Model integration extends the scope of model management to include the dimension of manipulation as well. This invariably leads to comparisons with database theory. Model integration is viewed from four perspectives: Organizational, definitional, procedural, and implementational. Strategic modeling is discussed as the organizational motivation for model integration. Schema and process integrati...

Markup languages, representations, schemas, and tools have significantly increased the ability for organizations to share their information. Languages such as the Extensible Markup Language (XML) provide a vehicle for organizations to represent information in a common, machine-interpretable format. Furthermore languages, such as the Document Type Definition Language (DTD) and XML Schema Definit...

Under the Unique Name Assumption (UNA), users need to have shared agreements on signifiers to use in schema or data, e.g. to use “genre” and not “type” to refer to a movie’s category. Agreements are difficult in open environments such as datasets on the web, open data, and crowd-sourced databases, thus this assumption can be invalid. Schema matching and data integration can be limited in respon...

Schema matching is the task of finding semantic correspondences between elements of two schemas. It takes two schemas as input and returns a mapping that identifies corresponding elements in the two schemas. Schema matching is an important and vital step in many schema and data translation and integration applications, such as integration of web data sources, data warehousing, XML message mappi...

The task of a data integration system is to combine the data residing at different, autonomous sources, and providing the user with a unified view of these data, called global schema. Users query the global schema, while the system carries out the task of suitably accessing different sources and assembling the data retrieved at each source into the final answer to the query. Since sources are i...

The heterogeneity of information sources can syntactic or semantic. The former can be due, for example to platform mismatches and different Database Management Systems, and, the latter to different database schemas and ontological mismatches [1]. Syntactic heterogeneity is catered for by middle-ware components known as wrappers, which provide a relational view of the data stored in the sources....

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...

Data on the Web in HTML tables is mostly structured, but we usually do not know the structure in advance. Thus, we cannot directly query for data of interest. We propose a solution to this problem based on document-independent extraction ontologies. The solution entails elements of table understanding, data integration, and wrapper creation. Table understanding allows us to recognize attributes...

Schema integration is the basis for successfully building a database federation. Current proposals directly integrate the diierent schemata using a semantical powerful data model, for example an extended ER model or an object-oriented model. We propose instead to use for integration a semantical poor model and build semantically rich representations as external views only. This approach enables...

We propose an integration of XML with F-Logic, a deductive object-oriented database framework. The F-Logic data model is in fact a semistructured data model, exhibiting many similarities with the XML/DOM data model: there is a canonical mapping from XML to a fragment of F-Logic. The advantages of the integration are that the full expressiveness of F-Logic (and the functionality of the FLORID sy...