Abstract: A method and system are provided. The method includes extracting shapelets from each of a plurality of time series dimensions of multi-dimensional time series data. The method further includes building a plurality of decision-tree classifiers, one for each time series dimension, responsive to the shapelets extracted therefrom. The method also includes generating a pairwise similarity matrix between respective different ones of the plurality of time series dimensions using the shapelets as intermediaries for determining similarity. The method additionally includes applying a feature selection technique to the matrix to determine respective feature weights for each of shapelet features of the shapelets and respective classifier weights for each of the decision-tree classifiers that uses the shapelet features.

Abstract: A method for predicting a failure of oilfield equipment based on univariate time series includes providing training data by a sensor. A training data stream comprising the training data is received by a preprocessor. The method further includes extracting training data segments and identifying each training data segment of the training data segments as corresponding to a normal operational state of the first oilfield equipment or a failed state of the first oilfield equipment. The method also includes generating a shapelet-based decision tree and receiving a test data stream from a sensor of second oilfield equipment. The method further includes determining, based on the shapelet-based decision tree, whether one or more test data segments extracted from the test data stream predict a failure of the second oilfield equipment.

Abstract: A method and system are provided. The method includes extracting shapelets from each of a plurality of time series dimensions of multi-dimensional time series data. The method further includes building a plurality of decision-tree classifiers, one for each time series dimension, responsive to the shapelets extracted therefrom. The method also includes generating a pairwise similarity matrix between respective different ones of the plurality of time series dimensions using the shapelets as intermediaries for determining similarity. The method additionally includes applying a feature selection technique to the matrix to determine respective feature weights for each of shapelet features of the shapelets and respective classifier weights for each of the decision-tree classifiers that uses the shapelet features.

Abstract: A complex event processing system and method of operation are disclosed. The system includes, in one example, a monitor configured to detect, from measurements received from one or more data streams, observations relating to the measurements, and interpretations of the observations, a plurality, an occurrence of an event, and a state transition engine configured to receive the event and, based at least in part on the event, determine a current state of a particular entity of a dynamic system based on a state model as well as a next state of the particular entity to which the state model should transition, wherein the particular entity of the dynamic system is associated with the event.

Abstract: A complex event processing system includes a complex event processing engine configured to detect one or more events across a plurality of data sources and provide a corrective action. The complex event processing system further includes an ontology repository in communication with the complex event processing engine, the ontology repository being configured to receive a first semantic query from the complex event processing engine. The complex event processing system also includes an enterprise integration pattern library in communication with the complex event processing engine, the enterprise integration pattern library being configured to receive a second semantic query from the complex event processing engine.