Informationen haben oft verschiedene Quellen und Formate, ungeachtet ihres Inhaltes. Eine Vereinheitlichung der Strukturen dieser Daten zur Überführung in Datenbanken zum Zwecke der Speicherung und Auswertung ist dann nicht ohne Weiteres möglich. Auch digitalisierte Textinformationen können in unterschiedlichen Formaten vorliegen. Für eine Datenintegration, insbesondere die Überführung von XML-Inhalten in ein bestehendes Datenbankschema, soll ein Schema-Mapping durchgeführt werden. Verschiedene Verfahren werden implementiert und ihre Funktionsweise auf Brauchbarkeit bei unterschiedlichen Input-Formaten und -Kombinationen untersucht.

Data Exchange is the problem of transforming data in one format (the source schema) into data in another format (the target schema). Its core component is a schema mapping, which is a high level specification of how such transformation should be done. Relational data exchange has been extensively studied, but exchanging XML data have been paid much less attention. The goal of this thesis is to develop a theory of XML data exchange with expressive schema mappings, extending a previous work using restricted mappings. Our mapping language is based on tree patterns that can use horizontal navigation and data comparison in addition to downward navigation. First we look at static analysis problems concerning a single mapping. More specif- ically, we consider consistency problems with different flavours. One such problem, for instance, asks if any tree has a solution under the given mapping. Then we turn to analyse the complexity of mapping themselves, i.e., recognising pairs of trees such that the one is mapped to the other. For both problems, we provide classifications based on sets of features used in the mappings. Second we investigate the composition of XML schema mappings. Generally it is hard, or rather simply impossible, to achieve closure under composition in XML settings unlike in relational settings. Nevertheless we identify a class of XML schema mappings that is closed under composition. Lastly we consider the problem of query answering. It is important to exchange data so that we can feasibly answer queries while it often leads to intractability. We identify the dividing line between tractable and intractable cases: answering queries with extended features is always intractable while tractability of answering simple queries can be retained in extended mappings.

Dataspaces aim to remove upfront cost in the generation of the schema mappings that reconcile schematic heterogeneities, and to incrementally improve the generated mappings based on user feedback. The reconciliation of schematic heterogeneities is a crucial step for translating queries between a mediating schema and data sources. The generation of schema mappings depends on the elicitation of conceptually equivalent schema constructs and information on schematic heterogeneities. Furthermore, many dataspace operations manipulate associations between schemas, for example for generating a global schema to mediate user queries. With a view to minimizing upfront costs associated with understanding the relationships between schemas, many schema matching algorithms and tools have been developed for postulating equivalent schema constructs. However, they derive simple associations between schema constructs, and do not provide rich information on schematic heterogeneities. Without manual refinement, the elicitation of conceptually equivalent schema constructs and schematic heterogeneities may create uncertainties that must be managed.The schematic correspondences captures a wide range of one-to-one and many-to-many schematic heterogeneities. This thesis investigates the use of schematic correspondences as a central component in a dataspace management system. To support query evaluation in a dataspace in which relationships between schemas are represented using schematic correspondences, we propose a mechanism for automatically generating schema mappings from the schematic correspondences. We then characterise model management operators, which can underpin the bootstraping and maintenance of dataspaces, over schematic correspondences. To support the management of uncertainty in dataspaces, we propose techniques for quantifying uncertainty in the equivalence of schema constructs from evidence in the form of similarity scores and user feedback, and provide a flexible framework for incrementally updating the uncertainties in the light of new evidence.

We propose a framework for semi-automatically verifying relational database schema mappings for data exchange. Schema mappings for data exchange formally describe how to move data between a source and target database. State-of-the-art schema mapping tools propose several mappings, but require user intervention to determine their semantic correctness. For this, the user must understand the domain the schemas represent and the meanings of individual schema elements in relation to the domain. Our framework eases the task of understanding the domain and schemas and performs preliminary mapping verification. We use a readable, expressive, and formal conceptual model - a domain ontology - to model the source and target schema domain. We model the schema semantics by annotating schema elements with ontology elements. Our mapping verification algorithm rewrites mappings as statements in terms of the ontology, and uses a reasoner to check that the statements are entailed by the ontology.

Our thesis investigates a mechanism for representing schema mapping by tabular forms and checking utility of the new representation. Schema mapping is a high-level specification that describes the relationship between two database schemas. Schema mappings constitute essential building blocks of data integration, data exchange and peer-to-peer data sharing systems. Global-and-local-as-view (GLAV) is one of the approaches for specifying the schema mappings. Tableaux are used for expressing queries and functional dependencies on a single database in a tabular form. In our thesis, we first introduce a tabular representation of GLAV mappings. We find that this tabular representation helps to solve many mapping-related algorithmic and semantic problems. For example, a well-known problem is to find the minimal instance of the target schema for a given instance of the source schema and a set of mappings between the source and the target schema. Second, we show that our proposed tabular mapping can be used as an operator on an instance of the source schema to produce an instance of the target schema which is `minimal' and `most general' in nature. There exists a tableaux-based mechanism for finding equivalence of two queries. Third, we extend that mechanism for deducing equivalence between two schema mappings using their corresponding tabular representations. Sometimes, there exist redundant conjuncts in a schema mapping which causes data exchange, data integration and data sharing operations more time consuming. Fourth, we present an algorithm that utilizes the tabular representations for reducing number of constraints in the schema mappings. At present, either schema-level mappings or data-level mappings are used for data sharing purposes. Fifth, we introduce and give the semantics of bi-level mapping that combines the schema-level and data-level mappings. We also show that bi-level mappings are more effective for data sharing systems. Finally, we implemented our algorithms and developed a software prototype to evaluate our proposed strategies.

