TY - GEN
T1 - On creating efficient object-relational views of scientific datasets
AU - Narayanan, Sivaramakrishnan
AU - Kurc, Tahsin
AU - Catalyurek, Umit V.
AU - Saltz, Joel
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Scientific datasets are often large and distributed in flat files across several storage nodes. Scientists frequently want to analyze subsets of these datasets. A data source abstraction that provides an object-relational view of data while hiding the details of storage and transport mechanisms and dataset layouts is useful in this regard. In this abstraction, Basic Data Sources (BDS) interpret flat files as a set of records and are the building blocks of the view mechanism. Derived Data Sources (DDS) may be built on top of BDSs and provide more complex objects that serve the scientists' needs. The simplest DDS is one that supports a join based view over BDSs. We investigate issues involving building such DDSs for scientific applications and consider distributed versions of the indexed join and the Grace Hash join algorithms. We construct cost models that capture their performance in a restricted space of dataset and system parameters and compare them analytically and experimentally.
AB - Scientific datasets are often large and distributed in flat files across several storage nodes. Scientists frequently want to analyze subsets of these datasets. A data source abstraction that provides an object-relational view of data while hiding the details of storage and transport mechanisms and dataset layouts is useful in this regard. In this abstraction, Basic Data Sources (BDS) interpret flat files as a set of records and are the building blocks of the view mechanism. Derived Data Sources (DDS) may be built on top of BDSs and provide more complex objects that serve the scientists' needs. The simplest DDS is one that supports a join based view over BDSs. We investigate issues involving building such DDSs for scientific applications and consider distributed versions of the indexed join and the Grace Hash join algorithms. We construct cost models that capture their performance in a restricted space of dataset and system parameters and compare them analytically and experimentally.
UR - https://www.scopus.com/pages/publications/34547409812
U2 - 10.1109/ICPP.2006.56
DO - 10.1109/ICPP.2006.56
M3 - Conference contribution
AN - SCOPUS:34547409812
SN - 0769526365
SN - 9780769526362
T3 - Proceedings of the International Conference on Parallel Processing
SP - 551
EP - 558
BT - ICPP 2006
T2 - ICPP 2006: 2006 International Conference on Parallel Processing
Y2 - 14 August 2006 through 18 August 2006
ER -