Alcatel Lucent

Alcatel-lucent USA Inc.

Nokia Technologies Oy

Nokia of America Corporation

Wsou Investments, LLC

Dan Kushnir

Huseyin Uzunalioglu

Ahmet Akyamac

Veena B Mendiratta

Derek Doran

Thomas P Chu

Chitra Phadke

Systems and methods for regulating data traffic using active learning techniques. In one embodiment, a global monitor communicates with a plurality of sensors over a wide area network. The global monitor builds a global model for a data analytics service that maps elements to values based on data reported by the sensors. The global monitor generates a query for the data from the sensors, selects one or more candidate elements from the elements in the global model, generates a global policy specifying that the data requested from the sensors is limited to the data targeted to the candidate element(s), and sends the query indicating the global policy to the sensors. The global monitor receives the data targeted to the candidate element(s) from the sensors according to the global policy, and adjusts the global model based on the data targeted to the candidate element(s).

Control of data reporting for a data analytics service using an active learning framework

Various embodiments relate to a network monitor and method thereof for receiving a plurality of alarms during a baseline period from a plurality of network elements, establishing a baseline for at least one of the plurality of the network elements by determining a number of the plurality of alarms during the baseline period that occur during each of a plurality of sub-intervals within the baseline period, calculating a mean arrival rate for each of the plurality of alarms during the baseline period, generating a probability density function for an arrival rate for each of the plurality of alarms during the baseline period, calculating a probabilistic score for each of a plurality of alarms during a test period based on the probability density function for the arrival rate of each of the plurality of alarms during the baseline period, determining if the score for each of the plurality of alarms during the test period is greater than or equal to a probabilistic threshold, summing the number of the scores for each of the plurality of alarms during the test period that is greater than or equal to probabilistic threshold, generating a list by sorting the plurality of network elements in descending order based on the sum of the number of the scores for each of the plurality of alarms that generated a probabilistic score greater than or equal to probabilistic threshold or based on a sum of the probabilistic scores that are greater than or equal to probabilistic threshold, reporting first network elements of the generated list and reporting alarms for the first network elements of the generated list having probabilistic scores higher than the probabilistic threshold.

Network monitor and method for event based prediction of radio network outages and their root cause

A capability is provided for adaptive polling of a device based on a set of polling control regions configured to control polling of the device. The set of polling control regions is defined based on at least one of a set of control parameters and non-parametric control information. A transition within the set of polling control regions is determined based on a current polling control region and a target polling control region that is determined based on input information received while in the current polling control region. The input information may include at least one of values of one or more parameters in the set of parameters and non-parametric input information. The transition may include remaining in the current polling control region or transitioning to a new polling control region. The transition may be performed based on a rapid up controlled down (RUCD) transition scheme.

Adaptive polling of information from a device

A processor accesses a plurality of time series of alarms of a plurality of alarm types that are produced by resources of a network function virtualization (NFV) system. The processor identifies clusters of the plurality of alarm types based on similarities between the plurality of time series and determine causal connections between alarm types in the clusters based on temporal proximity and ordering of the alarm types in the clusters. The processor then stores one or more causality templates representative of the causal connections in a memory.

Alarm causality templates for network function virtualization

Embodiments described herein provide improvements to the accuracy of location estimates. In one embodiment, an apparatus stores a first plurality of RF signal strength values that are mapped to known locations, and stores a second plurality of RF signal strength values that are not mapped to known locations. The apparatus estimates locations where the second plurality of RF signal strength values was measured based on the first plurality of RF signal strength values and their corresponding known locations, calculates an uncertainty for the estimated locations, and identifies an estimated location having a highest uncertainty. The apparatus directs a measurement device to measure an RF signal strength value at a known location that corresponds with the estimated location, and adds the RF signal strength value to the first plurality of RF signal strength values to reduce an uncertainty in subsequent estimates of locations within the broadcast area.

Optimized radio frequency signal strength sampling of a broadcast area for device localization

Various exemplary embodiments relate to a method of determining the root cause of service degradation in a network, the method including determining a window of time; determining one or more abnormal Key Quality Indicators (KQIs) in the window; determining one or more abnormal Key Performance Indicators (KPIs) in the window; calculating a conditional probability that each of one or more KPIs is abnormal when a Key Quality Indicator (KQI) is normal; calculating a conditional probability that the each of one or more KPIs is abnormal when the KQI is abnormal; calculating a score for each KPI based upon a divergence of a Beta distribution for the conditional probability that each of one or more KPIs is abnormal when a KQI is normal and a Beta distribution for the conditional probability that the each of one or more KPIs is abnormal when the KQI is abnormal; and generating a representative root-cause list based upon the score for each KPI.

Root cause analysis for service degradation in computer networks

An illustrative data classifier device includes data storage and at least one processor configured to operate as a query engine and a passive classifier that is configured to predict classification labels for data. The processor is configured to determine a relationship between the data and training data with associated training classification labels. The processor is also configured to assign a weighted version of at least one of the training classification labels to at least one member of the data based on the determined relationship. An illustrative method of classifying data includes predicting classification labels for data by determining a relationship between the data and training data with associated training classification labels. A weighted version of at least one of the training classification labels is assigned to at least one member of the data based on the determined relationship.

Data classifier using proximity graphs, edge weights, and propagation labels

A method for deriving a composite tie metric for an edge between nodes of a telecommunication call graph includes receiving descriptive data with original values for descriptive attributes associated with a telecommunication call graph formed by edges between nodes. Each edge relates to two nodes. Each original value relates to an edge and a descriptive attribute forming an edge-attribute pair for the corresponding original value. The descriptive data is stored in a local storage device. Scaling factors for each descriptive attribute are determined taking into account a distribution of the original values for the corresponding descriptive attribute and a common base for the descriptive attributes. Weighting factors are determined for each descriptive attribute. The composite tie metric is computed for an edge based on the original value, scaling factor, and weighting factor for the descriptive attributes.

Method and apparatus for deriving composite tie metric for edge between nodes of a telecommunication call graph

A system and method for generating a subscriber churn prediction includes receiving call detail records from a network operator detailing communication between subscribers of the network operator and determining tie-strengths between subscribers based on the call detail records. The system and method further includes generating a net churn influence accumulated at each subscriber from the tie-strengths by propagating churner influence between subscribers due to churn events.

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Springfield, NJ, United States of America

Dan Kushnir