Metadata Driven Incorporated S tatistical D ata M anagement S ystem CSB of Latvia By Karlis Zeila VP CSB of Latvia.

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Utilization of the framework in CSB of Latvia began move from Stove Pipe to Process Oriented way to deal with measurable information creation. META DATA DRIVEN ...
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Metadata Driven Integrated S tatistical D ata M anagement S ystem CSB of Latvia By Karlis Zeila Vice President CSB of Latvia MEXSAI, Cancun, November 2 - 4

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The framework has been created inside 2,5 years (January 2000 to July 2002), Development has been finished by outsourced organization Microlink Latvia in close collaboration with the specialists from CSB, 600 000 Euros has been spend for the framework improvement, Use of the framework in CSB of Latvia began move from Stove Pipe to Process Oriented way to deal with measurable information generation INTRODUCTION

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Any activity inside the framework is ruled by metadata , META DATA DRIVEN ... ? Meta information is the key component of the framework , All product modules of whole framework is associated with the Core Metadata module (Meta information base) . Any progressions inside the framework begins with the progressions of meta information Full cycle of the information handling is conceivable as late as the best possible depiction process in meta information base are finished

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Most of the framework programming modules are associated with the Registers module, INTEGRATED ... ? Registers module is an indispensable part of the framework, All studies are upheld by satisfactory groupings put away in the Meta information base In all reviews respondent information fields are associated with registers information All information is put away in corporative information distribution center Statistical information preparing has part in bound together strides for various overviews Export/Import techniques guarantee work with the framework information records utilizing diverse standard programming bundles

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Advantages and Restrictions Advantages 1. At most institutionalized fundamental business measurements information passage, preparing and capacity strategies, that give the exchange from stove funnel information handling to prepare situated information handling. Incorporated handling and capacity of the measurable information, including metadata, by utilizing information distribution center advancements and OLAP instruments. 3. Every one of the information handling techniques are being facilitated from regular metadata framework . These methods are being portrayed in metadata base, by utilizing uncommon pseudo dialect and characterized documentation bunch. In this manner for institutionalized methodology execution for every overview singular writing computer programs is not required . 4. The framework is educationally associated with Business Register , which furnishes with the immediate respondent information recovery and upgrading. 5. Uncommon import and fare technique is made for information trade with different frameworks. 6. A connection with PC Axis is made for electronic information scattering.

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Restrictions 1.The framework is arranged towards the information handling of various periodicity overviews, where information gathered utilizing respondents filled surveys ( Some adjustment would be fundamental for use CAPI, CATI advancements ). 2 .Metadata base does not anticipated depiction of classification standards for information dispersal, they are hard coded in the framework. 3 . Analytic devices for the metadata depictions are not sufficiently intense, along these lines specialists get ready meta information portrayals ought to be of high experience. 4 . Equipment and Standard programming necessities: PC\'s >/= Pentium II, RAM >/=128Mb outfitted with W – 95 to W-2000 and MS Office 2000 . 5 . Metadata base does not predicted portrayal of calculation for programmed production of respondents records for Sample studies from the Business register outline.

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ISDMS design Integrated factual information administration framework Corporative information Warehouse CSB Web Site User adminis-tration information base Dissemi-country information base Metadata base Macrodata base FIREWALL Raw information base Registers base OLAP information base Microdata base Windows 2000 Server Advanced MS Internet Information Server SQL server 2000, PC-Axis ISDMS Business application Software Modules Data passage and approval module related with DB: Data accumulation module related with DB: Data examination module related with DB: C metal metadata base module related with DB: Registers module related with DB: METADATA USER ADMINISTRATION REGISTERS USER ADMINISTRATION METADATA MICRODATA REGISTERS USER ADMINISTRATION METADATA MICRODATA REGISTERS USER ADMINISTRATION OLAP METADATA MACRODATA Data spread module related with DB: Data WEB section module related with DB: User organization module related with DB: Data mass passage module related with DB: Missed information ascription module related with DB: METADATA MICRODATA REGISTERS RAW DATABASE USER ADMINISTRATION METADATA M A CRODATA REGISTERS USER ADMINISTRATION METADATA MICRODATA REGISTERS USER ADMINISTRATION METADATA MICRODATA REGISTERS DATA IMPUTATION SOFTWARE METADATA MICRODATA MACRODATA USER ADMINISTRATION

