (Maintenance Agency) data = collection (Data Provider) and dissemination (Data Consumer) processes.

 

 

 




Word version of this section only 

 

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