Quarrying dataspaces: Schemaless profiling of unfamiliar information sources Export

@article{citeulike:3329329, abstract = {Traditional data integration and analysis approaches tend to assume intimate familiarity with the structure, semantics, and capabilities of the available information sources before applicable tools can be used effectively. This assumption often does not hold in practice. We introduce dataspace profiling as the cardinal activity when beginning a project in an unfamiliar dataspace. Dataspace profiling is an analysis of the structures and properties exposed by an information source, allowing 1) assessment of the utility and importance of the information source as a whole, 2) assessment of compatibility with the services of a dataspace support platform, and 3) determination and externalization of structure in preparation for specific data applications. In this paper, we define dataspace profiling and articulate requirements for dataspace profilers. We then describe the Quarry system, which offers a generic browse-and-query interface to support dataspace profiling activities, including path profiling, over a variety of data sources with minimal setup costs and minimal a priori assumptions.We show that the mechanisms used in Quarry deliver strong performance in large-scale applications. Specifically, we use Quarry to efficiently profile 1) a detailed standard for medication nomenclature supplied under a generic schema and 2) the metadata for an environmental observation and forecasting system, and conclude that in these contexts Quarry offers advantages over existing tools.}, address = {Los Alamitos, CA, USA}, author = {Howe, Bill and Maier, David and Rayner, Nicolas and Rucker, James}, booktitle = {Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on}, citeulike-article-id = {3329329}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/ICDEW.2008.4498331}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/ICDEW.2008.4498331}, citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4498331}, doi = {10.1109/ICDEW.2008.4498331}, isbn = {978-1-4244-2161-9}, journal = {Data Engineering Workshops, 22nd International Conference on}, keywords = {data\_integration, dataspaces}, pages = {270--277}, posted-at = {2009-10-23 15:19:18}, priority = {2}, publisher = {IEEE Computer Society}, title = {Quarrying dataspaces: Schemaless profiling of unfamiliar information sources}, url = {http://dx.doi.org/10.1109/ICDEW.2008.4498331}, volume = {0}, year = {2008} }

TY - JOUR ID - citeulike:3329329 L3 - citeulike-article-id:3329329 N2 - Traditional data integration and analysis approaches tend to assume intimate familiarity with the structure, semantics, and capabilities of the available information sources before applicable tools can be used effectively. This assumption often does not hold in practice. We introduce dataspace profiling as the cardinal activity when beginning a project in an unfamiliar dataspace. Dataspace profiling is an analysis of the structures and properties exposed by an information source, allowing 1) assessment of the utility and importance of the information source as a whole, 2) assessment of compatibility with the services of a dataspace support platform, and 3) determination and externalization of structure in preparation for specific data applications. In this paper, we define dataspace profiling and articulate requirements for dataspace profilers. We then describe the Quarry system, which offers a generic browse-and-query interface to support dataspace profiling activities, including path profiling, over a variety of data sources with minimal setup costs and minimal a priori assumptions.We show that the mechanisms used in Quarry deliver strong performance in large-scale applications. Specifically, we use Quarry to efficiently profile 1) a detailed standard for medication nomenclature supplied under a generic schema and 2) the metadata for an environmental observation and forecasting system, and conclude that in these contexts Quarry offers advantages over existing tools. JF - Data Engineering Workshops, 22nd International Conference on SN - 978-1-4244-2161-9 AD - Los Alamitos, CA, USA EP - 277 TI - Quarrying dataspaces: Schemaless profiling of unfamiliar information sources PB - IEEE Computer Society VL - 0 T2 - Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on SP - 270 KW - data_integration KW - dataspaces AU - Howe, Bill AU - Maier, David AU - Rayner, Nicolas AU - Rucker, James PY - 2008/// UR - http://dx.doi.org/10.1109/ICDEW.2008.4498331 ER -