Abstract: A method implemented through a data processing device of a computing network including includes detecting a point anomaly in real-time data associated with each network entity based on determining whether the real-time data falls outside a threshold expected value thereof, and representing the detected point anomaly in a full mesh Q node graph, with Q being a number of features applicable for the each network entity. The method also includes capturing a transition in the point anomaly associated with a newly detected anomaly or non-anomaly in the real-time data associated with one or more of the Q number of features via the representation of the full mesh Q node graph, and deriving a current data correlation score for the point anomaly across the captured transition via the representation of the full mesh Q node graph.

Abstract: A method implemented through a server of a cloud computing network including subscribers of application acceleration as a service provided therethrough includes detecting a point anomaly in real-time data associated with each network entity based on determining whether the real-time data falls outside a threshold expected value thereof, and representing the detected point anomaly in a full mesh Q node graph, with Q being a number of features applicable for the each network entity. The method also includes capturing a transition in the point anomaly associated with a newly detected anomaly or non-anomaly in the real-time data associated with one or more of the Q number of features via the representation of the full mesh Q node graph, and deriving a current data correlation score for the point anomaly across the captured transition via the representation of the full mesh Q node graph.

Abstract: A method implemented through a server of a cloud computing network including subscribers of application acceleration as a service provided therethrough includes detecting a set of point anomalies in real-time data associated with each network entity for each feature thereof, and, in accordance with reading anomaly scores associated with an event as an input feedback, the each feature of the each network entity as a dimension of the input feedback and a category of the event as a label thereof, predictively classifying a future event into a predicted category in accordance with subjecting the anomaly scores associated with the event to a binning process and interpreting a severity indicator of the event. The method also includes refining the predictive classification of the future event based on a subsequent input to the server from a client device modifying a classification model for predictively classifying the future event into the predicted category.

Abstract: A method implemented through a server of a cloud computing network including subscribers of application acceleration as a service provided therethrough includes detecting a set of point anomalies in real-time data associated with each network entity for each feature thereof, and determining at least a subset of the set of point anomalies as a sequential series of continuous anomalies based on a separation in time between immediately next point anomalies thereof. The method also determining a current longest occurring sequence of anomalies in the set of point anomalies, and, in light of new point anomalies of the set of point anomalies in the real-time data detected, improving performance of determination of a subsequent longest occurring sequence of anomalies in the set of point anomalies based on combining the determined current longest occurring sequence of anomalies incrementally with one or more new point anomalies.

Abstract: A method implemented through a server of a cloud computing network including subscribers of application acceleration as a service provided therethrough includes detecting a point anomaly in real-time data associated with each network entity based on determining whether the real-time data falls outside a threshold expected value thereof, and representing the detected point anomaly in a full mesh Q node graph, with Q being a number of features applicable for the each network entity. The method also includes capturing a transition in the point anomaly associated with a newly detected anomaly or non-anomaly in the real-time data associated with one or more of the Q number of features via the representation of the full mesh Q node graph, and deriving a current data correlation score for the point anomaly across the captured transition via the representation of the full mesh Q node graph.

Abstract: A method implemented through a server of a cloud computing network including subscribers of application acceleration as a service provided therethrough includes sampling time series data associated with each network entity for each feature thereof into a smaller time interval as a first data series and a second data series including a maximum value and a minimum value respectively of the sampled time series data for the each feature within the smaller time interval, and generating a reference data band from predicted future data sets. The method also includes detecting, based on the reference data band, an anomaly in real-time data associated with the each network entity for the each feature thereof and determining an event associated with a pattern of change of the real-time data associated with the each network entity based on executing an optimization algorithm to determine a series of anomalies including the detected anomaly.

Abstract: A method implemented through a server of a cloud computing network including subscribers of application acceleration as a service provided therethrough includes sampling time series data associated with each network entity for each feature thereof into a smaller time interval as a first data series and a second data series including a maximum value and a minimum value respectively of the sampled time series data for the each feature within the smaller time interval, and generating a reference data band from predicted future data sets. The method also includes detecting, based on the reference data band, an anomaly in real-time data associated with the each network entity for the each feature thereof and determining an event associated with a pattern of change of the real-time data associated with the each network entity based on executing an optimization algorithm to determine a series of anomalies including the detected anomaly.