Abstract: Examples described herein generally relate to receiving a query context for service events occurring on one or more networks, determining, based on the query context, a set of service events occurring on the one or more networks, querying multiple layers of a multiple-layer relational graph to determine one or more other service events having a defined relationship with the set of service events at one or more of the multiple layers, where the multiple layers include a configuration layer, an observation layer, and learned layer, defining relationships between services or service events, and indicating, via a user interface and in response to the query context, the one or more other service events.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.

Abstract: Examples described herein generally relate to receiving a query context for service events occurring on one or more networks, determining, based on the query context, a set of service events occurring on the one or more networks, querying multiple layers of a multiple-layer relational graph to determine one or more other service events having a defined relationship with the set of service events at one or more of the multiple layers, where the multiple layers include a configuration layer, an observation layer, and learned layer, defining relationships between services or service events, and indicating, via a user interface and in response to the query context, the one or more other service events.

Abstract: Examples described herein generally relate to managing a knowledge graph by providing, to an agent and based on a request from the agent, an output of a number of signals having an indicated correlation in a knowledge graph, receiving, from the agent, additional correlation information for at least a portion of the number of signals and/or additional signals, and storing, in the knowledge graph, the additional correlation information.

Abstract: Examples described herein generally relate to receiving a query context for service events occurring on one or more networks, determining, based on the query context, a set of service events occurring on the one or more networks, querying multiple layers of a multiple-layer relational graph to determine one or more other service events having a defined relationship with the set of service events at one or more of the multiple layers, where the multiple layers include a configuration layer, an observation layer, and learned layer, defining relationships between services or service events, and indicating, via a user interface and in response to the query context, the one or more other service events.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.

Abstract: Examples described herein generally relate to receiving a query context for service events occurring on one or more networks, determining, based on the query context, a set of service events occurring on the one or more networks, querying multiple layers of a multiple-layer relational graph to determine one or more other service events having a defined relationship with the set of service events at one or more of the multiple layers, where the multiple layers include a configuration layer, an observation layer, and learned layer, defining relationships between services or service events, and indicating, via a user interface and in response to the query context, the one or more other service events.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.

Abstract: Examples described herein generally relate to identifying a set of service events corresponding to an incident report, querying a multiple-layer relational graph to determine one or more other service events related to the set of service events, detecting a pattern in the set of service events and a subset of the one or more other service events, and indicating, via a user interface and based on the incident report, the subset of the one or more other service events as related to the incident report.

Abstract: Examples described herein generally relate to managing a knowledge graph by providing, to an agent and based on a request from the agent, an output of a number of signals having an indicated correlation in a knowledge graph, receiving, from the agent, additional correlation information for at least a portion of the number of signals and/or additional signals, and storing, in the knowledge graph, the additional correlation information.

Abstract: Examples described herein generally relate to identifying a set of service events corresponding to an incident report, querying a multiple-layer relational graph to determine one or more other service events related to the set of service events, detecting a pattern in the set of service events and a subset of the one or more other service events, and indicating, via a user interface and based on the incident report, the subset of the one or more other service events as related to the incident report.

Abstract: Examples described herein generally relate to receiving a query context for service events occurring on one or more networks, determining, based on the query context, a set of service events occurring on the one or more networks, querying multiple layers of a multiple-layer relational graph to determine one or more other service events having a defined relationship with the set of service events at one or more of the multiple layers, where the multiple layers include a configuration layer, an observation layer, and learned layer, defining relationships between services or service events, and indicating, via a user interface and in response to the query context, the one or more other service events.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.

Abstract: A deployable computing environment may facilitate interaction and data sharing between users and devices. Users, devices, and relationships between the users and devices may be represented within the deployable computing environment. A relationship between a user and a device may specify that the device is owned by the user and that the device is authorized to perform operations within the deployable computing environment on behalf of the user. Secure authentication of devices and users for interaction within the deployable computing environment is achieved by authenticating tickets corresponding to the user, the device, and the relationship. A device identification ticket and a user identification ticket are used to authenticate the device and user for interaction within the deployable computing environment. A device claim ticket allows the device to perform delegated operations (e.g., data synchronization, peer connectivity, etc.) on behalf of the user without the user's credentials (e.g.

Abstract: A deployable computing environment may facilitate interaction and data sharing between users and devices. Users, devices, and relationships between the users and devices may be represented within the deployable computing environment. A relationship between a user and a device may specify that the device is owned by the user and that the device is authorized to perform operations within the deployable computing environment on behalf of the user. Secure authentication of devices and users for interaction within the deployable computing environment is achieved by authenticating tickets corresponding to the user, the device, and the relationship. A device identification ticket and a user identification ticket are used to authenticate the device and user for interaction within the deployable computing environment. A device claim ticket allows the device to perform delegated operations (e.g., data synchronization, peer connectivity, etc.) on behalf of the user without the user's credentials (e.g.

Abstract: A deployable computing environment may facilitate interaction and data sharing between users and devices. Users, devices, and relationships between the users and devices may be represented within the deployable computing environment. A relationship between a user and a device may specify that the device is owned by the user and that the device is authorized to perform operations within the deployable computing environment on behalf of the user. Secure authentication of devices and users for interaction within the deployable computing environment is achieved by authenticating tickets corresponding to the user, the device, and the relationship. A device identification ticket and a user identification ticket are used to authenticate the device and user for interaction within the deployable computing environment. A device claim ticket allows the device to perform delegated operations (e.g., data synchronization, peer connectivity, etc.) on behalf of the user without the user's credentials (e.g.

Abstract: A deployable computing environment may facilitate interaction and data sharing between users and devices. Users, devices, and relationships between the users and devices may be represented within the deployable computing environment. A relationship between a user and a device may specify that the device is owned by the user and that the device is authorized to perform operations within the deployable computing environment on behalf of the user. Secure authentication of devices and users for interaction within the deployable computing environment is achieved by authenticating tickets corresponding to the user, the device, and the relationship. A device identification ticket and a user identification ticket are used to authenticate the device and user for interaction within the deployable computing environment. A device claim ticket allows the device to perform delegated operations (e.g., data synchronization, peer connectivity, etc.) on behalf of the user without the user's credentials (e.g.

Abstract: A deployable computing environment may facilitate interaction and data sharing between users and devices. Users, devices, and relationships between the users and devices may be represented within the deployable computing environment. A relationship between a user and a device may specify that the device is owned by the user and that the device is authorized to perform operations within the deployable computing environment on behalf of the user. Secure authentication of devices and users for interaction within the deployable computing environment is achieved by authenticating tickets corresponding to the user, the device, and the relationship. A device identification ticket and a user identification ticket are used to authenticate the device and user for interaction within the deployable computing environment. A device claim ticket allows the device to perform delegated operations (e.g., data synchronization, peer connectivity, etc.) on behalf of the user without the user's credentials (e.g.

Abstract: A representational state transfer-based model for a computing environment uses models resources with links between them. Security principals are resources which can be independently authenticated. Each resource may be associated with an authorization policy that determines level of access, protocol supported. Successfully presenting security credentials at a security principal allows use of an instance of the security principal (i.e. application) as well as generation of an authentication token that can be presented across the computing environment to resources subscribing to the same authorization policy. As security principals with different security policies are authenticated, the appropriate tokens may be combined to allow broader access without undue re-authentication for resources subscribing to the same security policy.