Abstract: Technologies are provided for reporting, monitoring, analyzing performance of distributed energy resources (DERs). The monitoring and analysis of a DER can be performed in real-time or essentially real-time. The monitoring also can include health monitoring of a DER. The reporting of performance of a DER is configurable. The monitoring and analysis of performance of the DER also is configurable. Attributes that control the reporting, monitoring, and analysis can be interactively configured via user interfaces having one multiple control elements. Performance of a DER can be reported, monitored, and analyzed irrespective of manufacturer of the DER.

Abstract: Example embodiments of the present invention relate to a method, a system, and a computer program product for disease outbreak prediction analytics. The method includes calculating a respective correlation value for each outbreak attribute pair among a set of outbreak attributes for a data set regarding a disease and assigning a weight value for each outbreak attribute according to the correlation values. A risk value for the disease then may be determined according to the weight values.

Abstract: A processing device in one embodiment comprises a processor coupled to a memory and is configured to obtain data characterizing a plurality of network sessions for a given user identifier. The network sessions are initiated from one or more user devices over at least one network and may comprise respective virtual private network (VPN) sessions. The processing device is further configured to extract features from the obtained data, to detect at least one potentially anomalous network session among the plurality of network sessions for the given user identifier by applying the extracted features to a support vector machine model, and to apply a rules-based verification process to the detected potentially anomalous network session in order to verify that the detected potentially anomalous network session is an anomalous network session. An alert is generated based on a result of the rules-based verification process and transmitted to a security agent.

Abstract: Activity-related data associated with a subject entity is obtained. Weight values are assigned to portions of the obtained activity-related data, wherein the weight values range between a low entity location-predictive weight value to a high entity location-predictive weight value, and wherein the portions of obtained activity-related data form a plurality of data paths. The assigned weight values are added along each data path of obtained activity-related data to compute a plurality of data path weight values. A past, present and/or future location of the subject entity is determined based on a selection of one of the plurality of data path weight values. For example, the location associated with the data path with the highest data path weight value is selected as the location of the subject entity.