=Paper=
{{Paper
|id=Vol-551/paper-24
|storemode=property
|title=Tax and revenue service scenario for ontology matching
|pdfUrl=https://ceur-ws.org/Vol-551/om2009_poster3.pdf
|volume=Vol-551
|dblpUrl=https://dblp.org/rec/conf/semweb/BridaCFG08
}}
==Tax and revenue service scenario for ontology matching==
https://ceur-ws.org/Vol-551/om2009_poster3.pdf
Tax and Revenue Service scenario for Ontology Matching
Stefano Brida1, Marco Combetto, Silvano Frasson2 and Paolo Giorgini3
1
Trentino Riscossioni S.p.A., Trento, Italy
2
Informatica Trentina S.p.A., Trento, Italy
3
D.I.S.I., University of Trento, Italy
Abstract. In this paper we present a scenario for ontology matching
posed by the Trentino Riscossioni S.p.A data integration system
focusing the opportunity to enhance the level of data integration over a
large set of Tax and Revenue industry-specific data sources.
Introduction. The mission of Trentino Riscossioni S.p.A1, a company owned by the
Autonomous Province of Trento, is to promote simplification processes and
harmonize the activity of more than 250 public entities in the province, creating
policies for fair taxation and for operating costs reduction. The need for consistent
and contextual use of the heterogeneous information sources between its offices, the
municipalities and the other public bodies is a fundamental requirement for the
implementation of an accurate and balanced taxation system. In this paper we want to
focus on the possibility offered by matching technology [1] to enhance the in the
present day data integration architecture and increase its flexibility in managing
hundreds of new data sources with reduced software development for each new
sources added. Besides, even if the data integration has been extensively studied in
the database community, according to some recent research works [2,3,4,5], the issue
to improve the automatic schema matching in a data integration scenario for the Tax
and Revenue market is a relative new ground of application. The contribution of this
paper includes a specific scenario focusing several of the basic requirements that have
to be considered in order to build a data integration system capable to support
dynamically hundreds of data sources.
Scenario. The scenario is to make possible the insertion, management and deletion of
new data sources (e.g., new data source from a new provincial database). The
inclusion of a new data source would result in the census of syntactic and semantic
information related to the attributes of the source and in an automatic mapping of
these attributes over the proper attributes of the destination database schema. If the
attributes are not present in the destination schema, the system must support the
design of a schema extension. The source information is collected in a knowledge
base. The search results will be available for at least 2 types of applications: (i) the
business intelligence application that enables the monitoring, tracking and
management of the data quality [6] of the integrated database and four (ii) mission-
critical applications focusing specific business-strategic tasks: assessment revenue,
territory mapping, planning support, final users services. As depicted hereafter in
Figure 1, the information coming from the external data sources is processed through
the SSMB (Semantic Schema/data Matching Box). The SSMB must be able to
calculate the new system status n + 1 through a function based on the previous states
1 http://www.trentinoriscossionispa.it
(n, n-1) in order to support a GUI tool that will provide the interface to the required
information to the Information Engineer and to the calculated matching suggestions
enabling to integrate the sources more rapidly than currently. There are about 10
different data sources for each municipality and 7-8 for each provincial data source.
In the next 2 years, the plan is to integrate about 200 municipalities and other
significant sources.
Monitoring, Municipality 1
Tracking,
Quality Notifications - Up-channel
Municipality N
Business Integrated
Applications Database Data ETL Data
Quality
Car Taxes Authority
Conf
DB
Source Power Authority
DB DB DB Dest Cadastre
SSMB Water Authority
assessment revenue
territory mapping Business
Information GUI Activities
planning support
final users services Engineer
Figure 1 – The scenario description
The process analysis and breakdown provides confidence to motivate an
implementation based on the use of a schema matching workbench like the
HARMONY[7] integration workbench. In fact, beside the other advantages this
approach enables the interoperation and the selection between different and various
prototypes and commercial tools for schema matching and enables the sharing a
common knowledge repository.
Conclusions and future works. We presented the business scenario for a solution
that leverages on matching technology in order to scale-out over hundreds of data
sources. Future works proceed in the following directions: (i) formalization of the
scenario, (ii) evaluation and test of the HARMONY workbench features, and (iii)
development of a specific working prototype for Trentino Riscossioni S.p.A.
Acknowledgments. This work has been supported by Trentino Riscossioni S.p.A and
by Informatica Trentina S.p.A.-TasLab Network Project funded by the EU FSE under
the act n. 1637 (30.06.2008) of the Autonomous Province of Trento .
References
[1] J. Euzenat and P. Shvaiko. Ontology matching. Springer, 2007.
[2] A. Bitterer, M. A. Beyer, and T. Friedman. Magic Quadrant for Data Integration
Tools. Gartner, 2008.
[3] P. Shvaiko and J. Euzenat. Ten Challenges for Ontology Matching. In Proc. of
ODBASE, 2008.
[4] K. Smith, P. Mork, L. Seligman, A. Rosenthal, M. Morse, C. Wolf, D. Allen, M.
Li: The Role of Schema Matching in Large Enterprises. In Proc. of CIDR, 2009.
[5] Y. Asnar, P. Giorgini, P. Ciancarini, R. Moretti, M. Sebastianis, N. Zannone.
Evaluation of Business Solutions in Manufacturing Enterprises. In International
Journal on Business Intelligence and Data Mining, Inderscience, 2008
[6] Jeffery G. Watson. Data Quality Essentials. Uni. of Wisconsin-Madison, 2006
[7] P. Mork, L. Seligman, A. Rosenthal, J. Korb, and C. Wolf. The Harmony
Integration Workbench. Journal on Data Semantics, 2008.