Quest Software Inc.

Dell Software Inc.

Jason Aylesworth

Michel Albert Brisebois

Curtis T Johnstone

Elena V Vinogradov

Stephen Pope

Andrew John Leach

Joel Stacy Blaiberg

Shawn Donald Holmesdale

GuangNing Hu

Guangning Hu

Marina Goldchtein

Christopher Macdonald

Benjamin Gribbin

Trapti Purohit

In one embodiment, a method includes extracting a cross-platform data-collection schema based, at least in part, on information available via heterogeneous interfaces of a plurality of source platforms. The cross-platform data-collection schema identifies a plurality of collectable data points in relation to particular source platforms. The method further includes configuring a cross-platform data-collection schedule for the plurality of source platforms. The cross-platform data-collection schedule indicates a collected subset of the plurality of collectable data points in relation to collection frequencies. The method also includes causing data values for the collected subset to be collected on an ongoing basis as dictated by the cross-platform data-collection schedule. Furthermore, the method includes processing a proposed change to at least one of the collected subset and the collection frequencies. In addition, the method includes modifying the cross-platform data-collection schedule to reflect the proposed change.

Systems and methods for tuning cross-platform data collection

In some implementations a starter task to gather data associated with at least a portion of a communications system may be received. A plurality of targets associated with the starter task may be determined. A portion of the plurality of targets may be grouped, based on grouping criteria, to create a collection task. A plurality of bids may be received from a plurality of collection agents, where each bid of the plurality of bids includes a cost estimate to perform the collection task. A winning bid of the plurality of bids may be selected. A particular collection agent associated with the winning bid may perform the collection task.

Distributed data collection in an enterprise network

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.

System and method for data aggregation and analysis

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.

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Nepean, Canada

Jason Aylesworth