Abstract: Systems and methods for querying a semantic data set are described. The semantic data set is defined by an ontology that represents a graphical relationship among data included in the semantic data set. One method includes receiving one or more keywords associated with a search operation from a user, and identifying a node associated with each of the one or more keywords. The method includes, for each identified node, tracing a path from the identified node to a root represented in the graphical relationship of the ontology, the path including one or more vertices, and identifying a lowest common ancestor of each of the vertices included in the paths for each identified node. The method includes constructing a subgraph connecting each identified node to the lowest common ancestor, and traversing the subgraph to generate a query in the query language executable against the semantic data set.

Abstract: Systems and methods for querying a semantic data set are described. The semantic data set is defined by an ontology that represents a graphical relationship among data included in the semantic data set. One method includes receiving one or more keywords associated with a search operation from a user, and identifying a node associated with each of the one or more keywords. The method includes, for each identified node, tracing a path from the identified node to a root represented in the graphical relationship of the ontology, the path including one or more vertices, and identifying a lowest common ancestor of each of the vertices included in the paths for each identified node. The method includes constructing a subgraph connecting each identified node to the lowest common ancestor, and traversing the subgraph to generate a query in the query language executable against the semantic data set.

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.