International Business Machines Corporation

Jason Allan Nikolai

David M Koster

Andrew Thomas Thorstensen

John Matthew Santosuosso

Alexander Cook

Susan F Crowell

Cory J Kleinheksel

Bradley William Fawcett

Adam D Reznechek

Jingdong Sun

Henry Chiu

Joseph W Cropper

Adam D Braham

Michael J Branson

Ryan Kirk Cradick

Jeffrey Alan Schmidt

Paul Gerver

Eric L Barsness

Peter Andrew Nicholls

Steven Ocepek

Johnny A Shaieb

Robert Douglas Holt

Cary Lee Bates

Schuman M Shao

Aditi Rajagopal

Alexander J Pogue

Warren Patrick Acker

Deanna L Q Brown

Jason Bornheimer

Robert Maier

Michael Redford

Jeffrey A Prentice

James E Carey

Jim C Chen

Christopher R Sabotta

Jonathan Louis Kaus

Steven Edward Royer

Daniel Mark Hursh

Joseph C Schmidt

Byron Lewis Bailey

Justin Podzunas

Shane Smith

Christopher Bedell

Seth Grey Glasgow

Charles Henderson

William Ramon Menoyo

A computer-implemented method for prioritizing exclusion renewal records is disclosed. The computer-implemented method includes determining vulnerability factors associated with a vulnerability exclusion record. The computer-implemented method further includes generating a vulnerability factor score for each vulnerability factor associated with the vulnerability exclusion record based, at least in part, on a level of risk associated with the vulnerability factor. The computer-implemented method further includes generating a vulnerability score for the vulnerability exclusion record based, at least in part, on the vulnerability factor score for each vulnerability factor. The computer-implemented method further includes updating a previous vulnerability score of the vulnerability exclusion record.

Dynamic prioritization of vulnerability exclusion renewals

Computer software that causes a stream processing management node to perform the following operations: (i) establishing data communication between the stream processing management node and a plurality of stream processing instances executing on respective computing environments in a multi-environment computing system; (ii) distributing one or more processing units of a stream processing job to a first set of stream processing instances of the plurality of stream processing instances; (iii) receiving, from the one or more stream processing instances of the first set of stream processing instances, processing results associated with the one or more processing units of the stream processing job; and (iv) performing a machine learning based stream management operation based, at least in part, on the received processing results.

Selection of stream management operations based on machine learning in a distributed computing environment

The subject matter herein provides an automated system and method for software patch management that ranks patches at least in part according to a score indicative of a complexity (e.g., cost) of remediating a vulnerability. This score is sometimes referred to herein as a vulnerability remediation complexity (VRC) score. A VRC score provides an objective measure by which an organization can determine which patches are most likely to be successfully applied, thus enabling implementation of a patching strategy that preferentially applies most critical, but less impact (in terms of remediation cost) patches first to remediate as must risk as possible as quickly as possible. Thus, for example, the approach herein enables the patching to focus on vulnerabilities of highest severity and small remediation cost over those, for example, representing lower severity and higher remediation cost.

Vulnerability remediation complexity (VRC) system

A method, computer system, and a computer program product for managing a multi-vendor environment is provided. The present invention may include receiving a request to make a change to an aggregate computer system from a participant of the aggregate computer system. The present invention may include creating, in response to the request, a change block. The present invention may include receiving approval for the change block in accordance with an endorsement policy. The present invention may include adding the change block to a distributed ledger. The present invention may include monitoring a performance of the aggregate computer system.

Blockchain based multi vendor change monitoring system

Computer software executing on computer hardware that performs the following operations: (i) training a machine learning model to determine allocations of computing resources to processing elements of a stream processing job according to a specified objective; and (ii) allocating a set of computing resources to the processing elements by: allocating to the processing elements a first subset of the set of computing resources based, at least in part, on a minimum resource requirement for the processing elements, and allocating to the processing elements a second subset of the set of computing resources based, at least in part, on an allocation determined using the trained machine learning model.

Optimizing resource allocation for distributed stream processing systems

A method for maintaining version consistency of resources. The method provides for one or more processors to receive a submitted request to run a job in which the job includes a processing element and a timestamp associated with running the job. Identification of a resource type associated with the processing element is determined, based on a tag included in the job, associated with the processing element. A version of the resource type of the processing element is determined, based on a mapping of the tag associated with the identified resource type and the timestamp of the job. The resource type of the determined version is requested from a resource manager, and responsive to a confirmation of assigning the version of the resource type from the resource manager, the process element of the job is performed on the version of the resource type assigned by the resource manager.

