A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit <a rel="external noopener" href="http://kurtstockinger.ch/publications/SODA_vldb2012.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="VLDB Endowment">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p6rqwwpkkjbcldejepcehaalby" style="color: black;">Proceedings of the VLDB Endowment</a>
The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14778/2336664.2336667">doi:10.14778/2336664.2336667</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4qjqmhctubbmfmg4mkdfxkfi2e">fatcat:4qjqmhctubbmfmg4mkdfxkfi2e</a> </span>
more »... swers to ad-hoc queries that were not anticipated. This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.
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