International Business Machines Corporation

Yiannis Gkoufas

Michele Gazzetti

Andrea Reale

Stefano Braghin

Christian Pinto

Michael Johnston

Konstantinos Katrinis

Vasileios Vasileiadis

Kostas Katrinis

Seetharami R Seelam

Beat Buesser

Sean A McKenna

Srikumar Venugopal

Marco Simioni

Vincent Lonij

Steven Nicholas Eliuk

In an approach for storage, search, acquisition, and composition of a digital artifact, a processor obtains the digital artifact in a digital marketplace platform. The digital artifact is a collection of digital data with automatically generated and verifiable provenance and usage data. A processor transforms the digital artifact to define an access privilege. A processor shares the digital artifact in the digital marketplace platform by providing a view of a catalogue including the digital artifact. A processor authorizes a usage request based on the access privilege. A processor rewards a source of the digital artifact based on the usage of the digital artifact.

Digital artifact marketplace

In an approach for transforming workflows based on goals and constraints, a processor receives a workflow definition and one or more events for a workflow output by a workflow orchestrator, wherein the one or more events are at least one of a parsing event and a scheduling event. A processor determines, using one or more transformers, one or more transformations to the workflow based on a set of goals and constraints, wherein the one or more transformations is at least one of addition of a new node, addition of a new graph, modification of at least one existing node, modification of at least one existing graph, removal of at least one existing node, and removal of at least one existing graph. A processor dynamically applies, using one or more enforcers, the one or more transformations to the workflow based on the set of goals and constraints.

Workflow transformation framework

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 data caching and data-aware placement for machine learning by a processor. Data may be cached in a distributed data store to one or more local compute nodes of cluster of nodes with the cached data. A new job may be scheduled, according to cache and data locality awareness, on the one or more local compute nodes with the cached data needed for execution.

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Dublin, Ireland

Yiannis Gkoufas