Abstract: The scheduling or allocation of images to nodes of a containerized computing environment is provided. Images that share layers (i.e. images that have one or more layers in common) are allocated to the same node. To determine whether different images share one or more layers, leveraging metadata that is associated with the images is provided. By analyzing metadata associated with the images, it may be determined if the images have one or more layers in common (i.e. comprise the same layer(s)). In this way, information that is already available with conventional images may be used.

Abstract: An example operation may include one or more of determining a data point trigger has occurred at a particular block cycle of a blockchain, initiating a sidechain to store subsequent entries based on the data point trigger, and a genesis block of the sidechain includes one or more relevant data fields from the blockchain, initiating a sidechain smart contract to manage data entries submitted to the sidechain, storing the data entries in the sidechain for a conditional period, and when the conditional period has matured, convoluting the sidechain into the blockchain.

Abstract: Building images that enable improved utilization of previously built image layers. An image build system evaluates commands prior to their use and differentiate between stateful and stateless commands. Employing such an approach enables stateless commands to be identified (e.g. labeled), thus enabling the image build system to handle the stateless commands differently from stateful commands. This enables the re-use of cached/stored image layers, thus reducing image size by avoiding the creation of new image layers.

Abstract: Unstructured text is identified as larger than a threshold size. Named-entity recognition analysis is executed on the unstructured text. One or more anchor entities of the unstructured text are determined that each occur more than a threshold amount of times within the unstructured text. Two or more instances of the one or more anchor entities that are separated by at least a threshold amount of text of the unstructured text are identified. The unstructured text is partitioned into at least three sections. The unstructured text is partitioned at respective natural language demarcation points associated with each of the two or more instances such that each of the at least three sections is smaller than the threshold size. Separate coreference analyses are performed in parallel on each of the at least three sections.

Abstract: Unstructured text is identified as larger than a threshold size. Named-entity recognition analysis is executed on the unstructured text. One or more anchor entities of the unstructured text are determined that each occur more than a threshold amount of times within the unstructured text. Two or more instances of the one or more anchor entities that are separated by at least a threshold amount of text of the unstructured text are identified. The unstructured text is partitioned into at least three sections. The unstructured text is partitioned at respective natural language demarcation points associated with each of the two or more instances such that each of the at least three sections is smaller than the threshold size. Separate coreference analyses are performed in parallel on each of the at least three sections.

Abstract: The scheduling or allocation of images to nodes of a containerized computing environment is provided. Images that share layers (i.e. images that have one or more layers in common) are allocated to the same node. To determine whether different images share one or more layers, leveraging metadata that is associated with the images is provided. By analyzing metadata associated with the images, it may be determined if the images have one or more layers in common (i.e. comprise the same layer(s)). In this way, information that is already available with conventional images may be used.

Abstract: Building images that enable improved utilization of previously built image layers. An image build system evaluates commands prior to their use and differentiate between stateful and stateless commands. Employing such an approach enables stateless commands to be identified (e.g. labeled), thus enabling the image build system to handle the stateless commands differently from stateful commands. This enables the re-use of cached/stored image layers, thus reducing image size by avoiding the creation of new image layers.

Abstract: Techniques for dynamic notifications including generating a machine learning model based on user profiles of a collaboration application. The technique further including receiving a first message from input to a first user interface presenting the collaboration application and associated with a first user profile, the first message including first content and a domain specific language (DSL) condition. The technique further including generating a plurality of notification groups, presenting the plurality of notification groups, and receiving a selected notification group. The technique further includes sending the first content to each user profile in the selected notification group.

Abstract: Techniques for dynamic notifications including generating a machine learning model based on user profiles of a collaboration application. The technique further including receiving a first message from input to a first user interface presenting the collaboration application and associated with a first user profile, the first message including first content and a domain specific language (DSL) condition. The technique further including generating a plurality of notification groups, presenting the plurality of notification groups, and receiving a selected notification group. The technique further includes sending the first content to each user profile in the selected notification group.

Abstract: An example operation may include one or more of determining a data point trigger has occurred at a particular block cycle of a blockchain, initiating a sidechain to store subsequent entries based on the data point trigger, and a genesis block of the sidechain includes one or more relevant data fields from the blockchain, initiating a sidechain smart contract to manage data entries submitted to the sidechain, storing the data entries in the sidechain for a conditional period, and when the conditional period has matured, convoluting the sidechain into the blockchain.

Abstract: A computer system and method operating with publish-subscribe pattern messaging. A message broker controls internal clients and also manages communication with external clients. To enable communication with the message broker, the external clients use authentication tokens obtained from a trusted authorization server. On receipt of a connect message from an external clients, the message broker extracts and stores relevant information from the token. When connected external clients subsequently publish messages to the message broker, they do so without resending the token, since the message broker has stored the token information itself or in its internal clients. The message broker can then pass on the messages to its internal clients who subscribe to messages of that topic.