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The Banca d Italia s active statistical meta-information system

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The Banca d Italia s active statistical meta-information system

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    1. The Banca d’Italia’s active statistical meta-information system Vincenzo Del Vecchio, Banca d’Italia, Statistical Services Department delvecchio.vincenzo@insedia.interbusiness.it

    2. The information context

    3. BI statistical inform. system

    4. Common data magnitude order Periodical surveys: on supervised institutions > 50 from Internal sources > 10 Data definitions (arrays): > 100 thousands Data records: total > 3 billion increment rate > 500 million per year

    5. Reuse of software packages

    6. Package Architecture

    7. Advantages

    8. The metadata architecture

    9. CONCEPTS: types SETS Example: the set of the italian cities ELEMENTS Example: a single city (Roma, Milano, ...) VARIABLES A specific meaning of a set (example: the city of birth, the city of residence, the city of work)

    10. CONCEPTS: relations VARIABLE take values in a SET contains ELEMENTS

    11. Part set structure

    12. CONCEPTS: characteristics Abstractions of real world and their relationships Hystorical existing in a time period changing with time Referenced by data definitions

    13. Data: the Statistical Function

    14. Statistical Function: types ARRAY (many times, many groups) TIME SERIES (many times, 1 group) CROSS SECTION (1 time, many groups) ARRAY: like KEY FAMILY in GESMES/CB TIME SERIES: like a single KEY in GESMES/CB ARRAY: like a time slice of a key familyARRAY: like KEY FAMILY in GESMES/CB TIME SERIES: like a single KEY in GESMES/CB ARRAY: like a time slice of a key family

    15. Statistical Function definition Independent variables Classification variables Time variables Dependent variables Attributes Domain Definition Domain Knowledge domain Independent variables = statistical concepts Dependent variables = cell Attributes = attributes Definition domain = cartesian product of list of values + compatibility rule Knowledge domain=updated runtime subspace of definition domain es. dates, reporting subjects, ... Independent variables = statistical concepts Dependent variables = cell Attributes = attributes Definition domain = cartesian product of list of values + compatibility rule Knowledge domain=updated runtime subspace of definition domain es. dates, reporting subjects, ...

    16. Transformation

    17. Transformation

    18. Some algorithm types Aggregation (multi-dimensional) Domain transformation (e.g. MD to TS) Function composition, e.g.: Algebraic & math. (+,-,*, /, log, …) Logical & Comparison (and, or, not, >, =, ... ) Statistical (mean, min, max, …) Time processing (shift, period conversion, …) Relational (join, space & code conversion, …)

    19. Transformation path

    20. Information sys.architecture

    21. Architecture trend

    22. Unique active dictionary Statistics production Quality improvement Data sharing and harmonizing Knowledge management

    23. The BI metainformation system Thank you Vincenzo Del Vecchio, Banca d’Italia, SISC delvecchio.vincenzo@insedia.interbusiness.it

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