Abstract: Techniques are provided for machine learning-based anomaly detection for repetitive tasks performed using edge instruments. One method includes obtaining sensor data characterizing an orientation and/or an acceleration of an edge instrument utilized to perform a repetitive task, comprising a sequence of actions, by a user. The sensor data is obtained from at least one sensor embedded in the edge instrument. The sensor data is applied to a machine learning model trained to identify a deviation from an expected sequence of actions associated with the repetitive task. The machine learning model is embedded in the edge instrument. An automated action is initiated in response to the machine learning model identifying the deviation from the expected sequence of actions.

Abstract: Methods, apparatus, and processor-readable storage media for data archival recommendation systems using artificial intelligence techniques are provided herein. An example computer-implemented method includes processing data associated with database operations performed across multiple database storage zones in one or more databases; generating, using one or more artificial intelligence techniques and based at least in part on the processing of data associated with the database operations performed across the multiple database storage zones, one or more temporal-based data archival recommendations for at least a portion of the multiple database storage zones in the one or more databases; and performing one or more automated actions based at least in part on the one or more temporal-based data archival recommendations.

Abstract: Methods, apparatus, and processor-readable storage media for augmenting identifying metadata related to group communication session participants using artificial intelligence techniques are provided herein.

Abstract: Techniques are disclosed for hyper-segmented personalization using machine learning-based models in an information processing system. For example, a method obtains one or more product experience recommendation data sets respectively from one or more product entities, and one or more purchase experience recommendation data sets respectively from one or more commerce entities. The method applies a federated ensemble-based machine learning algorithm to at least one of the one or more purchase experience recommendation data sets and at least one of the one or more product experience recommendation data sets to generate a personalized model, and causes adaptation of a purchasing interface of at least one of the one or more commerce entities with respect to a given user based on the personalized model.

Abstract: Methods, apparatus, and processor-readable storage media for identifying vulnerabilities across software code repositories are provided herein. An example computer-implemented method includes maintaining at least one database associated with a plurality of code repositories; in response to detecting a build process associated with a first code repository of the plurality of code repositories, extracting and storing metadata related to the first code repository in the at least one database; identifying at least one vulnerability associated with the first code repository of the plurality of code repositories; determining whether an additional code repository of the plurality of code repositories is impacted by the at least one vulnerability based at least in part on the metadata stored in the at least one database for the additional code repository; and initiating one or more automated actions to at least partially remediate the at least one vulnerability in the additional code repository.

Abstract: Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand using artificial intelligence techniques are provided herein.

Abstract: A method comprises receiving a request to combine data from a plurality of data collections based on one or more constraints, and analyzing the request using one or more machine learning algorithms to determine an order by which respective ones of the plurality of data collections are to be searched. The respective ones of the plurality of data collections are searched according to the determined order, and an output of combined data from the searching is generated in response to the request.

Abstract: Techniques are provided for managing an end-to-end software development life cycle. A software development lifecycle management system performs a plurality of stages of work for developing a software application, and records transactions which result from performing the plurality of stages of work, in an immutable digital ledger. The immutable digital ledger can be a blockchain ledger.

Abstract: An apparatus comprises a processing device configured to perform natural language processing of a first set of documents to generate a first data structure and a second set of documents to generate a second data structure, the first set of documents characterizing information associated with a user of an organization and the second set of documents characterizing information associated with the organization. The processing device is further configured to process the first data structure and the second data structure utilizing one or more machine learning models to generate a third data structure characterizing resources of an information technology infrastructure to be allocated for use by the user of the organization, and to generate one or more control signals for allocating one or more resources of the information technology infrastructure for use by the user of the organization based at least in part on the third data structure.

Abstract: A system comprises at least one camera disposed in an environment, and at least one computing node which hosts and executes an energy management system. The energy management system is configured to utilize computer vision processing of the images of the environment to extract information from the images, to utilize the extracted information from the images to determine usage patterns of light sources in the environment, and to generate intelligent recommendations for automated control of the light sources in the environment based on the determined usage patterns, to conserve energy consumption from use of the light sources in the environment.

