Abstract: A method for monitoring at least one simulation program includes capturing, by a computer, a plurality of simulation data from the at least one simulation program, the capturing is performed in real time while the at least one simulation program is continuously streaming the plurality of simulation data, analyzing, by the computer, the captured plurality of simulation data using a streaming data software, identifying a plurality of predefined criteria within the analyzed plurality of simulation data, the plurality of predefined criteria includes at least one of an event, a result and a variable, and providing feedback to the at least one simulation program to modify a plurality of simulation parameters according to the at least one identified event, result and variable.

Abstract: A prediction technique to predict an anomalous state of a processing environment at a future point in time. One or more values of one or more metrics of the processing system are obtained. For the one or more metrics, one or more predicted values are determined for one or more points in time in the future. Based the predicted values, one or more change values for one or more points in time are determined, and based on the one or more change values, a determination is made as to whether an anomalous state exists within the processing system.

Abstract: A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

Abstract: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream.

Abstract: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream.

Abstract: In one embodiment, a computer-implemented method includes observing one or more entities by way of two or more data sources. A plurality of detection scores are computed by one or more detectors. Each detection score corresponds to an entity of the one or more entities, a detector of the one or more detectors, and a time. The plurality of detection scores are compiled into two or more tensors, where each tensor corresponds to a data source of the two or more data sources. The two or more tensors are compared to one another, by a computer processor. An inconsistency score is calculated for each of the one or more entities, based on comparing the two or more tensors to one another.

Abstract: In one embodiment, a computer-implemented method includes observing one or more entities by way of two or more data sources. A plurality of detection scores are computed by one or more detectors. Each detection score corresponds to an entity of the one or more entities, a detector of the one or more detectors, and a time. The plurality of detection scores are compiled into two or more tensors, where each tensor corresponds to a data source of the two or more data sources. The two or more tensors are compared to one another, by a computer processor. An inconsistency score is calculated for each of the one or more entities, based on comparing the two or more tensors to one another.

Abstract: A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

Abstract: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream.

Abstract: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream.

Abstract: A method for monitoring at least one simulation program includes capturing, by a computer, a plurality of simulation data from the at least one simulation program, the capturing is performed in real time while the at least one simulation program is continuously streaming the plurality of simulation data, analyzing, by the computer, the captured plurality of simulation data using a streaming data software, identifying a plurality of predefined criteria within the analyzed plurality of simulation data, the plurality of predefined criteria includes at least one of an event, a result and a variable, and providing feedback to the at least one simulation program to modify a plurality of simulation parameters according to the at least one identified event, result and variable.

Abstract: A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

Abstract: A prediction technique to predict an anomalous state of a processing environment at a future point in time. One or more values of one or more metrics of the processing system are obtained. For the one or more metrics, one or more predicted values are determined for one or more points in time in the future. Based the predicted values, one or more change values for one or more points in time are determined, and based on the one or more change values, a determination is made as to whether an anomalous state exists within the processing system.

Abstract: A prediction technique to predict an anomalous state of a processing environment at a future point in time. One or more values of one or more metrics of the processing system are obtained. For the one or more metrics, one or more predicted values are determined for one or more points in time in the future. Based the predicted values, one or more change values for one or more points in time are determined, and based on the one or more change values, a determination is made as to whether an anomalous state exists within the processing system.

Abstract: An anomalous component within a processing system is identified. One or more transformed metrics of a processing system being monitored are analyzed, and an anomaly in the processing system is detected. Based on detecting the anomaly, at least one transformed metric is inversely transformed to obtain at least one suspect original metric. Using the at least one suspect original metric, the anomalous component is identified.

Abstract: An approach is provided for detecting an anomaly in a processing environment. The approach includes using a processor to obtain a series of values collected within a processing interval of the processor in the processing environment. The processor normalizes this first series of values to obtain a first series of normalized values. A second series of normalized values is generated by applying a predictive filter to the first series of normalized values. A comparison score is generated from the normalized values by comparing the first series of normalized values and the second series of normalized values. The approach then determines whether the comparison score represents an anomaly relative to at least one other comparison score derived from values collected within the processing interval.

Abstract: An approach is provided for detecting an anomaly in a processing environment. The approach includes using a processor to obtain a series of values collected within a processing interval of the processor in the processing environment. The processor normalizes this first series of values to obtain a first series of normalized values. A second series of normalized values is generated by applying a predictive filter to the first series of normalized values. A comparison score is generated from the normalized values by comparing the first series of normalized values and the second series of normalized values. The approach then determines whether the comparison score represents an anomaly relative to at least one other comparison score derived from values collected within the processing interval.

Abstract: A prediction technique to predict an anomalous state of a processing environment at a future point in time. One or more values of one or more metrics of the processing system are obtained. For the one or more metrics, one or more predicted values are determined for one or more points in time in the future. Based the predicted values, one or more change values for one or more points in time are determined, and based on the one or more change values, a determination is made as to whether an anomalous state exists within the processing system.

Abstract: An anomalous component within a processing system is identified. One or more transformed metrics of a processing system being monitored are analyzed, and an anomaly in the processing system is detected. Based on detecting the anomaly, at least one transformed metric is inversely transformed to obtain at least one suspect original metric. Using the at least one suspect original metric, the anomalous component is identified.

Abstract: A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.