In a data integration process, an important step after schema matching and duplicate detection is data fusion. It is concerned with the combination or merging of different representations of one real-world object into a single, consistent representation. In order to solve potential data conflicts, many different conflict resolution strategies can be applied. In particular, some representations ...

Interoperability will remain as an unsolvable problem forever at least until when you read this dissertation. Actually, we have spent unbelievable amounts of money due to lack of interoperability among any systems. According to the NIST reports, the estimated interoperability cost nearly reaches to a score billion US dollars just in two industries (i.e., the capital industry and part of the aut...

Heterogeneous multimedia contents are annotated by a sharable formal conceptualization, often called ontology, and these contents, regardless of their media, become sharable resources/instances. Integration of the sharable resources and acquisition of diverse knowledge is getting researchers’ attention at a rapid pace. In this regard, MPEG-7 standard convertible to semantic Resource Description...

AbstrAct: Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, " given two schemas, S i and S j , find the most plausible correspondences between the elements of S i and S j , exploiting all available information, such as the schemas, instance data, and auxiliary sources " [24]. Given the ...

Schema matching is recognized to be one of the basic operations required by the process of data and schema integration, and thus has a great impact on its outcome. We propose a new approach to combining matchers into ensembles, called Schema Matcher Boosting (SMB). This approach is based on a well-known machine learning technique, called boosting. We present a boosting algorithm for schema matc...

In this paper, we propose a general model of schema matching based on the following ideas: on the one hand, a schema is viewed as a context (namely as a partial and approximate representation of the world from an agent’s perspective); on the other hand, a schema cannot be assigned any arbitrary interpretation, as the meaning of the expressions used to label nodes (and possibly arcs) may be cons...

Data integration systems often provide a uniform query interface, called a mediated schema, to a multitude of data sources. To answer user queries, such systems employ a set of semantic matches between the mediated schema and the data-source schemas. Finding such matches is well known to be difficult. Hence much work has focused on developing semi-automatic techniques to efficiently find the ma...

Integration of multiple heterogeneous data sources continues to be a critical problem for many application domains and a challenge for researchers world-wide. Schema matching, a fundamental aspect of integration, has been a well-studied problem. However researchers have, for the most part, concentrated on the development of different schema matching algorithms, and their performance with respec...

To improve the matching accuracy, most of the schema matching tools aggregate the results obtained by several matching algorithms. The quality of matches depends on the adequacy and of the number of match algorithms used, and their combination and aggregation strategy. However, this aggregation entails several drawbacks on the performance, quality and tuning aspects. In this paper, we present a...

Schema matching is a crucial task to gather information of the same domain. This is even more the case when dealing with data warehouses, where a large number of data sources are available and require matching and integration. However, the matching process is still largely performed manually or semiautomatically, thus discouraging the use of large scale integration systems. Indeed, these large ...