Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network, including voltage fluctuations, current levels, temperature data, humidity measurements, and other observable physical quantities. The nodes execute stream functions to process the recorded time series data and generate data streams. The node is configured to transmit generated data streams to neighboring nodes. A neighboring node may execute other stream functions to process the received data stream(s), thereby generating additional data streams. A server coupled to the wireless mesh network collects and processes the data streams to identify events occurring within the network.

Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network, including voltage fluctuations, current levels, temperature data, humidity measurements, and other observable physical quantities. The nodes execute stream functions to process the recorded time series data and generate data streams. The node is configured to transmit generated data streams to neighboring nodes. A neighboring node may execute other stream functions to process the received data stream(s), thereby generating additional data streams. A server coupled to the wireless mesh network collects and processes the data streams to identify events occurring within the network.

Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network (or any other device network). One or more servers coupled to the wireless mesh network configures a data ingestion cloud to receive and process the time series data from the nodes to generate data streams. The server(s) also configure a distributed processing cloud to perform historical analysis on data streams, and a real-time processing cloud to perform real-time analysis on data streams. The distributed processing cloud and the real-time processing cloud may interoperate with one another in response to processing the data streams. Specifically, the real-time processing cloud may trigger a historical analysis on the distributed processing cloud, and the distributed processing cloud may trigger real-time processing on the real-time processing cloud. Any of the processing clouds may encompass edge nodes configured to perform real-time processing and generate data streams.

Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network (or any other device network). One or more servers coupled to the wireless mesh network configures a data ingestion cloud to receive and process the time series data from the nodes to generate data streams. The server(s) also configure a distributed processing cloud to perform historical analysis on data streams, and a real-time processing cloud to perform real-time analysis on data streams. The distributed processing cloud and the real-time processing cloud may interoperate with one another in response to processing the data streams. Specifically, the real-time processing cloud may trigger a historical analysis on the distributed processing cloud, and the distributed processing cloud may trigger real-time processing on the real-time processing cloud. Any of the processing clouds may encompass edge nodes configured to perform real-time processing and generate data streams.

Abstract: A node within a wireless mesh network is configured to record a zero crossing of alternating current or alternating voltage drawn by a single-phase power consumer and a precise timestamp when the zero crossing occurred, thereby generating timestamped zero crossing data. The node receives similar zero crossing data from a neighboring node. The node then compares the timestamped zero crossing data with the received zero crossing data to determine whether the phase associated with the node is equivalent to, leads, or lags the phase associated with the neighboring node. The node then acquires a positive phase identification associated with the neighboring node. Based on the phase identification, and based on the phase difference between the two nodes, the node infers the phase associated with the single-phase power consumer. That phase indicates the specific power line within a three-phase power distribution network to which the single-phase power consumer is coupled.

Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network, including voltage fluctuations, current levels, temperature data, humidity measurements, and other observable physical quantities. The nodes execute stream functions to process the recorded time series data and generate data streams. The node is configured to transmit generated data streams to neighboring nodes. A neighboring node may execute other stream functions to process the received data stream(s), thereby generating additional data streams. A server coupled to the wireless mesh network collects and processes the data streams to identify events occurring within the network.

Abstract: A node within a wireless mesh network is configured to record a zero crossing of alternating current or alternating voltage drawn by a single-phase power consumer and a precise timestamp when the zero crossing occurred, thereby generating timestamped zero crossing data. The node receives similar zero crossing data from a neighboring node. The node then compares the timestamped zero crossing data with the received zero crossing data to determine whether the phase associated with the node is equivalent to, leads, or lags the phase associated with the neighboring node. The node then acquires a positive phase identification associated with the neighboring node. Based on the phase identification, and based on the phase difference between the two nodes, the node infers the phase associated with the single-phase power consumer. That phase indicates the specific power line within a three-phase power distribution network to which the single-phase power consumer is coupled.

Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network (or any other device network). One or more servers coupled to the wireless mesh network configures a data ingestion cloud to receive and process the time series data from the nodes to generate data streams. The server(s) also configure a distributed processing cloud to perform historical analysis on data streams, and a real-time processing cloud to perform real-time analysis on data streams. The distributed processing cloud and the real-time processing cloud may interoperate with one another in response to processing the data streams. Specifically, the real-time processing cloud may trigger a historical analysis on the distributed processing cloud, and the distributed processing cloud may trigger real-time processing on the real-time processing cloud. Any of the processing clouds may encompass edge nodes configured to perform real-time processing and generate data streams.

Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network, including voltage fluctuations, current levels, temperature data, humidity measurements, and other observable physical quantities. The nodes execute stream functions to process the recorded time series data and generate data streams. The node is configured to transmit generated data streams to neighboring nodes. A neighboring node may execute other stream functions to process the received data stream(s), thereby generating additional data streams. A server coupled to the wireless mesh network collects and processes the data streams to identify events occurring within the network.

Abstract: Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network (or any other device network). One or more servers coupled to the wireless mesh network configures a data ingestion cloud to receive and process the time series data from the nodes to generate data streams. The server(s) also configure a distributed processing cloud to perform historical analysis on data streams, and a real-time processing cloud to perform real-time analysis on data streams. The distributed processing cloud and the real-time processing cloud may interoperate with one another in response to processing the data streams. Specifically, the real-time processing cloud may trigger a historical analysis on the distributed processing cloud, and the distributed processing cloud may trigger real-time processing on the real-time processing cloud. Any of the processing clouds may encompass edge nodes configured to perform real-time processing and generate data streams.

Abstract: Techniques for performing diagnostics for a monitored system. In one set of embodiments, an aggregate model can be built from a set of models corresponding to entities (e.g., applications and systems) in the monitored system, and data from the monitored system can be applied to the aggregate model. Diagnostic result information can then be generated based on the application of the data to the aggregate model. In certain embodiments, generating the diagnostic result information can include determining a state of an application or system in the monitored system and determining an impact of the state to a user.

Abstract: Techniques for building a model for performing diagnostics. In one embodiment, a set of models is determined based upon a topological relationship created upon receiving an alert or a request for which diagnostics are to be performed. An aggregate model is then generated based upon the set of models and the topological relationship. The aggregate model is then used for performing the diagnostics.

Abstract: Techniques for performing diagnostics are described. In one embodiment, in response to an alert or a request to perform diagnostics, a topological relationship is generated comprising a set of applications and a set of systems determined based upon information in the alert or request. The topological relationship encapsulates relationships between the set of applications and the set of systems. In one embodiment, a set of causal network models to be used for performing the diagnostics is determined based upon the applications and systems in the topological relationship.

Abstract: Techniques for performing diagnostics for a monitored system. In one embodiment, an aggregate model built using a set of models is used to determine a set of probes or tests to be executed for obtaining information related to the monitored system. The obtained evidence information is then applied to the aggregate model to perform diagnostics for one or more conditions detected in the monitored system.

Abstract: Techniques for performing diagnostics for a monitored system. In one set of embodiments, an aggregate model can be built from a set of models corresponding to entities (e.g., applications and systems) in the monitored system, and data from the monitored system can be applied to the aggregate model. Diagnostic result information can then be generated based on the application of the data to the aggregate model. In certain embodiments, generating the diagnostic result information can include determining a state of an application or system in the monitored system and determining an impact of the state to a user.

Abstract: Techniques for performing diagnostics are described. In one embodiment, in response to an alert or a request to perform diagnostics, a topological relationship is generated comprising a set of applications and a set of systems determined based upon information in the alert or request. The topological relationship encapsulates relationships between the set of applications and the set of systems. In one embodiment, a set of causal network models to be used for performing the diagnostics is determined based upon the applications and systems in the topological relationship.

Abstract: Techniques for performing diagnostics for a monitored system. In one embodiment, an aggregate model built using a set of models is used to determine a set of probes or tests to be executed for obtaining information related to the monitored system. The obtained evidence information is then applied to the aggregate model to perform diagnostics for one or more conditions detected in the monitored system.

Abstract: Techniques for building a model for performing diagnostics. In one embodiment, a set of models is determined based upon a topological relationship created upon receiving an alert or a request for which diagnostics are to be performed. An aggregate model is then generated based upon the set of models and the topological relationship. The aggregate model is then used for performing the diagnostics.