Federated database systems for managing distributed, heterogeneous, and autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
A framework for supporting data integration using the materialized and virtual approaches
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Schema Matching Using Duplicates
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Automatic data fusion with HumMer
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A survey of data provenance in e-science
ACM SIGMOD Record
Taverna: lessons in creating a workflow environment for the life sciences: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Model management 2.0: manipulating richer mappings
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Provenance in collection-oriented scientific workflows
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
BioFlow: A Web-Based Declarative Workflow Language for Life Sciences
SERVICES '08 Proceedings of the 2008 IEEE Congress on Services - Part I
ACM Computing Surveys (CSUR)
On-the-Fly Integration and Ad Hoc Querying of Life Sciences Databases Using LifeDB
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
An Algebraic Language for Semantic Data Integration on the Hidden Web
ICSC '09 Proceedings of the 2009 IEEE International Conference on Semantic Computing
Declarative data fusion – syntax, semantics, and implementation
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
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The integration of data stemming from heterogeneous sources is an issue that has challenged computer science research for years - not to say decades. Therefore, many methods, frameworks and tools were and are still being developed that all promise to solve the integration of data. This work describes those which we think are most promising by relating them to each other. Since our focus is on scientific applications, we consider important properties within this domain such as data provenance. However, aspects like the extensibility of an approach are also considered.