Abstract: A method comprises acquiring anomaly data including a plurality of anomalies detected from streaming data, wherein each of the anomalies relates to an entity on or associated with a computer network. The method determines a risk score of each of the anomalies, and adjusts the risk score of an anomaly according to a set of factors. The method further determines, for each of a plurality of sliding time windows of different lengths, an entity score of the entity in relation to the sliding time window, based on an aggregation of risk scores of all anomalies related to the entity that were detected within the sliding time window, where the entity score corresponds to a risk level associated with the entity. An action to prevent the entity from performing an operation can be determined and caused to occur based on the entity score.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: An identify resolution system performs actions comprises a set-up process and an identity resolution process that executes asynchronously with respect to the set-up process. the set-up process includes accessing machine data including a plurality of event data objects, each event data object of the plurality of event data objects including timestamped raw machine-generated data indicative of performance or operation of one or more entities in a computer network environment. The identity resolution process ascertains the identity of an entity associated with the computer network environment, based on the association data in the data store, wherein the identity of the entity is not expressed directly in the association data in the data store.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: A method comprises acquiring anomaly data including a plurality of anomalies detected from streaming data, wherein each of the anomalies relates to an entity on or associated with a computer network. The method determines a risk score of each of the anomalies, and adjusts the risk score of an anomaly according to a set of factors. The method further determines, for each of a plurality of sliding time windows of different lengths, an entity score of the entity in relation to the sliding time window, based on an aggregation of risk scores of all anomalies related to the entity that were detected within the sliding time window, where the entity score corresponds to a risk level associated with the entity. An action to prevent the entity from performing an operation can be determined and caused to occur based on the entity score.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: An approach is provided for utilizing role-based presentation interfaces. A role of a user among a plurality of roles is determined. The user is among a plurality of users that are grouped based on the roles. A presentation interface is populated with sales related information based on the determined role.

Abstract: An approach for providing data access via a common access manager configured to support security for multiple database management system types is described. A request specifying access for a user to a feature associated with one of a plurality of database management system types is determined by a common access manager configured to support the database management system types. A first-level approval of the access request by a first-level approver is determined. The access request is forwarded to a second-level approver based on the first-level approval. A provisioning of the access to the feature for the user is initiated based on a second-level approval by the second-level approver.

Abstract: An approach is provided for data migration. Table structure data associated with a logical table is extracted from a source database. Raw data associated with the logical table is extracted from the source database. A table structure in a target database is generated based on the table structure data. The raw data is loaded into the table structure in the target database.

Abstract: A server is configured to receive an input file or table that includes a number of data elements, where the data elements include some sensitive data elements of different types; identify each of the different types of sensitive data elements; identify a subset of the sensitive data elements that corresponds to each of the different types of sensitive data elements; simultaneously perform data masking operations on the sensitive data elements to create masked sensitive data elements, where a separate one of the data masking operations is performed on each of the subsets of the sensitive data elements; generate an output file or table that includes the data elements from the input file or table, where the sensitive data elements, of the input file or table, are replaced with the masked sensitive data elements; and output or store the output file or table.

Abstract: An approach is provided for managing session information across application boundaries. Session information is obtained of a first application. The session information is stored. The session information is provided to a second application, wherein the session information is particular to a user session and includes data to be persisted across boundaries of the first application and the second application.

Abstract: A server is configured to receive an input file or table that includes a number of data elements, where the data elements include some sensitive data elements of different types; identify each of the different types of sensitive data elements; identify a subset of the sensitive data elements that corresponds to each of the different types of sensitive data elements; simultaneously perform data masking operations on the sensitive data elements to create masked sensitive data elements, where a separate one of the data masking operations is performed on each of the subsets of the sensitive data elements; generate an output file or table that includes the data elements from the input file or table, where the sensitive data elements, of the input file or table, are replaced with the masked sensitive data elements; and output or store the output file or table.

Abstract: An interface gateway may receive a request including a first interface identifier. The interface gateway is associated with a group of interfaces, where each interface is associated with metadata that defines the interface. The metadata may include an interface identifier, information identifying services to be executed for the interface and an order in which the identified services are to be executed, and information identifying servers on which the identified services are implemented. The interface gateway may also identify, for the received request, one interface, of the group of interfaces, for processing the request based on the first interface identifier. The interface gateway may further process the received request using the one interface.

Abstract: An approach is provided for utilizing role-based presentation interfaces. A role of a user among a plurality of roles is determined. The user is among a plurality of users that are grouped based on the roles. A presentation interface is populated with sales related information based on the determined role.

Abstract: An approach is provided for managing session information across application boundaries. Session information is obtained of a first application. The session information is stored. The session information is provided to a second application, wherein the session information is particular to a user session and includes data to be persisted across boundaries of the first application and the second application.