Abstract: A method comprises reading data of one or more files and identifying one or more data types corresponding to the one or more files. In the method, at least one destination database for the data is recommended based at least in part on the one or more data types. A data model to use in connection with writing the data to the at least one destination database is generated. The generation of the data model is based at least in part on the one or more data types corresponding to the one or more files. At least the generation of the data model is performed using one or more machine learning algorithms.

Abstract: Techniques for managing critical workloads in container-based computing environments are disclosed. In one example, a method determines a resource trigger threshold associated with executing at least one containerized workload associated with a first service having a first criticality level, the resource trigger threshold corresponding to a resource capacity allocated to execute the first service. The method determines when the resource capacity allocated to execute the first service reaches the resource trigger threshold, and then re-allocates resource capacity allocated to execute at least one containerized workload associated with a second service having a second criticality level to the first service when the resource trigger threshold is reached. For example, the first criticality level may be higher than the second criticality level.

Abstract: A method comprises receiving a command inputted to a first device, analyzing the command and identifying a second device based at least in part on the analysis. In the method, the second device is authenticated and a connection is established between the first device and the second device over one or more networks. The command is for execution of a task on the second device.

Abstract: Computing devices with dual computing architectures for use in a subscription model are disclosed. For example, a computing device comprises a first computing architecture comprising a first set of computing resources dedicated to executing one or more first computing tasks, wherein the one or more first computing tasks are associated with a subscription-based user of the computing device. The computing device further comprises a second computing architecture comprising a second set of computing resources dedicated to executing one or more second computing tasks, wherein the one or more second computing tasks are associated with a subscription-based provider of the computing device.

Abstract: A method comprises receiving a request to combine data from a plurality of data collections based on one or more constraints, and analyzing the request using one or more machine learning algorithms to determine an order by which respective ones of the plurality of data collections are to be searched. The respective ones of the plurality of data collections are searched according to the determined order, and an output of combined data from the searching is generated in response to the request.

Abstract: A method comprises reading data of one or more files and identifying one or more data types corresponding to the one or more files. In the method, at least one destination database for the data is recommended based at least in part on the one or more data types. A data model to use in connection with writing the data to the at least one destination database is generated. The generation of the data model is based at least in part on the one or more data types corresponding to the one or more files. At least the generation of the data model is performed using one or more machine learning algorithms.

Abstract: A method comprises reading data from a source database, identifying one or more data types corresponding to the source database, identifying a destination database for the data, and identifying one or more data types corresponding to the destination database. In the method, a destination database model to use in connection with writing the data in the destination database is generated. The generation of the destination database model is based at least in part on the one or more data types corresponding to the destination database, and is performed using one or more machine learning algorithms.

Abstract: Methods, apparatus, and processor-readable storage media for augmenting identifying metadata related to group communication session participants using artificial intelligence techniques are provided herein.

Abstract: Techniques are disclosed for a multi-layer micro model analytics framework for analyzing or otherwise processing data. For example, a method comprises building two or more micro models respectively for two or more stages of a given process, wherein each micro model of the two or more micro models comprises a user interaction layer and a predictive learning layer that coordinate to train the micro model based on input to the user interaction layer and data accessible by the predictive learning layer for the corresponding stage of the two or more stages of the given process. The method then assembles the two or more micro models to perform analysis for the given process. In one non-limiting example, the given process is a new product introduction process and each micro model is built and trained to perform analytics for a specific lifecycle stage of the process.

Abstract: Methods, apparatus, and processor-readable storage media for data archival recommendation systems using artificial intelligence techniques are provided herein. An example computer-implemented method includes processing data associated with database operations performed across multiple database storage zones in one or more databases; generating, using one or more artificial intelligence techniques and based at least in part on the processing of data associated with the database operations performed across the multiple database storage zones, one or more temporal-based data archival recommendations for at least a portion of the multiple database storage zones in the one or more databases; and performing one or more automated actions based at least in part on the one or more temporal-based data archival recommendations.