Bank of America Corporation

Fernando A Maisonett

Kyle Scott Sorensen

Conor Mitchell Liam Nodzak

Shreyas Srinivas

Brian C Busch

Angela Dawn Roose

Timothy J Bendel

Joseph C Corbett

Michelle Andrea Boston

Wyatt Edward Maxey

Candace R Gordon

Sandip Kumar

Michael S Karafotis

Constance Jones Suarez

Mary Kathleen Riley

Laurie Readhead

Nikhil Ram

Christopher Emmanuel Huntley

Sevara Ergasheva

Benjamin Judson Gaines, Iii

Christine Hoagland

Robert Charles Hoard

Steven Paul Davidson

Wesley Keville

Ashley Meadows

Lisa Michelle Cook

Dari Ann Mckenzie

Musa Ajakaiye

Maria Ervin

Jesse Antonio Hernandez

Ramesh Naidu Chatta

Kenyell Javon Ollie

Salvatore Michael Certo

Elizabeth Rachel Rock

Terri Dorinda Lail

Shailesh Deshpande

Heather Newell

Joseph Seth Cushing

Mukesh Maraj

David John Perro

Carmen R Barnhill

Robin J Buck

Tara Michel Ramirez

Viresh Taskar

Jayshree G Patel

Namrata Kaushik

Sanjay Singeetham

David Alan Beumer

Nivetha Raghavan

Lisa Julia Brown

Yvette Alston

Bradley Walton

Jason Kenneth Bellew

Noel Mary Fuller

Ravindra Bandaru

Sina Bauer

Sachin Varule

Linda Trent

Dnyanesh P Ballikar

Christie M Weekley

James Wilson Foy, Jr

Prasad V Annadata

Jamie Gilchrist

Systems, computer program products, and methods are described herein for reconstructing time aware data activity across multiple software applications. The method includes receiving one or more node communication data packets. Each of the one or more node communication data packets are time-stamped and include two nodes of a plurality of nodes within a network. The method also includes generating a first time period path graph. The first time period path graph includes a directional data flow between the plurality of nodes during a first time period. The first time period path graph is generated based on the one or more node communication data packets. The method further includes determining a central node of the plurality of nodes for the first time period. The central node of the plurality of nodes is determined based on the first time period path graph.

System and method for reconstructing time aware data activity across multiple software applications

A multitenant server application dependency mapping system maps data flows through multitenant infrastructure components through the use of a machine learning model framework that continually learns data flow patterns across the enterprise network and predicts the state of any given server. The multitenant server application dependency mapping system treats the network architecture as a whole and collects data accordingly, and uses that data to compute state probabilities conditioned upon both a point in time (and the observed prior states retrieved from the historical telemetry data. This provides a way to predict the likelihood of observing a tenant state being occupied, while also accounting for variations among the activity levels of various application. To forecast future states of all infrastructure components, the transition probabilities from tenant state to tenant state are then computed through time and used as inputs to the model to provide an accurate reconstruction of the data flows through all multitenant infrastructure components.

Machine learning-based multitenant server application dependency mapping system

Systems, computer program products, and methods are described herein for data community detection via data network telemetry. Packet header information and a corresponding record is received via a network sensing module. The record is analyzed to identify the direction(s) of the data transfer. The record is then validated and used to generate at least one directionality map, which is segmented into a plurality of time segments based on a user defined interval. A clustering algorithm is then used to determine at least one community for each of the directionality maps. The at least one directionality maps is then displayed at the user interface of an endpoint device. The at least one community is then provided with a functional group indicator, the functional group indicator compared to a stored functional group indicator to identify any mismatches.

System and method for data community detection via data network telemetry

Embodiments of the present invention provide a system for identifying occurrence of events and performing one or more actions to mitigate the impacts of the events. The system is configured for gathering data from one or more data sources of an entity, generating dataflows using the data gathered from the one or more data sources, identifying an anomaly based on one or more indicators and the dataflows, determining occurrence of an event and generating one or more propagation models associated with the event, performing event impact analysis based on the one or more propagation models, perform one or more actions to contain the event based on the one or more propagation models, identifying a last good copy of data based on the data gathered from the one or more data sources, retrieving the last good copy of data, and restoring the last good copy of data.

Consolidated data restoration framework

Growing community of inventors

Charlotte, NC, United States of America

Fernando A Maisonett