Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the present disclosure include systems and methods for providing query service of secured contents. A data collection service collects data and security context associated with the data from a data source and stores the data with the security attributes in a datastore, where the security attributes are derived from the security context and used to determine access to the data so that access to the data is consistent with the security context. Upon receiving a query and a user context of a requester making the query of the datastore, a set of query results is obtained. Based on the user context and security attributes, it is determined whether the requestor has a proper right to access the query results. If the requestor has a proper right to access the query results, access to the query results is granted.

Abstract: 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.

Abstract: In one embodiment, a method is performed by a computer system. The method includes accessing information related to enterprise usage of a plurality of network-accessible peripheral devices and identifying, from the information, discrete content-imaging events that occurred on the plurality of network-accessible peripheral devices. In addition, the method includes determining particular users associated with the discrete content-imaging events on a per-event basis and determining particular content to which the discrete content-imaging events relate on a per-event basis. Further, the method includes abstracting correlated data related to the discrete content-imaging events into a standardized format, the correlated data comprising data related to the particular users and the particular content, the standardized format enabling expression of the discrete content-imaging events by user and by type of content-imaging activity.

Abstract: Dynamic user training rules can be employed to organize content within a folder. Each folder can be associated with a dynamic rule that defines a number of attribute and weighting factor associations and a folder threshold. Each attribute and weighting factor association can identify an attribute that is common among the contents of the smart folder and associate a weighting factor with the common attribute. Whenever a user manually moves content into or out of the smart folder, the attribute and weighting factor associations and possibly the folder threshold can be updated accordingly, such as by adjusting a value of one or more weighting factors to better reflect how common they are after the content has been manually moved into or out of the folder. In this way, the dynamic rule is automatically updated in response to the user's interaction with the smart folder.

Abstract: 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.

Abstract: 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).

Abstract: Techniques and systems for prioritizing messages are described. The messages may be associated with multiple message accounts of an individual and the message accounts may be administered by different message account providers. The messages may be prioritized according to priority scores determined based on priority rules. The priority rules may include components that correspond to attributes of the messages. The priority rules may also include weights for each of the respective components of the priority rules. The prioritized messages may be viewed in a user interface. In some cases, a user may modify an order of the messages in the user interface and the system may utilize the feedback from the user to change a priority rule.

Abstract: Embodiments of the present disclosure include systems and methods for providing query service of secured contents. A data collection service collects data and security context associated with the data from a data source and stores the data with the security attributes in a datastore, where the security attributes are derived from the security context and used to determine access to the data so that access to the data is consistent with the security context. Upon receiving a query and a user context of a requester making the query of the datastore, a set of query results is obtained. Based on the user context and security attributes, it is determined whether the requestor has a proper right to access the query results. If the requestor has a proper right to access the query results, access to the query results is granted.

Abstract: 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.

Abstract: Dynamic user training rules can be employed to organize content within a folder. Each folder can be associated with a dynamic rule that defines a number of attribute and weighting factor associations and a folder threshold. Each attribute and weighting factor association can identify an attribute that is common among the contents of the smart folder and associate a weighting factor with the common attribute. Whenever a user manually moves content into or out of the smart folder, the attribute and weighting factor associations and possibly the folder threshold can be updated accordingly, such as by adjusting a value of one or more weighting factors to better reflect how common they are after the content has been manually moved into or out of the folder. In this way, the dynamic rule is automatically updated in response to the user's interaction with the smart folder.

Abstract: 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.

Abstract: 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.

Abstract: 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.