2011 AcceleratingLargeScaleDataMinin

(Inchiosa, 2011) ⇒ Mario E. Inchiosa. (2011). “Accelerating Large-scale Data Mining Using in-database Analytics.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011). doi:10.1145/2020408.2020536

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Abstract

In more and more industries, competitive advantage hinges on exploiting the largest quantity of data in the shortest possible time - and doing so cost-effectively. Data volumes are growing exponentially, while businesses are striving to deploy sophisticated and computationally intensive predictive analytics. Often, massive data is stored in a data warehouse running on dedicated parallel hardware, but advanced analytics is performed on a separate compute platform. Moving data from the data warehouse to the compute environment can constitute a significant bottleneck. Organizations resort to considering only a fraction of their data or refreshing their analyses infrequently. To address the data movement bottleneck and take full advantage of parallel data warehouse platforms, vendors are offering new in-database analytics capabilities. They are opening up their platforms, allowing users to run their own user-defined functions and statistical models as well as vendor- and partner-supplied advanced analytics on the database platform, close to the data, in parallel, without transporting the data through a host node or corporate network. In this talk, we will present the need for in-database analytics and discuss a number of the new solutions available, highlighting case studies where solution times have been reduced from hours to minutes or seconds.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 AcceleratingLargeScaleDataMinin	Mario E. Inchiosa			Accelerating Large-scale Data Mining Using in-database Analytics				10.1145/2020408.2020536