Abstract: Architecture that decomposes of one or more monolithic data concepts into atomic concepts and related atomic concept dependencies, and provides streaming data processing that processes individual or separate (atomic) data concepts and defined atomic dependencies. The architecture can comprise data-driven data processing that enables the plug-in of new data concepts with minimal effort. Efficient processing of the data concepts is enabled by streaming only required data concepts and corresponding dependencies and enablement of the seamless configuration of data processing between stream processing systems and batch processing systems as a result of data concept decomposition. Incremental and non-incremental metric processing enables realtime access and monitoring of operational parameters and queries.

Abstract: Architecture that decomposes of one or more monolithic data concepts into atomic concepts and related atomic concept dependencies, and provides streaming data processing that processes individual or separate (atomic) data concepts and defined atomic dependencies. The architecture can comprise data-driven data processing that enables the plug-in of new data concepts with minimal effort. Efficient processing of the data concepts is enabled by streaming only required data concepts and corresponding dependencies and enablement of the seamless configuration of data processing between stream processing systems and batch processing systems as a result of data concept decomposition. Incremental and non-incremental metric processing enables realtime access and monitoring of operational parameters and queries.

Abstract: Architecture that decomposes of one or more monolithic data concepts into atomic concepts and related atomic concept dependencies, and provides streaming data processing that processes individual or separate (atomic) data concepts and defined atomic dependencies. The architecture can comprise data-driven data processing that enables the plug-in of new data concepts with minimal effort. Efficient processing of the data concepts is enabled by streaming only required data concepts and corresponding dependencies and enablement of the seamless configuration of data processing between stream processing systems and batch processing systems as a result of data concept decomposition. Incremental and non-incremental metric processing enables realtime access and monitoring of operational parameters and queries.

Abstract: Systems and methods are provided for using monads to facilitate complex computation tasks in a cloud computing environment. In particular, monads can be employed to facilitate creation and execution of data mining jobs for large data sets. Monads can allow for improved error handling for complex computation tasks. Monads can also facilitate identification of opportunities for improving the efficiency of complex computations.

Abstract: Systems, methods, and computer media for efficiently processing user log data are provided. The log data is progressively processed in variable sized windows based on a specified time period. The log data may be anonymized to protect user privacy. A log server processes the windowed log data in phases. The first phase includes fast data like page view log data. Subsequent phases include slow data like session data which may build on the page view data processed in the first phase. The log server identifies metrics based on the log data processed at each phase. Based on the identified metrics, the log server may identify interests across a community of users or for specific users.

Abstract: Architecture that employs machine-based learning to automatically categorize data on a per-user basis. Auto-tagging reduces the burden on infoworkers by creating a machine learning model to learn from user tagging behavior or preferences. Once this information is obtained, a trained model for this specific user is used to assign tags to incoming data, such as emails. The architecture finds particular applicability to compliance and message retention policies that otherwise would mandate extra work for the infoworker. The architecture learns the tagging behavior of a user and uses this learned behavior to automatically tag data based on the user's prior tagging habits. A regression algorithm is employed to process the training data according to an n-dimensional framework for prediction and application of the tag(s) to the incoming messages.

Abstract: Systems and methods are provided for using monads to facilitate complex computation tasks in a cloud computing environment. In particular, monads can be employed to facilitate creation and execution of data mining jobs for large data sets. Monads can allow for improved error handling for complex computation tasks. Monads can also facilitate identification of opportunities for improving the efficiency of complex computations.

Abstract: Methods, computer systems and computer readable media for optimizing non-deterministic computational paths are provided. In embodiments, requests are received to generate reports derived from a plurality of series of data files whose metadata attributes form certain mathematical structures that can be used to choose the optimal path in the non-deterministic dependency model. Storage for each of the series of data files is optimized. Available data files needed for the report are processed and missing data files are identified. Based on the mathematical structure of the plurality of series of data files, an optimal transition with the missing data files available is determined. An entry into the transition is triggered and the missing data files are processed. The report is generated and the optimized storage is retained for future requests.

Abstract: Systems, methods, and computer media for efficiently processing user log data are provided. A received user log data analysis request specifies: target user log features that identify users in a target user group, analysis user log features that identify data associated with the users in the target user group, and an analysis to perform on the identified data associated with the users in the target user group. Occurrences of specified features are extracted from user logs and stored. Users associated with an occurrence of each of the extracted and stored target user log features are identified as users in the target user group. Occurrences of the analysis user log features that are associated with a user in the target user group are extracted and reformatted for the analysis specified in the analysis request.

Abstract: Determining indicative features may be provided. First, a first set of features may be determined using a document frequency process. Then a second set of features may be determined using a boosting process. Using the boosting process may comprise using an approximation for a one-dimensional optimization. The approximation may include an upper bound. Next, the first set of features and the second set of features may be combined into a combined set of features. The combined set of features may comprise a union of the first set of features and the second set of features. At least one document may then be classified based on the combined set of features.

Abstract: Determining indicative features may be provided. First, a first set of features may be determined using a document frequency process. Then a second set of features may be determined using a boosting process. Using the boosting process may comprise using an approximation for a one-dimensional optimization. The approximation may include an upper bound. Next, the first set of features and the second set of features may be combined into a combined set of features. The combined set of features may comprise a union of the first set of features and the second set of features. At least one document may then be classified based on the combined set of features.

Abstract: A method is presented for searching for email messages that on a server computer. A request is received on the server computer to search for one or more email messages in one or more mailboxes on the server computer. Each of the one or more mailboxes includes a dumpster folder. The request includes search criteria including a parameter indicating whether the dumpster folder associated with a mailbox should be searched. The dumpster folder stores one or more email messages that have been deleted from a deleted items folder in the mailbox. One or more mailboxes that satisfy the search criteria in the request are identified. If the parameter indicates that the dumpster folder should be searched, the dumpster folder of each of the identified mailboxes that satisfy the search criteria is queried and any email messages in each dumpster folder that satisfy the search criteria are identified.

Abstract: Architecture that employs machine-based learning to automatically categorize data on a per-user basis. Auto-tagging reduces the burden on infoworkers by creating a machine learning model to learn from user tagging behavior or preferences. Once this information is obtained, a trained model for this specific user is used to assign tags to incoming data, such as emails. The architecture finds particular applicability to compliance and message retention policies that otherwise would mandate extra work for the infoworker. The architecture learns the tagging behavior of a user and uses this learned behavior to automatically tag data based on the user's prior tagging habits. A regression algorithm is employed to process the training data according to an n-dimensional framework for prediction and application of the tag(s) to the incoming messages.