Microsoft Technology Licensing, LLC

Shengquan Yan

Peng Yu

Zijian Zheng

Harvey Rook

Rajasi Saha

John Carlton Platt

Wei Guang Lu

Ashish Consul

Xian Zhang

Miriam Rosenberg

Guixi Zou

Yin He

Michael Kinoti

Xavier Drudis Rius

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.

Stream processing in search data pipelines

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.

Monad based cloud computing

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.

Machine-based learning for automatically categorizing data on per-user basis

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.

Boosting to determine indicative features from a training set

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Shengquan Yan