An analysis system determines a system aspect of a system, determines an evaluation perspective for use in performing an asset management evaluation on the system aspect relating to a build of the system, an evaluation viewpoint corresponding to discovered information of the system and selects a plurality of data structures identifying data to be collected based thereupon. The analysis system, based upon the system aspect, the evaluation perspective, the evaluation viewpoint, and the plurality of data structures, determining context data. Based upon the plurality of data structures, the analysis system identifies a plurality of physical assets of the system for collection of data, queries the plurality of physical assets of the system to collect data to populate the plurality of data structures. The analysis system evaluates the data structures using the context data to produce an evaluation of at least some of the plurality of physical assets of the system.

A computer-implemented method for evaluating team effectiveness and acting on one or more outliers based on the evaluated team effectiveness. The method retrieves electronic data of one or more users, wherein the electronic data comprises a plurality of electronic communications. The method further extracts one or more concepts and metadata from the retrieved electronic data. The method further determines one or more outliers based on the extracted one or more concepts and metadata, wherein the determined one or more outliers are based on one or more poorly connected concepts and one or more poorly connected users. The method further acts on the determined one or more outliers and displays a report based on the determined one or more outliers.

An initial sequence representing a partially configured engineering project is processed by a recurrent neural network to generate recommendations being a sequence of complementary items that completes an engineering project. A feature predictor component computes a set of features for each recommendation. A bisection component selects a feature from the sets of features that distinguishes some of the recommendations and forms pruned recommendations by choosing all instances from the recommendations that have the selected feature. A user interface displays the selected feature, detects a user interaction indicating that the selected feature is required, outputs the pruned recommendations. The engineering project is completed by combining the initial sequence with the chosen pruned recommendation. As a result, a user is supported in choosing optimal modules, as the selected feature can distinguish the recommendations that have the desired technical properties or target system KPI.

An Environmental, Social, and Governance (ESG) rating is provided for an entity with multiple assets. A plurality of metrics are defined. A value for each metric for each asset is obtained if available, or is set to 0. Similarly, a value for each metric for a benchmark is obtained if available, or is set to 0. For each metric and for each asset, a weight is calculated as a difference between the corresponding value of such metric for the asset and the corresponding value of such metric for the benchmark. For each metric, the weights thereof are combined to produce a composite weight across all assets. For each composite weight, a point value is assigned thereto based on a corresponding risk model. The point values are aggregated, and the aggregate is adjusted based on a perceived risk for the entity to produce the ESG rating.

An infrastructure manager for placing workloads for performance across on-demand infrastructure and dedicated infrastructure includes a storage device for storing a performance metrics repository and a processor. The processor identifies a performance metric change event using the performance metrics repository; in response to identifying the performance metric change event: identifies a placed workload of the workloads that is impacted by the performance metric change event; identifies a new placement for the placed workload; makes a determination that a current placement of the placed workload is different from the new placement; and in response to the determination: updates placement of the workload based on the new workload placement.

An automated, multi-stage decommissioning method for business intelligence artifacts may comprise generating a classification for a business intelligence artifact in a business intelligence environment; determining, based on the classification, whether the business intelligence artifact is a target artifact to be decommissioned; and if the business intelligence artifact is determined to be a target artifact, then initiating a decommissioning process on the target artifact.

A system employing new and improved artificially intelligent system for employing modularized and taxonomy-based classifications to generate compliance-related content. In one embodiment, the system comprises monitoring circuitry that receives regulatory compliance data from one or more regulatory institutions, as well as a taxonomy engine that processes the regulatory compliance data to generate taxonomy-based classifications of the regulatory compliance data comprising a plurality of modules and compliance requirements within each module. In certain embodiments, the system also includes a database storing the taxonomy-based classifications of the regulatory compliance data, and a plurality of processors in operative communication with the database that receive at least two of the plurality of modules from the taxonomy-based classifications and process the compliance requirements within each received module using natural language processing to generate a mapping of semantic relationship pairs between each received module. In certain embodiments, the system also includes scoring circuitry that processes the mapping of semantic relationship pairs between each received module to produce a similarity score for each relationship pair, as well as interface circuitry that uses the similarity scores to generate a set of compliance steps that covers all compliance requirements from each of the received modules.

Examples provide analyzing motion data. Worker specific sensor data is received and both a task and at least one worker movement based on the received worker specific sensor data are identified. At least one task threshold parameter associated with the identified task and ergometric data for a worker related to the identified task is retrieved and at least one worker specific task parameter based on the received worker specific sensor data is generated. The at least one task threshold parameter is compared with the at least one worker specific task parameter and the at least one worker movement is compared with the ergometric data for the worker related to the identified task. Worker specific task data is generated based on the comparison and a change for future worker movement is identified based on the comparison.

Methods, apparatus, and processor-readable storage media for processing order experience data across multiple data structures are provided herein. An example computer-implemented method includes processing data, obtained from a first set of data structures, pertaining to orders placed with an enterprise, wherein the first set of data structures contains data associated with distinct portions of order transactions; extracting information pertaining to pre-defined attributes from the processed data and processing the extracted information into a second set of data structures; calculating order experience scores for the orders by applying at least one algorithm to the extracted information in the second set of data structures; generating at least one benchmark order experience value, wherein each benchmark order experience value is based at least in part on the calculated order experience scores; and performing operations related to order experience within the enterprise based at least in part on the benchmark order experience values.

A virtual marketplace may offer end users the ability to acquire articles (including tools and metadata objects) that are compatible with an enterprise operating system platform. The virtual marketplace may determine one or more articles that are implemented (or installed) on the enterprise operating system platform. The enterprise operating system platform may be provided to the end user by an operator of the marketplace. The articles including articles that may be used for evaluating a performance of an entity. The virtual marketplace may further ascertain data that are processed or generated by the articles that are implemented on the enterprise operating system platform. The virtual marketplace may additionally determine functionalities that are used or provided by the articles. Accordingly, the virtual marketplace may provide a recommendation of associated articles from the virtual marketplace that have at least one dependency relationship with the articles.