International Business Machines Corporation

Paul Gerver

Jason Allan Nikolai

Bradley William Fawcett

Jingdong Sun

Henry Chiu

Lennard G Streat

Austin J Dorenkamp

Mahesh P Bhat

Cameron J Young

Ryan Kirk Cradick

John A Riendeau

Rachel Mertz

Cameron Cornick

Jonathan Fouk

Nicholas Roessler

Jonathan C F Fouk

Laura J Mokrzycki

Encryption key management for containerized applications is provided. An application container receives a request, directed to a wrapped data encryption key (WDEK), and it is determined whether a local cache associated with the application container stores a mapping of the WDEK to an unwrapped data encryption key (DEK). In response to a cache miss, a shared list, that stores entries corresponding to WDEKs to be synchronized among a plurality of local encryption key caches of a set of application containers, is accessed to determine a set of WDEKs that are missing from the local cache. A key management service provides the WDEKs and corresponding DEKs for the set of WDEKs. The shared list data structure and the local encryption key cache are updated based on the WDEKs and DEKs obtained from the key management service, to thereby synchronize the local encryption key cache with the shared list data structure.

Key management cache synchronization

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

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

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

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 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 computer-implemented method for vehicle event management. The method receives one or more sets of vehicle sensor data from a first vehicle, wherein the one or more sets of vehicle sensor data correspond to one or more sensor systems of the first vehicle. The method further maps the received one or more sets of vehicle sensor data with one or more candidate vehicle events, based on one or more sets of archived vehicle sensor data. The method further identifies one or more relationships between the received one or more sets of vehicle sensor data and the one or more candidate vehicle events, and provides an alert, to the first vehicle, with the identified one or more relationships between the received one or more sets of vehicle sensor data and the one or more candidate vehicle events.

Cognitive learning for vehicle sensor monitoring and problem detection

A method, apparatus, system, and computer program product for detecting and obscuring references that inadvertently disclose key events according to a user's progress through a media. A computer system ingests data from the media and conceptualizes events from the data. The computer system identifies the key events in the media based on the conceptualized events. The computer system assigns a value to each identified key event. The computer system intakes the data that tracks a progression through the media. The computer system identifies references in related content data that are associated with the key events having values that exceeds a threshold. The computer system obscures the references to the key events displayed in the related content data according to the progression through the media.

Cognitive computing to identify key events in a set of data

Apparatuses, systems and computer-implemented methods are disclosed for augmented reality stimulus removal. A field definition module determines a permitted field of view for a user. A field recognition module identifies a portion of an image stream outside the permitted field of view. The image stream is obtained by an augmented reality device of the user. An overlay module modifies the image stream for display to the user via the augmented reality device. The overlay module modifies the image stream by obscuring the portion of the image stream outside the permitted field of view in response to a restrictive mode being set for the user.

Augmented reality for removing external stimuli

An approach is provided in which a system analyzes a first subject in a first image taken at a first point in time against a second subject in a second image taken at a second point in time. The first image and the second image are captured at a venue. The system determines, based on a time duration between the first point in time and the second point, a probability that the first subject is at the venue at the second point in time. Next, the system computes, based on the probability, a relevance score that quantifies a relationship between the first subject in the first image and the second subject in the second image. Finally, the system assigns the second image to a first image file corresponding to the first image based on the relevance score reaching a relevance score threshold.

Classifying and grouping electronic images

The embodiments relate to classifying and grouping images. Primary images and secondary images are created and stored. A preliminary relationship between the primary and secondary images is established. The primary image is compared with the secondary image to produce a relevance score. The relevance score is converted to a secondary image assignment, which includes selectively assigning the secondary image to a first group based on the relevance score.

Growing community of inventors

Rochester, MN, United States of America

Paul Gerver