Abstract: The disclosed embodiments relate to a system that improves operation of a monitored system. During a training mode, the system uses a training data set comprising labeled data points received from the monitored system to train the SVM to detect one or more conditions-of-interest. While training the SVM model, the system makes approximations to reduce computing costs, wherein the approximations involve stochastically discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model. Next, during a surveillance mode, the system uses the trained SVM model to detect the one or more conditions-of-interest based on monitored data points received from the monitored system. When one or more conditions-of-interest are detected, the system performs an action to improve operation of the monitored system.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: The notion of checkpointing model building is introduced to safeguard against the loss of computational results due to abnormal termination of long running computation algorithms. A checkpoint manager initiates a checkpoint, wherein initiation occurs by various scenarios, including dynamically, automatically and manually. A computation module executes the checkpoint on an in progress data computation. The checkpoint manager also monitors the execution of the checkpoint for abnormal termination. Upon a determination of abnormal checkpoint termination, the inability to resume model building from a checkpoint, the checkpoint manger either resumes model build from the checkpoint or aborts the model build.

Abstract: The present invention relates to progress notification systems, computer program products and methods of operation thereof, that reports processing progress of data mining operations at regular periodic intervals. The system comprises: an input/output interface for exchanging information with a network; a memory for storing updated progress objects associated with the data mining operation as a set of data mining algorithms progress in processing; and a processor coupled to the input/output interface and the memory, the processor for performing the data mining operation, the data mining operation implementing the set of data mining algorithms; and generating a notification object for the data mining operation at a pre-determined interval, the notification object based on the progress objects at each of the pre-determined intervals.