Comparing recurring processes. A method includes automatically identifying a plurality of recurring process instances having one or more commonalities. The method further includes displaying the plurality of recurring process instances having one or more commonalities to a user in a user interface. The method further includes receiving user input at the user interface selecting a first recurring process instance from the plurality of recurring process instances. The method further includes receiving user input at the user interface selecting a second recurring process instance from the plurality of recurring process instances. The method further includes automatically identifying differences in the first and second recurring process instances. The method further includes presenting the differences in the graphical user interface to the user.

The technology disclosed relates to systems and methods usable for developing and testing dashboards with production data in non-production environments including a development server with APIs interposed between a development app and a production server, providing development access to production data before deployment into the production environment. The disclosed development server includes an app server that serves a development app in response to a request from a developer device; and exposes development APIs that mirror production APIs of the production server, and relay of messages between the development APIs and the production APIs. The development server replicates access to production server APIs relied on to retrieve and process time series data from a data store, the replication including access to development server APIs that mirror relative addressing of production APIs. Users can select among reviewed and approved dashboards, and can select to deploy the selected dashboards to the production server.

A fail-safe visual indicator (62) for indicating the readiness status of a medical device is described. The visual indicator incorporates iconic status indications (67, 70) which are easier to understand for users of all nationalities and languages. The status indications are superimposed upon each other in order to reduce the space required on the medical device for the indicator.

Examples provide a system and method for retraining a machine learning (ML) algorithm associated with a trained model using root cause pattern recognition. The system analyzes the results of parsing unstructured data and identifies a root cause pattern causing the trained model to underperform when parsing data including the identified pattern. Examples of data including the pattern are created for use in retraining the model to correctly detect and parse data following the identified pattern. Once retrained, the model is able to parse unstructured data, including data having the identified pattern, in accordance with expected performance metrics. The system automatically identifies parsing errors, identifies the root cause patterns for these errors and retrains the models to correctly handle those patterns for more accurate and efficient handing of unstructured data by trained models.

An apparatus comprises at least one processing device that is configured to implement a data collection agent and obtain a telemetry data collection specification. The at least one processing device is configured to train the data collection agent based at least in part on the telemetry data collection specification and to obtain an issue description corresponding to a technical support issue associated with a source device of an information processing system. The at least one processing device is configured to generate a telemetry data collection query based at least in part on the obtained issue description using the trained data collection agent and to submit the query to the source device. The at least one processing device is configured to obtain from the source device, telemetry data generated based at least in part on the query and to present the telemetry data to a user via a graphical user interface.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for monitoring performance of computing systems. In some implementations, a set of tasks for a server system to perform is identified. Multiple performance testing cycles are performed, in which each of the performance testing cycles includes: sending, for each task in the set of tasks, a request for the server system to perform the task; receiving a response from the server system for each of the requests, and storing a performance measure for each of the tasks based on the response received from the server system for the task. Based on the performance measures for the multiple performance testing cycles, an evaluation is performed whether conditions are satisfied for adjusting one or more operating parameters of the server system or for providing a notification regarding the operation of the server system.

A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (10, 12, 14) to perform a log pattern analysis method (300) by applying a log pattern to log data undergoing analysis comprising log events which are time stamped and which are defined by values for a set of fields. The log data undergoing analysis are stored on a server (30) accessible by the at least one electronic processor via an electronic network (31). The log pattern analysis method includes: constructing a retrieval query to select log events that are in a time interval and that also satisfy at least one additional constraint associated with the log pattern and defined on one or more filter fields of the set of fields; downloading a sub-set of the log data undergoing analysis from the server to the at least one electronic processor via the electronic network, wherein the sub-set is selected from the log data undergoing analysis by the retrieval query; applying the log pattern to the downloaded sub-set of the log data undergoing analysis to identify log pattern hits in the log data undergoing analysis; and controlling a display screen (16) to display a report of the log pattern hits in the log data undergoing analysis.

A system for generating a model health score includes a memory storing computer-executable instructions and a processor configured to execute the computer-executable instructions to cause the system to perform selecting a model of a plurality of models to analyze, selecting a prediction from a plurality of predictions where the selected model was executed, and determining a model health score for the selected model. The selected model includes a plurality of variables and the model health score is based on at least a subset of the plurality of variables. The model health score indicates viability of the selected model for the selected prediction.

Provided is a knowledge base information awareness method and system based on operation and maintenance of a data center. The method includes that: a data collector collects basic resource information of the data center; a data analyzer analyzes the basic resource information, and determines whether information of an information node of the data center is updated to obtain a determination result and an update parameter; a context retriever establishes an index for context information stored in a knowledge base, and performs retrieval; a monitor monitors updates of the context information and the information node; when the monitor monitors that the context information and/or the information node is updated, the monitor performs an adaptive management through an operation and maintenance related body, and triggers a problem manager; the problem manager sends a processing scheme corresponding to a fault identification result to a uniform service management software platform for performing.

Techniques for an optimization service of a service provider network to generate an architecture diagram that represents an architecture of a web-based application. The optimization service may use the architecture diagram to determine modifications or changes to make to the application. For example, the optimization service may compare the architecture diagram with optimized architecture diagrams that represent application best practices, and determine the modifications or change to make to the application to optimize the application and bring the application in-line with best practices. Further, the optimization service may use the architecture diagram to generate a visualization, and provide the user account with the visualization of the architecture diagram to show users their application architecture.