Dell Software Inc.

Quest Software Inc.

Guangning Hu

Michel Albert Brisebois

Curtis T Johnstone

Jason Aylesworth

Elena V Vinogradov

Stephen Pope

Andrew John Leach

Joel Stacy Blaiberg

Shawn Donald Holmesdale

Xuejun Situ

This disclosure describes systems, methods, and computer-readable media for optimizing data collection in a distributed environment by leveraging real-time and historical data collection performance statistics and server performance data. In some configurations, a computing device can be initially configured for data collection. In such configurations, the initial configuration can include preferred target servers for a particular task. The computing device can request batches of data from the preferred target servers, and process the information through a buffer. Techniques and technologies described herein collect the batches of data from servers as well as corresponding data collection statistics (e.g., server performance per task, server historical performance, etc.) and server performance data (e.g. server status).

Dispatcher for adaptive data collection

In one embodiment, a method includes managing and controlling a plurality of data-access credentials. The method further includes accessing data from a plurality of sources in a plurality of data formats. The accessing includes using one or more data-access credentials of the plurality of data-access credentials. The one or more data-access credentials are associated with at least a portion of the plurality of data sources. The method also includes abstracting the data into a standardized format for further analysis. The abstracting includes selecting the standardized format based on a type of the data. In addition, the method includes applying a security policy to the data. The applying includes identifying at least a portion of the data for exclusion from storage based on the security policy. Additionally, the method includes filtering from storage any data identified for exclusion. Further, the method includes storing the data in the standardized format.

Aggregation and classification of secure data

In one embodiment, a method includes providing an a priori classification engine, an a posteriori classification engine, and a heuristics engine. The a priori classification engine is operable to perform an a priori classification. The a posteriori classification engine is operable to perform an a posteriori classification. The heuristics engine is operable to perform a heuristics classification. In addition, the method includes accessing data from at least one source. The method further includes, responsive to an indication that the a priori classification should be performed, performing the a priori classification on the data. The method also includes, responsive to an indication that the a posteriori classification should be performed, performing the a posteriori classification on the data. Further, the method includes, responsive to an indication that the heuristics classification should be performed, performing the heuristics classification on the data.

System and method for classifying data

In one embodiment, a method includes maintaining, by a computer system, a plurality of data and at least one classification for each of the plurality of data. Each data of the plurality of data is aggregated from a plurality data sources and is abstracted into one or more of a plurality of standardized formats based on a type of the data. The computer system has executing thereon a plurality of classification engines. The plurality of classification engines include an a priori classification engine, an a posteriori classification engine, and a heuristics engine. Each at least one classification is generated by one of the plurality of classification engines. In addition, the method includes receiving query input from a user. Further, the method includes querying at least a portion of the plurality of data responsive to the query input.

System and method for querying data

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Ottawa, Canada

Guangning Hu