International Business Machines Corporation

Nationaluniversity of Ireland, Maynooth

University College Dublin

Hitham Ahmed Assem Aly Salama

Patrick Joseph O'Sullivan

Jonathan D Dunne

James Patrick Galvin, Jr

Teodora Sandra Buda

Lei Xu

Faisal Ghaffar

Christian Benjamin Kau

Diaa Eldin Ali Mohamed Mahmoud Ali

Andres Omar Portillo Dominguez

Liam S Harpur

Paul Basil French

Peter Francis Sweeney

Erik Richter Altman

Nicholas Matthew Mitchell

Bora Caglayan

Shadi Ghaith

Al Chakra

Nirmala Venkatraman

Alfredo Del Campo Del Corral

Carlos Martin-Nieto Gonzalez

Luis Pinto

Daniel B Kehn

Christina Thorpe

Shane Brady

Leandro Almeida

Magdalena Kacmajor

Philip Perry

An embodiment includes a method for use in managing a cluster comprising a plurality of computers, each computer comprising at least one hardware processor coupled to at least one memory. The method comprises a computer-implemented manager: collecting access pattern data for the plurality of computers; based at least in part on the access pattern data, determining that at least a first one of the plurality of computers is overutilized; and responsive to determining that the first computer is overutilized, moving stored data from the first computer to at least a second one of the plurality of computers.

Managing heterogeneous cluster environment

A computer-implemented method, computer program product, and computing system is provided for determining quality of experience for communication sessions. In an implementation, a method may include determining a plurality of intrusive quality of experience scores associated with a plurality of intrusive audio transmission samples. The method may also include determining a plurality of non-intrusive quality of experience scores associated with a plurality of non-intrusive audio transmission samples. The method may further include deriving a quality of experience coefficient based upon a relationship between the plurality of intrusive quality of experience scores and the plurality of non-intrusive quality of experience scores.

Determining quality of experience for communication sessions

According to an embodiment of the present invention, an approach accurately detects anomalies or outliers of a time-series dataset. A method for identifying whether a particular data element of the time-series dataset is an outlier comprises predicting a value for that particular data element and obtaining a threshold value that defines, relative to the predicted value, whether an actual value of the data element is an outlier. In an aspect of a present invention embodiment, the threshold value is generated based on historic error values associated with data elements temporally preceding the particular data element of the time-series dataset.

Anomaly detection

Techniques that facilitate machine learning optimization are provided. In one example, a system includes a computational resource component, a batch interval component, and a machine learning component. The computational resource component collects computational resource data associated with a group of computing devices that performs a machine learning process. The batch interval component determines, based on the computational resource data, batch interval data indicative of a time interval to collect data for the machine learning process. The machine learning component provides the batch interval data to the group of computing devices to facilitate execution of the machine learning process based on the batch interval data.

Machine learning optimization framework

An embodiment for contextualizing abnormal events which employ location-based social networks, LBSN, data to determine events that may be linked to the abnormal events is provided. The embodiment may include detecting an occurrence of an abnormal event within a geographic region, wherein the abnormal event occurs at an occurrence time. The embodiment may also include obtaining location-based social networks, LBSN, data relating to the geographic region for a time period including the occurrence time. The embodiment may further include analyzing the obtained LBSN data, wherein the analyzation determines a linked event within the geographic region for the time period. The embodiment may also include associating the linked event with the abnormal event.

Abnormal event analysis

An embodiment includes a method for use in managing a system comprising one or more computers, each computer comprising at least one hardware processor coupled to at least one memory, the method comprising a computer-implemented manager: generating a potential configuration for hardware resources of the system; determining whether the potential configuration satisfies accuracy and time constraints for a selected machine learning model; if the potential configuration satisfies the constraints, indicating the potential configuration to be the optimal configuration for the system; and if the potential configuration does not satisfy the constraints, adapting the potential configuration to satisfy the constraints. The adapting may comprise repeating the generating and determining steps. The adapting may be based at least in part on the hardware resources and the selected machine learning model.

Adaptive configuration of a heterogeneous cluster environment

Embodiments for detecting data anomalies by a processor. A machine learning model may be trained according to collected scores and anomaly labels of a plurality of anomaly detection operations applied to one or more data sets such that the collected scores and labels identify a degree of accuracy of estimating anomalies for each of the plurality of anomaly detection operations. An anomaly may be detected in an unstructured data set by applying the trained machine learning model on an unstructured data set.

Detecting data anomalies

Embodiments for data replication in a distributed file system environment by a processor. Data replication of one or more files that are more frequently used as compared to other files in a plurality of files may be increased according to hot data detected from one or more queries to a distributed file system.

Data replication in a distributed file system

A system and method for recognizing recurrent crowd mobility patterns in a defined geographical area are presented. A crowded area may be determined for a selected geographical region at predefined time intervals based on spatial distribution of a plurality of users collected from a social media network. A crowd footprint may be generated according to the crowded areas determined at the predefined time intervals. Recurrent crowd mobility patterns may be detected according to the crowd footprint.

Recognizing recurrent crowd mobility patterns

Detecting data anomalies by receiving a first data set related to a first variable metric, determining data anomaly detection scores for data points of the first data set according to a plurality of data anomaly detection techniques, generating an adaptive ground-truth window according to the data anomaly detection scores, assigning a weighting value to each data point within the adaptive ground-truth window, training a machine learning system using the set of data anomaly detection scores and weighting values, and providing a trained machine learning system for evaluating a second data set.

Adaptive window based anomaly detection

A method, computer system, and a computer program product for improving social media network actions is provided. The present invention may include determining a proposed action by a user and predicting a predicted influence score for the proposed action. The present invention may include identifying a prior related action in the user's social media network. The present invention may include retrieving a previously calculated average influence score for the type and user of the prior related action and applying a decay profile to the average influence score from the time of the action to a current time. The present invention may include comparing the predicted influence score with the decayed average influence score at the current time. The present invention may include posting the proposed action on the social media network at a time when the predicted influence score is greater than the decayed average influence score.

Timing social media network actions

A computer-implemented method, computer program product, and computing system is provided for managing quality of experience for communication sessions. In an implementation, a method may include determining a participant focus metric for each of a plurality of participants of a communication session. The method may also include identifying one of the plurality of participants of the communication session as having sufficient capacity to support the communication session based upon, at least in part, the participant focus metric for each of the plurality of participants of the communication session. The method may also include selecting the one of the plurality of participants having a sufficient capacity to support the communication session as a selected focus for the communication session. The method may further include adaptively implementing a centralized communication session architecture utilizing the selected focus.

Quality of experience for communication sessions

In response to a user-initiated interaction request sent by a user using an electronic communication, subsequent actions performed by other users that received the user-initiated interaction request are analyzed. A determination is made as to whether the subsequent actions performed by the other users that received the user-initiated interaction request correlate to an intended interaction result of the user-initiated interaction request. A visual representation of a collaboration model that correlates probabilities of successful collaborations between the user and the other users is generated in accordance with determined correlations between the subsequent actions performed by the other users and the intended interaction result. A collaboration recommendation based upon a degree of correlation between the subsequent actions performed by the other users and the intended interaction result represented within the collaboration model is provided in association with the visual representation of the collaboration model.

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Blanchardstown, Ireland

Hitham Ahmed Assem Aly Salama