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How Machine Learning Attacks the Problems of Database Performance - The New Stack

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Two years ago, market analysts thought it seemed a little weird that a communications service provider such as Alcatel-Lucent would buyout, rather than just purchase the license to, a customer experience management tool called Motive. It was one of those science fiction ideas that so many folks treated as science fiction that they didn't realize it had already become science fact: Machine learning could be put to use in diagnosing the causes of data center failures and performance degradation, and furthermore, to become so familiar with the patterns of traffic and their underlying sources that it could predict when failures may occur in the future. On Tuesday, SIOS announced an expansion of iQ designed to detect issues with SQL Server, including the newly released SQL Server 2016, when running in VMware environments. "By using a machine learning technology, our projections are based on actually learned patterns of behavior, of your environment, over time, across all the tiers of computing, and is projecting how they're changing over time, and how on a particular day that environment will be impacted."


Using Artificial Intelligence to Improve Call Center Performance RankMiner

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Also, there was a reliance on agents, reps and clerical staff typing information into systems. Also, there was a reliance on agents, reps and clerical staff typing information into systems. Predictive modeling utilizes past historical data and automatically builds strategies to predict future customer tendencies and expectations. The key advantage of predictive analytics artificial intelligence software is that it can identify patterns in hours rather than the weeks or months that more traditional methods can take to come up with a conclusion (not necessarily the correct one).