53 research outputs found
Matching operators in data integration
No full textThis paper discusses the role of matching operators in Data Integration process. The paper proposes acategorization of matching operators, with the aim of distinguishing different relations to the possiblegoals of an integration process and their impact on the implementation of the mappings defining thisintegration. In order to lead the discussion some example
An FCA-based mapping generator
No full textWe present an overview of ODDI an Ontology Driven Data Integration system based on Formal concept analysis and instance comparison. Data Integration systems are used to integrate heterogeneous data sources in a single view. Following the Global-as-View approach the data is retrieved through a common conceptualization, that in our system is modeled as an ontology. This paper focuses on the problem of matching and mapping of elements between the common ontology and the data sources. The problem of query translation is also mentioned for sake of completeness, but it will be treated in detail in a future paper. Recent works on Business Intelligence do highlight the need oftrustable and sound data access systems. We propose a system based on FCA to generate the mapping to the common representation and the relations between the heterogeneous data sources
An experiment to study the colour constancy behaviour in human observers and its algorithmic computation
No full textIndagini sulla coltura in vitro di alcuni genotipi di castagno cinese (C. mollissima Blume) : Atti Convegno Nazionale sul Castagno
No full textIndagini sulla coltura in vitro di alcuni genotipi di castagno cinese (C. Mollissima Blume)
No full textSemantics-aware matching strategy (SAMS) for the ontology mediated data integration (ODDI)
No full textData integration systems are used to integrate heterogeneous data sources in a single view. Recent work on business intelligence highlights the need of on-time, reliable and sound data access systems relying on methods based on semi-automatic procedures. A crucial factor for any semi-automatic algorithm is that of the matching strategy. Different categories of matching operators carry different semantics. For this reason, combining them into a single strategy is a non-trivial process that has to take into account a variety of options. This paper presents SAMS, a matching strategy based on a semantics-aware categorisation of matching operators that allows to group similar attributes on a semantically-rich form
ODDI : ontology-driven data integration
No full textData Integration systems are used to integrate heterogeneous data sources in a single view. Recent works on Business Intelligence do highlight the need of on-time, trustable and sound data access systems. This require for method based on a semi-automatic procedure that can provide reliable results. A crucial factor for any semi automatic algorithm is based on the matching operators implemented. Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process that have to take into account a variety of options.
This paper proposes a solution based on a categorization of marching operators that allow to group similar attributes on a semantic rich form. The validation of the system have demonstrate how the aggregation of matching operators is not a trivial problem because traditional aggregators produce a compensation effect on operators that can have very different informative values. For this reason this work is now evolving thought the implementation of aggregators based on logic theories, able to distinguish different properties of matching operators
ODDI : a framework for semi-automatic data integration
No full textRecent works on Business Intelligence do highlight the need of on-time, trustable and sound data access systems.
Moreover the application of these systems in a flexible and dynamic environment requires for an approach based on automatic procedures that can provide reliable results.
A crucial factor for any automatic data integration system is the matching process.
Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process that have to take into account a variety of options.
This paper proposes a solution based on a categorization of matching operators that allow to group similar attributes on a semantic rich form. This way we define all the information need in order to create a mapping. Then Mapping Generation is activated only on those set of elements that can be queried without violating any integrity constraints on data
Using ontologies to map concept relations in a data integration system
No full textIn this paper we propose a Data Integration System based on an ontology as Global Representation. The paper briefly introduces the motivations and the benefits that the use of an ontology brings to a Data Integration System, which is also formally defined. The paper, in particular, focuses on the limitation of the proposed system to handle the different relations that can exist between concepts of an ontology. We divide the relations that can be defined in an ontology in two different sets: Mapped relations, which we consider atomic relations and Derived relations, which can be generated by combining mapped relations using SWRL rules. Some examples of the different kind of relations are reported to clarify the concepts and a new definition of Mapping in the Data Integration System is proposed, in order to define the atomic relations. The paper ends with considerations about the problems that still need to be considered
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