Discovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matchin...

Consider the problem of migrating a company’s CRM or ERP database from one application to another, or integrating two such databases as a result of a merger. This problem requires matching two large relational schemas with hundreds and sometimes thousands of fields. Further, the correct match is likely complex: rather than a simple one-to-one alignment, some fields in the source database may ma...

Discovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matchin...

Thesaurus is used in many Information Retrieval (IR) applications such as data integration, data warehousing, semantic query processing and schema matching. Schema matching or mapping is one of the most important basic steps in data integration. It is the process of identifying the semantic correspondence or equivalent between two or more schemas. Considering the fact of the existence of many t...

This dissertation lies in the research area of schema integration: the problem of combining the data of different data sources by creating a unified representation of these data. Two core issues in schema integration are schema matching, i.e. the identification of correspondences, or mappings, between input schema objects, and schema merging, i.e. the creation of a unified schema based on the i...

In the last few years, we have witnessed a rapid growth in distributed database processing. We consider the question of data integration: how we can integrate distributed schemas into a new one and query just that new schema without losing the ability to retrieve data from the original schemas. The area in which we try to answer that question is federated databases, where the original heterogen...

Schema matching algorithms aim to identify relationships between database schemas, which underpin a wide range of model management operations, such as merge, compose and difference, as required for tasks such as data integration, exchange or schema evolution. However, the results of matching algorithms are typically expressed as lowlevel, 1-to-1 associations between pairs of attributes or entit...

Match cardinality aspect in schema matching is categorized as simple element level matching and complex structural level matching. Simple matching comprises of 1:1, 1:n and n:1 match cardinality, whereas n:m match cardinality is considered to be complex matching. Most of the existing approaches and tools give good 1:1 local and global match cardinality but lack the capabilities for handling the...

Data models created to store or exchange data between systems are, by nature, heterogeneous in terms of standards and representation. In this research project, a novel approach called Semantic Markers, along with ontologies are used to integrate the heterogeneous data schema common in manufacturing systems. Once integrated, the schema can provide interfaces for global queries, synchronization a...

In this paper, we present YAM, a schema matcher factory. YAM (Yet Another Matcher) is not (yet) another schema matching system as it enables the generation of a la carte schema matchers according to user requirements. These requirements include a preference for recall or precision and a training data set (a set of expert correspondences or a domain of interest). YAM uses a knowledge base that i...