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Structure of microdata (perception information) [Bo Sundgren model] Objects attributes: C o = O(t).V(t), where: O - is an article sort; V - is a variable; t - is a period parameter. Every consequence of perception is an estimation of variable (information component) - C o All estimations of every variable are connected to question (respondent) requirements, which could be called - vectors or measurements. Examining populace of the respondents, these measurements we are utilizing for development of various groupings and for information accumulation. The measurements recorded beneath could be joined to every estimation of variable in horticultural insights : - Main sort of Activities (ISIC characterization); - Kind of Ownership and Entrepreneurship (code) - Regional area (code) - Employees bunch order (code) - Turnover bunch arrangement (code).

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Structure of macrodata (measurements) Macrodata are the aftereffect of estimations (collections ) of an arrangement of microdata . Measurable attributes: C s = O(t).V(t).f, where: O and V - is an item qualities; t - is a period parameter, f – is a collection capacity ( sum,count,average, and so forth) outlining the genuine estimations of V(t) for the articles in O(t) . The structure for macrodata is alluded in metadata base to as box structure or " alfa-beta-gamma-tau " structure (  ). For information trade alfa alludes to the choice property of items (O) , beta – outlined estimations of variables (V) , gamma – crossclassifying variables, tau – time parameters (t) .

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Structure of Surveys ( polls ) New review ought to be portrayed in the Metadata base . For every overview might by make d poll rendition , which is legitimate for no less than one year . In the event that survey content and/or design don\'t change, then current form and it depiction in Metadata base is usable for one year from now. Every overview contains one or more information section tables or parts which c ould be steady table - with altered number of lines and segments or table with variable number of lines or variable number of segments . Lines and sections f or every part we are describ ing in the Metadata base with their codes and titles . T his data is fundamental for programmed information section application era , information acceptance e.t.c. Last stride in the poll substance and design depiction is cells development. Cells are littlest information unit in overview information preparing. Cells are made as mix of line and segment from study adaptation side and variable from pointers and qualities side.

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Example of horticultural poll

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Name of Questionnaire, list, code ; Respondents (object) code, name and address; Period (year, quarter, month) Name of section Structure of rural insights survey (illustration - altered table) Metadata vault : basic table of measurable pointers , table of traits (characterizations) and table of made variables INDICATOR 1 + ATTRIBUTE I n d i c a t o r s CELL [2010,1] VARIABLE 1 A t r i b u t e s

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Row heading Row\'s code Total Name 1 Name 2 N Name n-1 Name n A B 9999 ISIC 1 code ISIC 2 code … .. ISIC n-1 code ISIC n code Number of workers 1110 … Net turnover 1120 … Other pay 1130 1. Information network - Fixed number of Rows (3) and variable number of Columns (n) (Example) Main practical markers of the financial matters movement

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Name of generation Product code ( HS or SITC ) Produced in regular estimation Sailed in normal estimation Income in USD A B 1 2 3 Product 1 1234567 Product 2 2345678 … . . . . . . . . . Item n-1 4567890 Product n 5678901 2. Information grid - Fixed number of Columns (3) and variable number of Rows (n) (Example) Production of items

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Creating of variables ATTRIBUTES (CLASSIFICATIONS) = VARIABLES INDICATOR + Dimensions ( Vector s) of markers Example : Number of workers + no quality = Number of representatives , complete + Local sort of movement ( ISIC) = Number of representatives in breakdown by sort of action + Regional code = Number of workers in breakdown by districts

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Dimensions of articles and pointers (illustration) Main measurements (vectors) of respondents (items O(t) ) MAIN KIND OF ACTIVITY (ISIC) REGIONS (Code) OWNERSHIP AND ENTERPRENERSHIP (Code) EMPLOYEES GROUP (Code) TURNOVER GROUP (Code) INDICATOR Number of representatives in breakdown by areas Dimensions (vectors) of marker

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Integrated Metadata Driven Quasy Process Oriented Technology

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Metadata base connection with Microdata and Macrodata bases META DATA BASE (REPOSITORY) General portrayal of study Selecting Indicators Selecting Attributes Description of study rendition Creating of Variables Description of sections (information network) Description of lines and segments Linking variables to cells Generation structure for information passage (consequently) Data total capacity ( naturally) Defining of information total tenets MACRO DATABASE MICRO DATABASE IMPORT EX

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