Abstract: Techniques are disclosed for anomaly detection based on a predicted value. A search query can be executed over a period of time to produce values for a key performance indicator (KPI), the search query defining the KPI and deriving a value indicative of the performance of a service at a point in time or during a period of time, the value derived from machine data pertaining to one or more entities that provide the service. A graphical user interface (GUI) enabling a user to indicate a sensitivity setting can be displayed. A user input indicating the sensitivity setting can be received via the GUI. Zero or more of the values as anomalies can be identified in consideration of the sensitivity setting indicated by the user input.

Abstract: Techniques are disclosed for anomaly detection based on a predicted value. A search query can be executed over a period of time to produce values for a key performance indicator (KPI), the search query defining the KPI and deriving a value indicative of the performance of a service at a point in time or during a period of time, the value derived from machine data pertaining to one or more entities that provide the service. A graphical user interface (GUI) enabling a user to indicate a sensitivity setting can be displayed. A user input indicating the sensitivity setting can be received via the GUI. Zero or more of the values as anomalies can be identified in consideration of the sensitivity setting indicated by the user input.

Abstract: Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs) that are updated using training data. Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention.

Abstract: An automatic service monitor in an information technology environment may be equipped to automatically process machine data originating from a running IT environment to identify the entities that perform services in the environment, and to reflect the discovered entities and service associations in the control and configuration data that directs the monitoring operations performed by the system.

Abstract: Techniques are disclosed for providing a graphical user interface (GUI) for displaying and configuring adaptive or static thresholds for Key Performance Indicators (KPIs). The GUI may include one or more presentation schedules that may display threshold information associated with time policies. Each presentation schedule may include multiple time slots and span a portion of one or more time cycles. Some of the time slots may be associated with a specific time policy and may have a unifying appearance that distinguishes the time slots from timeslots associated with other time policies. The presentation schedules may arrange the time slots in a time grid arrangement (e.g., calendar grid view) or a graph arrangement with depictions (e.g., points, lines) that may illustrate KPI values and threshold markers that may illustrate the threshold values.

Abstract: Provided is a method for predicting kinematic viscosity of a fraction of a crude oil to optimize selection of crude oils. The method includes receiving parameters of the crude oil, such as Vacuum Residue yield and Conradson Carbon Residue (CCR), content as an input. The method also includes determining kinematic viscosity of the fraction of the crude oil at a first predetermined temperature based on a first correlation model between the physical parameters of the crude oil and the kinematic viscosity at the first predetermined temperature. The method further includes generating the kinematic viscosity of the fraction of the crude oil at the predetermined temperature based on the first correlation model corresponding to the input. Also provided is a system for predicting kinematic viscosity at a predetermined temperature to optimize crude oil selection. Further provided is a method for estimating an amount of cutter stock for crude oil processing.

Abstract: Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs) that are updated using training data. Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention.

Abstract: Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs). Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention.

Abstract: An automatic service monitor in an information technology environment may be equipped to automatically process machine data originating from a running IT environment to identify the entities that perform services in the environment, and to reflect the discovered entities and service associations in the control and configuration data that directs the monitoring operations performed by the system. A related user interface is taught.

Abstract: An automatic service monitor in an information technology environment may be equipped to automatically process machine data originating from a running IT environment to identify the entities that perform services in the environment, and to reflect the discovered entities and service associations in the control and configuration data that directs the monitoring operations performed by the system.

Abstract: An automatic service monitor in an information technology environment may be equipped to automatically process machine data originating from a running IT environment to identify the entities that perform services in the environment, and to reflect the discovered entities and service associations in the control and configuration data that directs the monitoring operations performed by the system.

Abstract: Techniques are disclosed for providing a graphical user interface (GUI) for displaying and configuring adaptive or static thresholds for Key Performance Indicators (KPIs). The GUI may include one or more presentation schedules that may display threshold information associated with time policies. Each presentation schedule may include multiple time slots and span a portion of one or more time cycles. Some of the time slots may be associated with a specific time policy and may have a unifying appearance that distinguishes the time slots from timeslots associated with other time policies. The presentation schedules may arrange the time slots in a time grid arrangement (e.g., calendar grid view) or a graph arrangement with depictions (e.g., points, lines) that may illustrate KPI values and threshold markers that may illustrate the threshold values.

Abstract: Provided is a method for predicting kinematic viscosity of a fraction of a crude oil to optimize selection of crude oils. The method includes receiving parameters of the crude oil, such as Vacuum Residue yield and Conradson Carbon Residue (CCR), content as an input. The method also includes determining kinematic viscosity of the fraction of the crude oil at a first predetermined temperature based on a first correlation model between the physical parameters of the crude oil and the kinematic viscosity at the first predetermined temperature. The method further includes generating the kinematic viscosity of the fraction of the crude oil at the predetermined temperature based on the first correlation model corresponding to the input. Also provided is a system for predicting kinematic viscosity at a predetermined temperature to optimize crude oil selection. Further provided is a method for estimating an amount of cutter stock for crude oil processing.

Abstract: Techniques are disclosed for providing a graphical user interface (GUI) for displaying and configuring adaptive or static thresholds for Key Performance Indicators (KPIs). The GUI may include one or more presentation schedules that may display threshold information associated with time policies. Each presentation schedule may include multiple time slots and span a portion of one or more time cycles. Some of the time slots may be associated with a specific time policy and may have a unifying appearance that distinguishes the time slots from timeslots associated with other time policies. The presentation schedules may arrange the time slots in a time grid arrangement (e.g., calendar grid view) or a graph arrangement with depictions (e.g., points, lines) that may illustrate KPI values and threshold markers that may illustrate the threshold values.

Abstract: A computer system determines if events in a machine data store satisfy event selection criteria. The events may pertain to a service entity represented by a stored entity definition. The entity definition may include information to identify the events from the machine data. Other informational fields in the entity definition may be effectively attributed to the identified events and take part in satisfying the event selection criteria.

Abstract: An automatic service monitor in an information technology environment may be equipped to automatically process machine data originating from a running IT environment to identify the entities that perform services in the environment, and to reflect the discovered entities and service associations in the control and configuration data that directs the monitoring operations performed by the system.

Abstract: An automatic service monitor in an information technology environment may be equipped to automatically process machine data originating from a running IT environment to identify the entities that perform services in the environment, and to reflect the discovered entities and service associations in the control and configuration data that directs the monitoring operations performed by the system. A related user interface is taught.

Abstract: A computer system determines if events in a machine data store satisfy event selection criteria. The events may pertain to a service entity represented by a stored entity definition. The entity definition may include information to identify the events from the machine data. Other informational fields in the entity definition may be effectively attributed to the identified events and take part in satisfying the event selection criteria.

Abstract: Techniques are disclosed for providing a graphical user interface (GUI) for displaying and configuring adaptive or static thresholds for Key Performance Indicators (KPIs). The GUI may include one or more presentation schedules that may display threshold information associated with time policies. Each presentation schedule may include multiple time slots and span a portion of one or more time cycles. Some of the time slots may be associated with a specific time policy and may have a unifying appearance that distinguishes the time slots from timeslots associated with other time policies. The presentation schedules may arrange the time slots in a time grid arrangement (e.g., calendar grid view) or a graph arrangement with depictions (e.g., points, lines) that may illustrate KPI values and threshold markers that may illustrate the threshold values.

Abstract: Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs). Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention.