towards intelligent data insights in central banks: challenges and opportunities for declarative...
TRANSCRIPT
Towards intelligent data insights in central banks
Roma, 24th Feb 2017
Luigi Bellomarini, IT Department
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Challenges and opportunities for declarative languages
WHATINSIGHTS
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• Credit and credit 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
THEDATAPRODUCTIONPROCESS
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BA
NK
S IN
PU
T L
AY
ER
BanksOperational
SystemsB
AN
KS
OU
TP
UT
LA
YER
PR
IMA
RY
REP
OR
TIN
G
Transformations by banks
NA
TIO
NA
L ST
AT
IST
. PR
OD
UC
TIO
N
SEC
ON
DA
RY
REP
OR
TIN
G
SUP
RA
NA
TIO
NA
L
ST
AT
IST
ICA
L P
RO
DU
CT
ION
Transformations by central banks
Transformations by international institutions
STANDARDIZATION
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• 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 ModelVTL
Operand Operand
Expression
Result
VTL:ASTANDARDLANGUAGE(FROMSDMXINITIATIVE)
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High-level and business oriented• 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 = 9.876.543Deposits = 10.234.567
Loans value type = measuredDeposits value type = estimated
Naples
Private
31 Dec 2010
Dimensions Measures Attributes
City Reference Date
Sector Loans DepositsLoans 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
… … … … … … ,,,
VTL– AGRAPHOFTRANSFORMATIONS
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Banks & OFIs reports …
D1D2
D3 D4D5
T1
T3T2
D10D12
D13D15
D17D16T13
T12T14
Other data sources
D51D52
T53T52
T51Economic research models
D54D53
T54
C.C.R.
D21D22
D23D24T22
T21
D60D61
Statistical bulletinT60
T61Statistical products
D70T71
T70T72D71 D72
D41T42
T41D42
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!
EXECUTIONPLATFORMS- @BankofItaly
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VTLRGDP := 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 PFROM PQR C1 , RGDPPC C2
WHERE C1.Q = C2.Q AND C1.R = C2.R
PQR(q,r,p), RGDPPC(q,r,g) à ∃𝑧RGDP(q,r, z)
User specification
Logical representation
IT implementation
FROMASETOFRULES...TOWARDSAKNOWLEDGEBASE
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Metadata-driven systemDeclarative representationActive dictionaryIntegrated approachNO INFERENCE
• Knowledge base generation
• First Order Languages
REASONING
AICognitive Computing
CREDITS
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• SDMX TWG and 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 processing, 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/