1. Italian National Institute of Statistics - Istat
The Italian Integrated System of Statistical Registers On the Design of an Ontology-based Data Integration Architecture
R. Radini M. Scannapieco , G.Garofalo
Italian National Institute of Statistics - Istat
Monica Scannapieco – Brussels, NTTS, March 2017

2. Outline Introduction to ISSR OBDM and examples Data architecture
Correspondence with EARF
DV vs DW
Conclusions
Monica Scannapieco – Brussels, NTTS, March 2017

3. ISSR – Italian Integrated System of Statistical Registries
Istat engaged a modernization programme aimed at a significant revision of the statistical production
One of the main pillars of this revision is the design of production processes based on an Integrated System of Statistical Registers
Single logical environment to support the consistency of statistical production processes in Istat, in particular consistency in “identification” and “estimation” for the whole integrated system of units and variables
Monica Scannapieco – Brussels, NTTS, March 2017

4. ISSR: Types of Registers
RSE (Extended registers) extends the information of a specific RSB on a specific RSB’s population
RST (Thematic registers) supports more statistical processes through a consistent and shared treatment on some topics
RSB (Base registers) contains several statistical populations and the minimum set of variables useful to characterize stat units
Monica Scannapieco – Brussels, NTTS, March 2017

5. OBDM Ontology Based Data Management System
Ontology (or computational ontology): conceptual data representation expressed through «computational» languages
In mathematical logic: assiomatic first order theory expressable in description logic
OBDM is an integration system where the usual ER global schema is replaced by the conceptual model of the application domain formulated as an ontology
Monica Scannapieco – Brussels, NTTS, March 2017

6. OBDM Architecture Main features
Data source transparency property (called data virtualization by IT platform)
Global view
Consistency
Ontology
Mapping
Data source 1
Data source 3
Data source 2
Three-level architecture:
Ontology, Sources, Mapping
Monica Scannapieco – Brussels, NTTS, March 2017

7. Excerpt of the Ontology of the Working Relationships
Employee
Self-employee
Worker
Monica Scannapieco – Brussels, NTTS, March 2017

8. Excerpt of the Population Ontology
Family registry
Common law family
Family
Individual
Monica Scannapieco – Brussels, NTTS, March 2017

9. Data Integration: same concept
Individual (Population Ontology)
Individual (Working relationships ontology)
Monica Scannapieco – Brussels, NTTS, March 2017

10. Querying over the ontology
Query: We would like to query for people that have the residence in a certain region and classify them by age, educational degree and employment condition We don’t have to know how information are stored in the sources!
Monica Scannapieco – Brussels, NTTS, March 2017

11. by employment condition
Query
Ontology
Mapping
Mapping
Query rewritten over the sources
RS of
Individuals
RS of Labour
people that
have residence in a certain region classified by age and educational degree
by employment condition
Monica Scannapieco – Brussels, NTTS, March 2017

12. High expressive power
It is possible to give different definition of a concept dependending on the istance
It is possible to express different constraints related to each definition
CorporationManager- Labour Force
Employee
Self-employee
Corporation Manager NationalAccount
CorporationManager has a different semantics according to the domain
Monica Scannapieco – Brussels, NTTS, March 2017

13. Data architecture
Compliance to EARF (Enterprise Architecture Reference Framework)
Metadata Management
Primary Data Storage
Quality Assessment
Unitary Metadata System
Logical centralization of ISSR
Data consistency ODBM
Monica Scannapieco – Brussels, NTTS, March 2017

14. Data architecture: IT View
Features DV DW
Storage of Historical Data NO
YES
Capture Every Change in Production Data
(requires integration with CDC)
Multi-Dimensional Data Structures
Data Pre-Aggregation
Query performance on large amounts of data
SLOW
(relative to DW)
FAST
(relative to DV)
Data Integration on Demand
Operational Cost
LOW
HIGH
Time-To-Market
Easy to Make Changes
Dependence on IT
Monica Scannapieco – Brussels, NTTS, March 2017

15. Conclusions EA approach for ISSR design and implementation
ISSR Data Architecture: Hybrid solution with DV and DW
E.g. DV-based data architecture with DW for historical data and dissemination
Next steps:
Prototypes of RSB Individual, Families and Cohabitations and RST Working Relationships
Guidelines for the Management of the Integrated System of Statistical Registers
Monica Scannapieco – Brussels, NTTS, March 2017