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![EXECUTION PLATFORMS - @Bank of Italy
7
VTL
RGDP := PQR * RGDPPC
tmp <- merge(PQR,RGDPPC,by=c("q","r")) tmp$i <-
tmp["p"] * tmp["g"]
TGDP <- tmp[-c("p","g")]
Rgdp = get_tab(pqr * rgdppc)
INSERT INTO RGDP(Q,R,P)
SELECT C2.Q AS Q, C2.R AS R, C1.P*C2.G AS P
FROM PQR C1 , RGDPPC C2
WHERE C1.Q = C2.Q AND C1.R = C2.R
PQR(q,r,p), RGDPPC(q,r,g) à ∃𝑧 RGDP(q,r, z)
User specification
Logical representation
IT implementation](https://image.slidesharecdn.com/vtlbellomarini-170308170051/75/Towards-intelligent-data-insights-in-central-banks-challenges-and-opportunities-for-declarative-languages-Luigi-Bellomarini-7-2048.jpg)
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Towards intelligent data insights in central banks: challenges and opportunities for declarative languages - Luigi Bellomarini
This document discusses intelligent data insights in central banks. It summarizes the data and statistical production processes used by central banks, including credit risk data, securities holdings, and monetary financial institution statistics. It also describes standardization efforts including validation and transformation languages to guide data extraction and processing. Finally, it provides examples of execution platforms used by the Bank of Italy to implement logical representations and transformations on statistical data.
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Towards intelligent data insights in central banks: challenges and opportunities for declarative languages - Luigi Bellomarini
- 1. Towards intelligent datainsights in central banks Roma, 24th Feb 2017 Luigi Bellomarini, IT Department 1 Challenges and opportunities for declarative languages
- 2. WHAT INSIGHTS 2 • Credit andcredit risk data & Institutional Units register • Securities Holdings Statistics & Securities register • Monetary Financial Institutions Balance Sheets Statistics • Monetary Financial Institutions Interest Rates Statistics • Balance of Payments • National Accounts • Single Supervisory Mechanism • Regulatory frameworks
- 3.
- 4. STANDARDIZATION 4 • Of processes,models and languages • Guide the process in the banks • Extract the data into harmonized models • Standardize the transformations • Validation & Trasformation Language (VTL) GSBPM Information ModelProcess Model VTL Operand Operand Expression Result
- 5. VTL: A STANDARD LANGUAGE (FROM SDMX INITIATIVE) 5 High-level and businessoriented • Fully declarative approach • Logic and functional paradigms Mathematical functions are first-class objects • VTL manipulates data as mathematical functions • Based on operators (higher-order functions) Sector City Reference Date Loans = 9.876.543 Deposits = 10.234.567 Loans value type = measured Deposits value type = estimated Naples Private 31 Dec 2010 Dimensions Measures Attributes City Reference Date Sector Loans Deposits Loans value type Deposits value type Naples 2010 12 31 private 9.876.543 10.234.567 measured estimated Naples 2010 12 31 public 543.210 654.321 measured measured Naples 2009 12 31 private 9.210.876 10.987.654 estimated estimated Naples 2009 12 31 public 876.543 1.654.123 measured measured … … … … … … Rome 2010 12 31 private 1.234.567 1.546.897 measured measured … … … … … … ,,,
- 6. VTL – A GRAPH OF TRANSFORMATIONS 6 Banks &OFIs reports … D1 D2 D3 D4 D5 T1 T3 T2 D10 D12 D13 D15 D17 D16T13 T12 T1 4 Other data sources D51 D52 T53 T52 T51 Economic research models D54 D53 T54 C.C.R. D21 D22 D23 D24T22 T21 D60 D61 Statistical bulletin T60 T61 Statistical products D70 T71 T70 T72D71 D72 D41 T42 T41 D42 Supervision models D4 = get ( D1_LOANS_FLOW, keep (DATE, BANK, AMOUNT), sum (AMOUNT)) D5 = get ( D2_LOANS_STOCK, keep (DATE, BANK, AMOUNT), sum (AMOUNT)) D6_CHECK = check( D5 = lag(D5, -1) + D4) It’s a DAG!
- 7. EXECUTION PLATFORMS - @Bank of Italy 7 VTL RGDP :=PQR * RGDPPC tmp <- merge(PQR,RGDPPC,by=c("q","r")) tmp$i <- tmp["p"] * tmp["g"] TGDP <- tmp[-c("p","g")] Rgdp = get_tab(pqr * rgdppc) INSERT INTO RGDP(Q,R,P) SELECT C2.Q AS Q, C2.R AS R, C1.P*C2.G AS P FROM PQR C1 , RGDPPC C2 WHERE C1.Q = C2.Q AND C1.R = C2.R PQR(q,r,p), RGDPPC(q,r,g) à ∃𝑧 RGDP(q,r, z) User specification Logical representation IT implementation
- 8.
- 9. FROM A SET OF RULES ... TOWARDS A KNOWLEDGE BASE 9 Metadata-driven system Declarative representation Activedictionary Integrated approach NO INFERENCE • Knowledge base generation • First Order Languages REASONING AI Cognitive Computing
- 10. CREDITS 10 • SDMX TWGand VTL task force (www.sdmx.org) • Statistical Data and Concept Representation The Banca d’Italia’s Active Statistical Metainformation System, Modelling Levels in the Statistical Information System of Bank of Italy, The “Matrix” Model: unified model for statistical representation and proces sing, Vincenzo Del Vecchio, Fabio Di Giovanni et al. (https://www.bancaditalia.it/ statistiche/raccolta-dati/sistema-informativo-statistico/index.html) • BIRD project (http://banks-integrated-reporting-dictionary.eu) • GSBPM (http://www1.unece.org/stat/platform/display/GSBPM/GSBPM+v5.0) https://creativecommons.org/licenses/by-nc-sa/3.0/