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 computer-implemented method for prioritizing candidate objects on which to perform a physical process includes receiving a time series history of measurements from each of a plurality of candidate objects at a data processing framework. The method further includes reducing dimensionality of the time series history of measurements with a convolutional autoencoder to obtain latent features for each of the plurality of candidate objects. The method also includes applying a kernel regression model to the latent features to generate a predicted value of physical output for performing the physical process on each of the plurality of candidate objects. The method additionally includes generating a prioritization of the candidate objects based on the values of physical output. The method involves selecting fewer than all of the plurality of candidate objects on which to perform the physical process. The selected candidate objects are based on the prioritization.

Abstract: A computer-implemented method for prioritizing candidate objects on which to perform a physical process includes receiving a time series history of measurements from each of a plurality of candidate objects at a data processing framework. The method further includes reducing dimensionality of the time series history of measurements with a convolutional autoencoder to obtain latent features for each of the plurality of candidate objects. The method also includes applying a kernel regression model to the latent features to generate a predicted value of physical output for performing the physical process on each of the plurality of candidate objects. The method additionally includes generating a prioritization of the candidate objects based on the values of physical output. The method involves selecting fewer than all of the plurality of candidate objects on which to perform the physical process. The selected candidate objects are based on the prioritization.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.

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.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.

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.

Abstract: A method for deriving data provenance information corresponding to a workflow process having lower-level workflow processes includes deriving internal provenance information for data pertaining to at least one of the lower-level workflow processes, identifying data objects that are shared between at least a pair of lower-level workflow processes to derive external provenance information for the identified data objects, in response to a user-submitted query, using the internal and external provenance information to retrieve the data provenance information for the workflow process, and outputting the derived data provenance to a user.

Abstract: A method for deriving data provenance information corresponding to a workflow process having lower-level workflow processes includes deriving internal provenance information for data pertaining to at least one of the lower-level workflow processes, identifying data objects that are shared between at least a pair of lower-level workflow processes to derive external provenance information for the identified data objects, in response to a user-submitted query, using the internal and external provenance information to retrieve the data provenance information for the workflow process, and outputting the derived data provenance to a user.

Abstract: Systems and methods are directed to forecasting oilfield production in an integrated asset management framework and specifying conditions associated with the plurality of models. A graphical interface for generates a plurality of models that represent asset components in the oilfield, and a database stores the plurality of models. An application toolkit for analyzes at least one of the plurality of stored models based on a scenario to forecast a performance of an asset component associated with the at least one analyzed model.

Abstract: A method for creating an integrated asset management system for an oilfield, the method including: creating a plurality of models representing asset components each model having more than one levels of detail; connecting the more than one models to communicate with one another to create an integrated asset management system; selecting the levels of detail for the more than one models; and performing an analysis on the integrated asset management system utilizing the selected levels of detail to predict a characteristic of the integrated asset management system.

Abstract: Video data compression techniques reduce necessary storage size and communication channel bandwidth while maintaining acceptable fidelity. Vector quantization provides better overall data compression performance by coding vectors instead of scalars. The search algorithm and VLSI architecture for implementing it is herein disclosed, and such a search algorithm is useful for real-time image processing. The architecture employs a single processing element and external memory for storing the N constant value hyperplanes used in the search, where N is the number of codevectors. The design does not perform any multiplication operation using the constant value hyperplane tree search, since the tree search method is independent of any L.sub.q metric for q between one and infinity. Memory used by the design is significantly less than memory employed in existing architecture.