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: Described herein is a system for generating echolocation sounds to assist a user having no sight or limited sight to navigate a three-dimensional space (e.g., physical environment, computer gaming experience, and/or virtual reality experience). Input is received from a user to generate echolocation sounds to navigate a three-dimensional space. Based at least on the received input, a digital representation of the three-dimensional space is segmented into one or more depth planes using an unsupervised machine learning algorithm. For each depth plane, object segments are determined for each object within the particular depth plane. Locations of a plurality of echo sound nodes are determined in accordance with the depth level and surface area of each object defined by the determined segments. The echolocation sounds comprising a spatialized sound from each echo sound node originating from the determined location are generated.

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 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 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: Described herein is a system for generating echolocation sounds to assist a user having no sight or limited sight to navigate a three-dimensional space (e.g., physical environment, computer gaming experience, and/or virtual reality experience). Input is received from a user to generate echolocation sounds to navigate a three-dimensional space. Based at least on the received input, a digital representation of the three-dimensional space is segmented into one or more depth planes using an unsupervised machine learning algorithm. For each depth plane, object segments are determined for each object within the particular depth plane. Locations of a plurality of echo sound nodes are determined in accordance with the depth level and surface area of each object defined by the determined segments. The echolocation sounds comprising a spatialized sound from each echo sound node originating from the determined location are generated.

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.