Our thesis investigates a mechanism for representing schema mapping by tabular forms and checking utility of the new representation. Schema mapping is a high-level specification that describes the relationship between two database schemas. Schema mappings constitute essential building blocks of data integration, data exchange and peer-to-peer data sharing systems. Global-and-local-as-view (GLAV) is one of the approaches for specifying the schema mappings. Tableaux are used for expressing queries and functional dependencies on a single database in a tabular form. In our thesis, we first introduce a tabular representation of GLAV mappings. We find that this tabular representation helps to solve many mapping-related algorithmic and semantic problems. For example, a well-known problem is to find the minimal instance of the target schema for a given instance of the source schema and a set of mappings between the source and the target schema. Second, we show that our proposed tabular mapping can be used as an operator on an instance of the source schema to produce an instance of the target schema which is `minimal' and `most general' in nature. There exists a tableaux-based mechanism for finding equivalence of two queries. Third, we extend that mechanism for deducing equivalence between two schema mappings using their corresponding tabular representations. Sometimes, there exist redundant conjuncts in a schema mapping which causes data exchange, data integration and data sharing operations more time consuming. Fourth, we present an algorithm that utilizes the tabular representations for reducing number of constraints in the schema mappings. At present, either schema-level mappings or data-level mappings are used for data sharing purposes. Fifth, we introduce and give the semantics of bi-level mapping that combines the schema-level and data-level mappings. We also show that bi-level mappings are more effective for data sharing systems. Finally, we implemented our algorithms and developed a software prototype to evaluate our proposed strategies.

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Our thesis investigates a mechanism for representing schema mapping by tabular forms and checking utility of the new representation. Schema mapping is a high-level specification that describes the relationship between two database schemas. Schema mappings constitute essential building blocks of data integration, data exchange and peer-to-peer data sharing systems. Global-and-local-as-view (GLAV) is one of the approaches for specifying the schema mappings. Tableaux are used for expressing queries and functional dependencies on a single database in a tabular form. In our thesis, we first introduce a tabular representation of GLAV mappings. We find that this tabular representation helps to solve many mapping-related algorithmic and semantic problems. For example, a well-known problem is to find the minimal instance of the target schema for a given instance of the source schema and a set of mappings between the source and the target schema. Second, we show that our proposed tabular mapping can be used as an operator on an instance of the source schema to produce an instance of the target schema which is `minimal' and `most general' in nature. There exists a tableaux-based mechanism for finding equivalence of two queries. Third, we extend that mechanism for deducing equivalence between two schema mappings using their corresponding tabular representations. Sometimes, there exist redundant conjuncts in a schema mapping which causes data exchange, data integration and data sharing operations more time consuming. Fourth, we present an algorithm that utilizes the tabular representations for reducing number of constraints in the schema mappings. At present, either schema-level mappings or data-level mappings are used for data sharing purposes. Fifth, we introduce and give the semantics of bi-level mapping that combines the schema-level and data-level mappings. We also show that bi-level mappings are more effective for data sharing systems. Finally, we implemented our algorithms and developed a software prototype to evaluate our proposed strategies.

Our thesis investigates a mechanism for representing schema mapping by tabular forms and checking utility of the new representation. Schema mapping is a high-level specification that describes the relationship between two database schemas. Schema mappings constitute essential building blocks of data integration, data exchange and peer-to-peer data sharing systems. Global-and-local-as-view (GLAV) is one of the approaches for specifying the schema mappings. Tableaux are used for expressing queries and functional dependencies on a single database in a tabular form. In our thesis, we first introduce a tabular representation of GLAV mappings. We find that this tabular representation helps to solve many mapping-related algorithmic and semantic problems. For example, a well-known problem is to find the minimal instance of the target schema for a given instance of the source schema and a set of mappings between the source and the target schema. Second, we show that our proposed tabular mapping can be used as an operator on an instance of the source schema to produce an instance of the target schema which is `minimal' and `most general' in nature. There exists a tableaux-based mechanism for finding equivalence of two queries. Third, we extend that mechanism for deducing equivalence between two schema mappings using their corresponding tabular representations. Sometimes, there exist redundant conjuncts in a schema mapping which causes data exchange, data integration and data sharing operations more time consuming. Fourth, we present an algorithm that utilizes the tabular representations for reducing number of constraints in the schema mappings. At present, either schema-level mappings or data-level mappings are used for data sharing purposes. Fifth, we introduce and give the semantics of bi-level mapping that combines the schema-level and data-level mappings. We also show that bi-level mappings are more effective for data sharing systems. Finally, we implemented our algorithms and developed a software prototype to evaluate our proposed strategies.

A common task in many database applications is the migration of legacy data from multiple sources into a new one. This requires identifying semantically related elements of the source and target systems and the creation of mapping expressions to transform instances of those elements from the source format to the target format. Currently, data migration is typically done manually, a tedious and timeconsuming process, which is difficult to scale to a high number of data sources. In this paper, we describe QuickMig, a new semi-automatic approach to determining semantic correspondences between schema elements for data migration applications. QuickMig advances the state of the art with a set of new techniques exploiting sample instances, domain ontologies, and reuse of existing mappings to detect not only element correspondences but also their mapping expressions. QuickMig further includes new mechanisms to effectively incorporate domain knowledge of users into the matching process. The results from a comprehensive evaluation using real-world schemas and data indicate the high quality and practicability of the overall approach.