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Database Support for Enabling Data-Discovery Queries over Semantically-Annotated Observational Data

  • Huiping Cao
  • Shawn Bowers
  • Mark P. Schildhauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7600)

Abstract

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.

Keywords

Complex Query Conjunctive Normal Form Query Evaluation Semantic Annotation Disjunctive Normal Form 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huiping Cao
    • 1
  • Shawn Bowers
    • 2
  • Mark P. Schildhauer
    • 3
  1. 1.Dept. of Computer ScienceNew Mexico State UniversityUSA
  2. 2.Dept. of Computer ScienceGonzaga UniversityUSA
  3. 3.NCEASUniversity of California Santa BarbaraUSA

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