![](bg7.png)
various subjects such as in the areas of public policy,
security, economy, education. (Ludwig, 2005)
In Figure 6, we present our proposed centralized
ontology repository, which adjusts its ontology
according to taxonomy of the four BIs that are
integrated. Moreover, in case of inconsistency of a
certain BI system, the centralized ontology
repository may ignore it and consider only the
remaining BI systems.
Additional tasks may be performed by the
administrator in order to create pre-defined
indicators for general use inside the centralized
ontology repository. These tasks fulfill the
requirements proposed by Berthold et al. (2010).
Although our architecture can be easily built,
some questions related to performance are identified.
In this sense, we point out some future
improvements to solve these questions: the use of
optimized mechanisms of SOA transport, the
implementation of cache schemas in OLAP servers
and data services, the setting of restrictions on the
amount of data that can be generated, the
implementation of compression techniques to
transfer data from the Data Service layer to the
OLAP layer.
Another crucial aspect is the capacity to fully
automate tasks in the aligning and merging
processes. The commitment to fully implement the
automation in a sole step seems to be unreasonable,
since this process requires information exchanges
regarding similarities, an activity that still imposes
human intervention. Therefore, it is required a
mediator to check if a minimum consistency level
has been reached, otherwise an automated solution
may affect existing models of the participants.
Figure 6: Example of the proposed centralized ontology
repository for integrating four BI systems.
However, this effort can be minimized if the
selected elements for the ontology layer are able to
use a rich combination of different approaches for
ontology integration, such as measuring the
similarity between texts, extraction of text
keywords, execution of language-based analytical
methods, identification of relationships between
words, evaluation of similarity between types of data
with assessments in domains and value ranges,
general structural and taxonomic analysis,
integration of data with analysis of key properties,
and graphical mapping.
5 CONCLUSIONS
In this paper we propose a collaborative BI
environment architecture in which the composition
and treatment of analytical queries are based on the
interoperation of a centralized repository of concepts
and decentralized data services.
Our approach departs from a conventional BI
environment in several respects, but mainly its two
founding principles. First, our proposal is based
upon the integration of heterogeneous semantic
concepts so as to compose upper ontologies. In
addition, source data retrieval is accomplished
according to fundamental SOA practices, such as
low coupling and abstraction.
This way, our architecture constitutes an
improved alternative to the conventional BI
structure, offering a distributed solution that is able
to integrate heterogeneous information concepts.
Finally, as a future work, we propose to integrate
BI systems of the Brazilian government based on the
Centralized Ontology Repository in Figure 6.
REFERENCES
Abels, S., Haak, L., Hahn, A., (2005). Identification of
Common Methods Used for Ontology Integration
Tasks. University of Oldenburg - Business Information
Systems- Oldenburg, Germany.
Bennett, K., Layzell, P., Budgen, D., Brereton, P.,
Macaulay, L., Munro, M., (2000). Service-based
software: the future for flexible software. Asia-Pacific
Software Engineering Conference, vol. 0, p. 214.
Berthold, H., Rösch, P., Zöller, S., Wortmann, F.,
Carenini, A., Campbell, S., Bisson, P., Strohmaier, F.,
(2010). An Architecture for Ad hoc and Collaborative
Business Intelligence. ACM International Conference
Proceeding Series, ACM.
Böhringer, M., Gluchowski, P., Kurze, V., Schieder, C.,
(2010). A Business Intelligence Perspective on the
Future Internet. Americas Conference on Information
Systems, AMCIS 2010 Proceedings, Paper 267.
Chaudhuri, S., Dayal, U., (1997) An overview of data
warehousing and OLAP technology, ACM SIGMOD
Record, vol.26, no.1, pp.65-74, March 1997, doi:
10.1145/248603.248616.
Chaudhuri, S., Dayal, U., Narasayya, V., (2011) An
overview of business intelligence technology.
ArchitectureofaCollaborativeBusinessIntelligenceEnvironmentbasedonanOntologyRepositoryandDistributedData
Services
105