A method for discovering and diagnosing network anomalies. The method includes receiving key performance indicator (KPI) data and alarm data. The method includes extracting features based on samples obtained by discretizing the KPI data and the alarm data. The method includes generating a set of rules based on the features. The method includes identifying a sample as a normal sample or an anomaly sample. In response to identifying the sample as the anomaly sample, the method includes identifying a first rule that corresponds to the sample, wherein the first rule indicates symptoms and root causes of an anomaly included in the sample. The method further includes applying the root causes to derive a root cause explanation of the anomaly and performing a corrective action to resolve the anomaly based on the first rule.

Methods, apparatus, systems and articles of manufacture are disclosed herein to identify media presentation by analyzing network traffic. Example instructions cause a machine to generate a traffic profile to reduce a computational burden of identifying streaming media being presented on a media presentation device, the traffic profile including first network traffic data indicative of the streaming media; obtain the traffic profile and second network traffic data corresponding to the streaming media; and generate, in response to a score for the second network traffic data meeting a threshold of similarity, a network traffic analysis report identifying the streaming media being presented on the media presentation device.

Methods, computer-readable media and devices are disclosed for selecting a plurality of network devices to perform a plurality of tasks in accordance with a set of functional network analytics instructions. For example, a processor deployed in a telecommunication network may receive a set of functional network analytics instructions compiled from a set of instructions in accordance with a functional network analytics platform application programming interface. The processor may further, in accordance with the set of functional network analytics instructions, select a plurality of network devices to perform a plurality of tasks, send the plurality of tasks to the plurality of network devices, receive control plane data from the plurality of network devices, correlate the control plane data in accordance with operations defined in the set of functional network analytics instructions to create resulting data, and forward the resulting data to at least one recipient device.

Disclosed herein is a system that implements a model for automatic discovery and identification of a person who is most relevant to handle a notification generated for a resource based on a triggered event. The model accesses an activity log for the resource to identify operations that are relevant to a type of the event. The operations are performed by different users (e.g., owners of the shared resource). The model then calculates an operation relevance score for each of the operations and a user relevance score for each of the different users. The user relevance scores are used to identify a most relevant person from the different users. Contact information for the most relevant person (e.g., name, email address, phone number) is added to the notification so that a person that first views the notification can efficiently forward the notification to the person best positioned to deal with the event.

A system, method and non-transitory computer readable media for optimizing input data for an ML model associated with a communications network. In one implementation, example ML model(s) may be trained using a modified dataset obtained for a plurality of cellular aggregation units (CAUs) of the RAN infrastructure(s), wherein the modified dataset is derived from data collected for individual CAUs over a data collection period with respect to a plurality of KPI variables. The modified data set is optimized by replacement of null values of variables with corresponding modal values of the variables. The trained ML model may be used for predicting one or more KPIs based on a set of test data associated with the RAN infrastructure(s).

A system, method and non-transitory computer readable media for optimizing input data for an ML model associated with a communications network. In one implementation, example ML model(s) may be trained using a modified dataset obtained for a plurality of cellular aggregation units (CAUs) of the RAN infrastructure(s), wherein the modified dataset is derived from data collected for individual CAUs over a data collection period with respect to a plurality of KPI variables. The modified data set is optimized by replacement of null values of variables with corresponding modal values of the variables. The trained ML model may be used for predicting one or more KPIs based on a set of test data associated with the RAN infrastructure(s).

A communication method includes receiving, by a second data analytics network element, a status analytics output of a target object from a first data analytics network element. The target object includes one or more of a network device, a sub-domain of a network, an all-domain of a network, or a terminal device. The communication method also includes obtaining, by the second data analytics network element based on the status analytics output of the target object, first input data corresponding to a target type of analytics. The status analytics output of the target object indicates that the target object is in an abnormal state, indicating that the first input data does not comprise data corresponding to the target object. The communication method further includes generating, by the second data analytics network element based on the first input data, a first analytics output corresponding to the target type of analytics.

The disclosed scoring uses a “dynamic packet loss threshold” that is based on benchmarks of “good” packet loss behavior of network paths associated with circuits of different bandwidths and recent behavior of the path being scored. The observations for good packet loss behavior are bucketized by corresponding circuit load. For the path being scored, observations are also bucketized and aggregated into a moving average per load bucket. The moving averages represent recent behavior of the path by load bucket. The scoring system scores a path as a function of the current time interval packet loss of the network path being scored and the dynamic packet loss threshold of the current time interval. The dynamic packet loss threshold of the current time interval is a function of a good packet loss benchmark and the packet loss moving average for the load of the current time interval.

An apparatus and associated method for processing packets transmitted in a packet-switched communication network includes a sampling module that identifies amongst the received packets a plurality of samples distributed in a statistically uniform way amongst at least two non-overlapping sample sequences. Each sample sequence is then subjected to at least one identification rule, thereby identifying in the sample sequence at least one sub-sequence of samples fulfilling the at least one identification rule. The identification rule comprises a condition on the value of at least one identification field of the packets. Then, at least one parameter indicative of a behavior of the at least one sub-sequence of samples is provided.

A method, system, and computer program product for garbage collection of redundant partitions in distributed data management systems are provided. The method stores data across a set of nodes with the data being stored using one or more partitions and the data and the one or more partitions are replicated across the set of nodes. A first partition is determined to be stale at a first node of the set of nodes. The first partition is marked for deletion locally at the first node. A set of deletion votes are determined for the first partition with each node being associated with a deletion vote. The method determines a deletion decision for the first partition on the first node based on the set of deletion votes.