Scientific research requires access, analysis, and sharing of data that is
distributed across various heterogeneous data sources at the scale of the
Internet. An eager ETL process constructs an integrated data repository as its
first step, integrating and loading data in its entirety from the data sources.
The bootstrapping of this process is not efficient for scientific research that
requires access to data from very large and typically numerous distributed data
sources. a lazy ETL process loads only the metadata, but still eagerly. Lazy
ETL is faster in bootstrapping. However, queries on the integrated data
repository of eager ETL perform faster, due to the availability of the entire
data beforehand.
In this paper, we propose a novel ETL approach for scientific data
integration, as a hybrid of eager and lazy ETL approaches, and applied both to
data as well as metadata. This way, Hybrid ETL supports incremental integration
and loading of metadata and data from the data sources. We incorporate a
human-in-the-loop approach, to enhance the hybrid ETL, with selective data
integration driven by the user queries and sharing of integrated data between
users. We implement our hybrid ETL approach in a prototype platform, Obidos,
and evaluate it in the context of data sharing for medical research. Obidos
outperforms both the eager ETL and lazy ETL approaches, for scientific research
data integration and sharing, through its selective loading of data and
metadata, while storing the integrated data in a scalable integrated data
repository.Comment: Pre-print Submitted to the DMAH Special Issue of the Springer DAPD
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