A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: enabling a customer to identify a customer metric, the customer metric representing an aspect of a data center infrastructure configuration of importance to the customer; analyzing the customer metric, the analyzing comprising at least one of prioritizing and weighting the customer metric; and, generating a recommendation for a data center infrastructure configuration based upon the analyzing.

Systems and methods for setting correspondence between pieces of equipment, locations and elements of a lockout procedure checklist comprising a procedure checklist and marking tools. The procedure checklist is printed over a markable support and comprises elements printed thereon each comprising a unique graphical assemblage. Marking tools, provided at locations to perform securing operations on pieces of equipment, are adapted to generate markings on the support matching the graphical assemblages. The operator following each of the elements of the procedure uses the marking tools at the locations to generate markings on the support, and thereby completion of the procedure may be validated by comparing the markings generated on the support with the graphical assemblages thereon. Procedure allows to validate that operations are performed only on appropriate pieces of equipment and location, and that all steps are performed.

A system and associated method provides a visualization of a life cycle of a trade order. The visualization may be in the form of a tree structure with a plurality of linked nodes. Each node may be associated with an event that occurs during the life cycle of the trade. A monitoring system receives information associated with a plurality of events associated with the trade order, generates a plurality of nodes based on the received information, stores identifiers associated with each of the plurality of nodes, and links the plurality of nodes based on the identifiers to create the tree structure. The monitoring system also performs a verification process to determine whether the visualization is missing information or includes incorrect information and alerts to a discrepancy identified during the verification process.

Techniques are described for cognitive defined network management (CDNM) that seek to perform real-time collection and analysis of raw network data from across a disaggregated wireless network and to dynamically orchestrate network management functions substantially in real time, accordingly. For example, a multi-modal artificial intelligence (AI) engine is trained to normalize the heterogeneous raw network data into homogeneous so-called “golden record data.” A repository of historical golden records can be maintained for generating data models for use in training AI network management applications. An orchestrator can operate to directing execution of pre-developed network management workflows based on results obtained from querying the trained AI network management applications with newly received (real-time) golden records.

Systems and methods for generating status requests for units of work are disclosed. Some implementations may: manage environment state information maintaining a collaboration environment; identify one or more work unit records having potentially incorrect status values for a status parameter; generate status requests prompting user input to update the potentially incorrect status values for the one or more work unit records identified; and effectuate presentation of the status requests, such that the first status request is presented via a client computing platform associated with the first user.

Information defining a plurality of states, a plurality of transitions, an initial state, and a final state is received from a user. The user may also provide additional information including pre-conditions and post-conditions for one or more transitions. Context information including one or more context variables and context variable values is generated based on the information provided by the user. A first plurality of possible paths between the initial state and the final state is automatically identified, wherein each path traverses at least one state and at least one transition. A second plurality of paths is identified from among the plurality of paths, based on the context information and the pre-conditions defined by the user. A Q-value is determined for each path in the second plurality of paths, using the rewards. A path having a highest Q-value is selected and presented to the user as a BPM. An acceptance or rejection of the proposed BPM is received from the user. Reward values associated with transitions in the selected path are updated, if the user accepts the proposed BPM.

Systems and methods described herein provide an automated workflow tool that automatically and efficiently creates an executable workflow while preventing or reducing the introduction of errors into the workflow. The tool receives a selection of a start tile. In response, the tool presents a number of selectable items to configure an action to detect that initiates execution of the start tile as the first step in the workflow. Responsive to saving a configuration of the start tile including the action, the tool determines that the configuration of the start tile instructs the tool to add a second tile connected to the start tile on the canvas of the workflow. The tool presents a number of selectable items to configure the second tile as a next step in the workflow. The tool executes the workflow configured with the start tile and the second tile responsive to detecting the action configured in the start tile.

Systems and methods described herein provide an automated workflow tool that automatically and efficiently creates an executable workflow while preventing or reducing the introduction of errors into the workflow. The tool receives a selection of a start tile. In response, the tool presents a number of selectable items to configure an action to detect that initiates execution of the start tile as the first step in the workflow. Responsive to saving a configuration of the start tile including the action, the tool determines that the configuration of the start tile instructs the tool to add a second tile connected to the start tile on the canvas of the workflow. The tool presents a number of selectable items to configure the second tile as a next step in the workflow. The tool executes the workflow configured with the start tile and the second tile responsive to detecting the action configured in the start tile.

A dismantling procedure selecting apparatus includes a dismantling information storage unit that stores a plurality of pieces of dismantling information respectively including a plurality of predetermined dismantling procedures for a plurality of dismantled objects each used as a reference for dismantling an object to be dismantled, a whole detector that captures a whole image of the object to be dismantled, a detail detector that captures an image of at least a portion of the object to be dismantled, and a dismantling procedure deriving unit that extracts a first feature as a feature of the object to be dismantled based on data obtained by capturing an image by at least one of the whole detector and the detail detector, obtains a degree of matching between the first feature and each of a plurality of second features that are respectively features of a plurality of dismantled objects, and selects a dismantling procedure associated with one of a plurality of the dismantled objects having a second feature having a highest degree of matching among a plurality of second features.

A process mining system performs process mining using visual logs generated from video streams of worker devices. Specifically, for a given worker device, the process mining system obtains a series of images capturing a screen of a worker device while the worker device processes one or more tasks related to an operation process. The process mining system determines activity labels for a plurality of images. An activity label for an image may indicate an activity performed on the worker device when the image was captured. The activity label is determined by extracting information from pixels of the image and inferring the activity of the worker device from the extracted information. The process mining system generates event logs from the visual logs of worker devices and uses the event logs for process mining.