International Business Machines Corporation

Michele Gazzetti

Christian Pinto

Yiannis Gkoufas

Andrea Reale

Konstantinos Katrinis

Panagiotis Koutsovasilis

Stefano Braghin

Beat Buesser

Sergiy Zhuk

Marco Luca Sbodio

Stephen Michael Moore

Michele Berlingerio

Sean A McKenna

Seshu Tirupathi

Srikumar Venugopal

Marco Simioni

Ioannis Gkoufas

Pol MacAonghusa

Vincent Lonij

Kostas Katrinis

Systems and techniques that facilitate policy-driven views of datasets are provided. In various embodiments, a system can comprise a data component that can access a dataset. In various instances, the system can further comprise a view policy component that can respectively correlate a set of security credentials to a set of view instructions associated with the dataset. In various embodiments, the system can further comprise an input component that can receive as input a first security credential that is listed in the set of security credentials. In various aspects, the system can further comprise an output component that can identify a first view instruction that is listed in the set of view instructions associated with the dataset, wherein the first view instruction is respectively correlated to the first security credential. In various cases, the output component can generate as output a view of the dataset based on the first view instruction.

Policy-driven views of datasets

Local memory and disaggregated memory may be identified and monitored for integrating disaggregated memory in a computing system. Candidate data may be migrated between the local memory and disaggregated memory to optimize allocation of disaggregated memory and migrated data according to a dynamic set of migration criteria.

Integrating and increasing performance of disaggregated memory in operating systems

Embodiments for processing data with multiple machine learning models are provided. Input data is received. The input data is caused to be evaluated by a first machine learning model to generate a first inference result. The first inference result is compared to at least one quality of service (QoS) parameter. Based on the comparison of the first inference result to the at least one QoS parameter, the input data is caused to be evaluated by a second machine learning model to generate a second inference result.

Methods and systems for sequential model inference

A request may be identified having one or more constraints for accessing disaggregated resources in a computing environment. One or more resources in a plurality of disaggregated resources may be identified based on the request. Computing server instances may be dynamically orchestrated using the one or more resources in the plurality of disaggregated resources based on the one or more constraints.

Dynamic orchestration of disaggregated resources

In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

GPU accelerated perfusion estimation from multispectral videos

Embodiments for an intelligent directing service in an Internet of Things (IoT) computing environment by a processor. One or more objects may be identified within a defined region relative to an entity. At least a portion of an extremity of the entity may be directed to select or avoid the one or more objects according to one or more internet of things (IoT) devices.

Intelligent directing system in an internet of things (IoT) computing environment

Embodiments for preventing data loss in a computing storage environment by a processor. One or more offline events in a locality failing to have continuous network connectivity may be detected. An ad-hoc distributed file system, having one or more edge devices associated with the locality, may be created for storing data produced during the one or more offline events.

Minimizing data loss in a computer storage environment with non-guaranteed continuous network connectivity

Embodiments for optimization of storage and network usage in an Internet of Things (IoT) environment by a processor. A data stream production rate for one or more applications may be dynamically adjusted based on current network conditions, a network status forecast, storage availability, network disruption patterns, quality of service (QoS) policies, application or network priorities, or a combination thereof to reduce data loss at a locality caused by network disruption in the IoT computing environment.

Application-level, cooperative minimization of offlining incidents in an internet of things (IoT) environment

Embodiments for implementing a fault tolerant color palette in a device having potential hardware faults by a processor. A fault tolerant color palette, assistive of user color interpretation for a display, may be generated according to a set of colors in a predetermined color palette and one or more user preferences.

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Dublin, Ireland

Michele Gazzetti