Abstract: Systems and approaches for interacting with industrial equipment are provided that include an interface having an input and an output, and a processor coupled to the interface. The processor is configured to link a uniform identification construct to a device and receive a request to access the uniform identification construct. The processor is further configured to translate the uniform identification construct into an internet protocol (IP) address and route the request to the device having the uniform identification construct according to the IP address via the output.

Abstract: Systems and approaches for interacting with industrial equipment are provided that include an interface having an input and an output, and a processor coupled to the interface. The processor is configured to link a uniform identification construct to a device and receive a request to access the uniform identification construct. The processor is further configured to translate the uniform identification construct into an internet protocol (IP) address and route the request to the device having the uniform identification construct according to the IP address via the output.

Abstract: Systems and approaches for interacting with industrial equipment are provided that include an interface having an input and an output, and a processor coupled to the interface. The processor is configured to link a uniform identification construct to a device and receive a request to access the uniform identification construct. The processor is further configured to translate the uniform identification construct into an internet protocol (IP) address and route the request to the device having the uniform identification construct according to the IP address via the output.

Abstract: A system for predicting events by associating time series data with other types of non-time series data can include a processor configured to receive a data stream including time series data transmitted from a sensor configured to measure an operating parameter of a component being monitored. The processor identifies sequences of interest in the time series data having predictive value. The processor compares the real-time data stream to a set of pre-existing event data that act as effective leading indicators of different alarms and events. The processor extracts any identified sequences of interest from the time series data as an extracted event. The processor quantifies the relationship between the data of the extracted event and the known historical pattern by calculating a confidence level to denote a probability of occurrence of the event by comparing how closely the new time series data matches the data patterns associated with known events.

Abstract: A system for predicting events by associating time series data with other types of non-time series data can include a processor configured to receive a data stream including time series data transmitted from a sensor configured to measure an operating parameter of a component being monitored. The processor identifies sequences of interest in the time series data having predictive value. The processor compares the real-time data stream to a set of known historical patterns that act as effective leading indicators of different alarms and events. The processor extracts any identified sequences of interest from the time series data as an extracted event. The processor quantifies the relationship between the data of the extracted event and the known historical pattern by calculating a confidence level to denote a probability of occurrence of the event by comparing how closely the new time series data matches the data patterns associated with known events.

Abstract: Time series data is received from a time series data repository and the time series data includes a plurality of sub-portions. The sub-portions of data are first sorted in chronological order to appear as if the data is being generated in real time and are then sent for analysis. The received sorted time series data is then analyzed to detect one or more predefined events or patterns in the data. When the predefined events or patterns are detected in the time series data by the analysis, a user or downstream analysis component is informed that the one or more predefined events or patterns have been found.

Abstract: Approaches are provided where, at a local computing device, time series data is received from an industrial component in a first format. This time series data additionally has a tag or identifier appended thereto and a quality setting. The tag is automatically converted to a descriptor used by an analytic system, and the time series data is converted to a format readable by the analytic system. This format is specified by at least a portion of an analytic system blueprint. The time series data is then transmitted to the analytic system such that the time series data and the descriptor are in a format that do not require additional conversions in order to be processed by the analytic system.

Abstract: Provided is a method and system for detecting changes in models applied to analyze time series data. A method includes receiving at a processor a data stream transmitted from a sensor configured to measure an operating parameter of a component being monitored, wherein the data stream comprises at least time series data. The method also includes analyzing the data stream to identify a sequence of interest in the time series data, searching metadata stored separately for an appropriate model to apply to the time series data, and selecting the appropriate model. Information about the selected appropriate model is carried forward with the time series data.

Abstract: Time series data is identified that is related to a predetermined characteristic and the predetermined characteristic being at least one of an identity of a sensor or a time range. Based upon the identified time series data, the time series data is moved to selected ones of the plurality of separate data storage devices, and the movement is temporary for processing purposes. In parallel, queries are performed on the time series data on each of the selected ones of the plurality of separate data storage devices to obtain a plurality of results. The plurality of results are aggregated.

Abstract: A user query or analytic user is received. The user query or analytic requires one or more portions of time series data from a plurality of transient memories. The time series data is linked together across the transient memories devices. A location of one or more portions of the time series data is identified at the plurality of transient memories. One or more portions of the time series data is automatically retrieved from the transient memories.