Maintaining stream processing resource type versions in stream processing

In response to receiving a flow of data, one or more operators in a parallel region included in a distributed data processing system for stream processing the flow of data are initialized. In response to identifying a change to the flow of data or a change to the distributed data processing system, at least one of the flow of data and a size of the parallel region included in the distributed data processing system are dynamically adjusted. The flow of data is processed via the dynamically adjusted one of the flow of data and the parallel region included in the distributed data processing system.

Load balancing in streams parallel regions

An embodiment includes determining, in response to receiving data at a source node for transmission as streaming data on a network, whether a characteristic of the data indicates that an attribute of the data includes sensitive information. The embodiment also includes calculating, responsive to determining that the attribute includes sensitive information, an entropy value for the attribute. The embodiment also includes determining, responsive to the calculating of the entropy value, a likelihood that the attribute is encrypted by comparing the entropy value to a threshold value. The embodiment also includes generating a parameter value for a cryptographic parameter for the attribute.

Selective encryption of streaming data

A stream of tuples to be processed by processing elements that operate on one or more compute nodes of a stream application is monitored. Each processing element has one or more stream operators. A program request to execute a set of program code instructions is received. A stream application target of the set of program code instructions is identified based on the program request. A portion of the stream application is encapsulated with a generic program execution structure during operation of the stream application. The structure is configured to receive and execute program code instructions outside of a preconfigured operation of the stream application. The set of program code instructions is executed by the structure during operation of the stream application, at a time period of execution of the portion, in response to the program request, and based on the stream application target.

Aspect aware streams computing

A method, computer program product, and computing device for monitoring network activity associated with streaming a data load through a stream application including a plurality of stream operators deployed on a plurality of computing devices. One or more stream operators with one or more external connections may be identified from the plurality of stream operators. The identified one or more stream operators may be deployed based upon, at least in part, the one or more external connections.

System and method for identifying external connections in a streaming application

A method and system are provided. The method includes automatically monitoring one or more voice communication sessions occurring on multiple communication devices to transcribe into text, by a processor-based voice-to-text transcriber, voices uttered by two or more respective participants other than a user in each of the voice communications sessions. The method further includes comparing, for each of the voice communication sessions, the topic of interest to conversation features derived from the text to detect a match there between. The method also includes providing the user with a summary of each of the voice communication sessions for which the match has been detected by said comparing step, the summary being based on the text. The method additionally includes automatically connecting the user to at least one of the voice communication sessions for which the summary thereof has been provided to the user.

Notification bot for topics of interest on voice communication devices

A stream of tuples is monitored. The stream of tuples is to be processed by a plurality of processing elements of a stream application that operate on one or more compute nodes, each processing element having one or more stream operators. A processing rate of a first stream operator of the stream application is calculated. The processing rate is based on the number of tuples that are processed by the first stream operator. It is determined that the processing rate of the first stream operator meets a predetermined tuple processing criterion. The processing rate of the first stream operator is adjusted based on the predetermined tuple processing criterion.

Streams runtime processing rate adjustment

A method, system and computer program product for facilitating remote debugging of parallel regions in stream computing applications. A stream computing management server (SCMS) communicates a list of processing elements to a debugging interface. Responsive to setting a debugging breakpoint for a processing element of the list of processing elements, the SCMS receives a command to enable remote debugging for the selected processing element. In this regard, the processing element is a part of a parallel channel in a distributed processing environment. The SCMS maps the processing element to an attachment information in the distributed environment. The SCMS dynamically attaches a remote debugger to the processing element based on the attachment information.

Remote debugging parallel regions in stream computing applications

A method can include obtaining access card data from an access card. The access card can include accessibility data. The access card can be configured to electronically permit access to one or more systems by transmitting the accessibility data. The access card data can include at least a portion of the accessibility data. The method can further include storing the access card data. The method can further include transmitting the access card data to a penetration test system that is configured to test the one or more systems for at least one system vulnerability based, at least in part, on the access card data.

Access card penetration testing

A method, system and computer program product are provided for implementing anomaly detection and notification through profiled context. Anomalies are detected by effectively using multiple devices and using predefined profiles to generate a contextual comparison for subtle differences detected. When predefined subtle anomalies are detected using at least one of the predefined profiles, a selected notification is provided.

Method and system for anomaly detection and notification through profiled context

Growing community of inventors

Rochester, MN, United States of America

Jason Allan Nikolai