A graph model of a graphical user interface (GUI) may be generated by processing usage data of the GUI where the usage data comprises sequences of GUI pages and actions between GUI pages. The nodes of the graph model may be determined by obtaining GUI pages from the usage data, identifying dynamic GUI elements in the GUI pages, generating canonical GUI pages by modifying the GUI pages using the dynamic GUI elements, and creating graph nodes using the canonical GUI pages. The edges of the graph may be determined by processing actions from the GUI data that were performed by users to transition from one GUI page to another GUI page. The graph model of the GUI may be used for any appropriate application, such as determining statistics relating to the GUI or statistics relating to individual users of the GUI.

A system and process extract software application performance data from disparate ownership sources and make the various source data compatible for comparison data. A software application's performance in the marketplace may be compared to other applications in a same group with comparable data information. A M2M (mobile-to-mobile) technology is an interface layer connection to a backend server that builds machine learning pipelines and may use artificial intelligence to turn massive datasets into identifiable patterns, algorithms and statistical models. This layer is capable of cleaning, aggregating, and organizing data from disparate sources to produce meaningful conclusions to complex problems to inform strategic business decisions.

An instrumentation analysis system processes data streams by executing instructions specified using a data stream language program. The data stream language allows users to specify a search condition using a find block for identifying the set of data streams processed by the data stream language program. The set of identified data streams may change dynamically. The data stream language allows users to group data streams into sets of data streams based on distinct values of one or more metadata attributes associated with the input data streams. The data stream language allows users to specify a threshold block for determining whether data values of input data streams are outside boundaries specified using low/high thresholds. The elements of the set of data streams input to the threshold block can dynamically change. The low/high threshold values can be specified as data streams and can dynamically change.

State of the art predictive maintenance systems that generate predictions with respect to maintenance of High Performance Computing (HPC) systems have the disadvantage that they either are reactive, or the predictions are affected due to quality issues associated with the data being collected from the HPC systems. The disclosure herein generally relates to predictive maintenance, and, more particularly, to a method and system for predictive maintenance of High Performance Computing (HPC) systems. The system performs abstraction and cleansing on performance data collected from the HPC systems, and generates a cleansed performance data, on which a Machine Leaning (ML) prediction is applied to generate predictions with respect to maintenance of the HPC systems.

Comparing the performance of multiple versions or branches/paths of an application (e.g., a web service or application) may be conducted within a suitable computing environment. Such an environment may be virtual in nature, cloud-based, or server-based, and is hosted with tools for simultaneously (or nearly simultaneously) executing multiple containers or other code collections with the same or similar operating conditions (e.g., network congestion, resource contention, memory management schemes). By arranging the performance test of different application versions in different sequences executed in parallel in separate containers, fair comparisons of the tested applications will be obtained. Testing sequences may be executed multiple times, and metrics are collected during each execution. Afterward, the results for each metric for each code version are aggregated and displayed to indicate their relative performance quantitatively and/or qualitatively.

It is possible to reduce analysis cost of a management system.

The management system includes a CPU and manages one or more storage devices that provide, to a higher-level device, one or more volumes for inputting and outputting data. The CPU is configured to collect performance information of the volume from the storage device at a predetermined first time interval and detect a QoS violation of the performance information of the volume at a second time interval longer than the first time interval.

An example system includes a processor to receive mouse dynamics data of a session to be analyzed and a uniform resource locator (URL) category mapping. The processor can group the mouse dynamics data into a plurality of groups using the URL category mapping. The processor can separately extract features from each of the plurality of groups to generate a plurality of groups of features for the session. The processor can input the groups of features into a trained classification model. The processor can receive an output score from the trained classification model.

A method and system for detecting anomaly transition point candidates in performance metadata. The method can be applied to computer system performance monitoring. Anomaly candidates, indicative of a possible transition, of a process generating the performance metadata, to or from an anomalous behavior mode are identified, for example by comparing z-scores to the left and right of various timestamps and identifying anomaly candidates when the z-scores are significantly different. Anomaly candidates occur singularly rather than as pairs of endpoints of an anomaly interval. For at least one of the anomaly candidates, an explanatory predicate, indicative of a human-readable explanation of behavior of the process, can be generated. The set of anomalies can then be filtered, for example by removing those without explanatory predicates or replacing clusters of anomalies with a most relevant anomaly.

Methods that provide dynamic index management for a set of computing storage resources are disclosed herein. One method includes collecting, by a processor, a set of current performance data for a set of storage resources storing data and implementing a set of indexes for the data stored on the set of storage resources based on an optimized performance predicted for the set of storage resources based on the collected set of current performance data and a set of predicted performance data that identifies the set of indexes. Also disclosed herein are apparatus, systems, and computer program products that can include, perform, and/or implement the methods for providing dynamic index management for a set of computing storage resources.

Each of one or more commit cycles may be associated with a predicted number of updates. A statistics collection time for a database table can be determined by estimating a sum of predicted updates included in one or more commit cycles. Whether the estimated sum of predicted updates is greater than a first threshold may be determined. In addition, a progress point for a first one of the commit cycles can be determined. A time to collect statistics may be selected based on the progress point of the first commit cycle.