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Iyer R., Lind J.'s SQL Server 2005 Data Mining PDF

By Iyer R., Lind J.

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Figure 5b illustrates the PREDICTION operator’s scalability. The scalability results were generated using a linear SVM model built on 500,000 connection records. The same hardware was used as in the build timing tests. 0. In our application prototype periodic model updates are scheduled as new data is accumulated. A model rebuild is also triggered when the performance accuracy falls below a predefined level. Also, as part of the intrusion detection prototype, an OBIEE-based dashboard was created.

Indianapolis, IN: Wiley Publishing Inc. , et al. (2007). Continuous queries in Oracle. In Proceedings of the 33rd International Conference on Very Large Data Bases (pp. 1173-1184). 17 Chapter 2 Improving OLAP Analysis of Multidimensional Data Streams via Efficient Compression Techniques Alfredo Cuzzocrea ICAR-CNR, Italy and University of Calabria, Italy Filippo Furfaro University of Calabria, Italy Elio Masciari ICAR-CNR, Italy Domenico Saccà University of Calabria, Italy AbstrAct Sensor networks represent a leading case of data stream sources coming from real-life application scenarios.

The output of the analysis can be captured in reports that are displayed in the reporting module. The data mining component combines automated model generation and distribution, as well as real-time and offline monitoring. Data mining techniques that have been used in the context of sensor data include maximum likelihood classifiers, neural networks, decision trees, and support vector machines (SVM). Modern RDBMSs offer, to different degrees, robust and effective implementations of data mining algorithms that are fully integrated with core database functionality.

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SQL Server 2005 Data Mining by Iyer R., Lind J.


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