Schema mappings, data exchange, and metadata management
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
DBNotes: a post-it system for relational databases based on provenance
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Survey of semantic annotation platforms
Proceedings of the 2005 ACM symposium on Applied computing
MONDRIAN: Annotating and Querying Databases through Colors and Blocks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
An annotation management system for relational databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A Conceptual Modeling Framework for Expressing Observational Data Semantics
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Clio: Schema Mapping Creation and Data Exchange
Conceptual Modeling: Foundations and Applications
Building semantic mappings from databases to ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Detecting and Interpreting Variable Interactions in Observational Ornithology Data
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Composition with target constraints
Proceedings of the 13th International Conference on Database Theory
Approaches for semantically annotating and discovering scientific observational data
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
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Observational data plays a critical role in many scientific disciplines, and scientists are increasingly interested in performing broad-scale analyses by using observational data collected as part of many smaller scientific studies. However, while these data sets often contain similar types of information, they are typically represented using very different structures and with little semantic information about the data itself, which creates significant challenges for researchers who wish to discover existing data sets based on data semantics (observation and measurement types) and data content (the values of measurements within a data set). We present a formal framework to address these challenges that consists of a semantic observational model (to uniformly represent observation and measurement types), a high-level semantic annotation language (to map tabular resources into the model), and a declarative query language that allows researchers to express data-discovery queries over heterogeneous (annotated) data sets. To demonstrate the feasibility of our framework, we also present implementation approaches for efficiently answering discovery queries over semantically annotated data sets. In particular, we propose two storage schemes (in-place databases rdb and materialized databases mdb) to store the source data sets and their annotations. We also present two query schemes (ExeD and ExeH) to evaluate discovery queries and the results of extensive experiments comparing their effectiveness.