Techniques for implementing user interfaces, systems, and processes for multidimensional clustering and analysis are described herein. In one aspect, an application or cloud service receives a request to cluster a set of records where the request identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns. Responsive to receiving the request to cluster the set of records, the system generates clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records. The system may generate, for each respective cluster, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster. The system may present the clusters and analytic results for further processing.

Methods, systems, and computer program products are provided for creating a resource management testing environment. An initial population of databases is established in a database ring, having an in initial count of databases and different types of databases that are determined based on an initial database population model. The initial population model receives ring classification information for the database ring from a ring grouping model. A sequence of database population-change events is generated based on a model, to change the population of the databases over time in the ring. An orchestration framework performs testing of resource manager operations based on the model-defined initial population of databases and the model-defined populations of databases changed over time. Model-defined resource usage metrics for each database are utilized to test the resource manager operations. Resource usage metrics and database add/drop events of a production system are used to train the models.

A method for updating firmware includes receiving, at a device, an updated installation package. The updated installation package includes an updated version of an installation package, which belongs to a set of installation packages stored on the device for installation of firmware on the device. The method further includes updating the set of installation packages by replacing the installation package with the updated installation package. The method further includes installing updated firmware in volatile memory of the device based on the updated set of installation packages. The method further includes storing an image of the updated firmware in nonvolatile storage of the device. Additionally, the method includes, during a boot process, loading the image from the nonvolatile memory of the device onto the volatile memory of the device, to enable running the updated firmware from the volatile memory, and verifying the authenticity of the updated firmware.

An example method comprises: causing display of a user interface comprising a plurality of dynamic elements, the user interface to facilitate configuring a search frequency for metrics associated with the plurality of dynamic elements, wherein each metric represents a respective point in time or a period of time and is derived from a metric-time search of machine data associated with a respective asset node; and for each dynamic element of the plurality of dynamic elements: receiving, via the user interface, a search frequency for a metric associated with the dynamic element; and determining a value of the metric by executing, according to the search frequency for the metric, a search query associated with the dynamic element.

A computer-implemented method for access path optimization is provided according to embodiments of the present disclosure. In the method, a plurality of real values of an access path factor may be collected during a specified time period. One of the real values may be generated when a query is executed on a first access path. Then, at least one second access path may be generated for the query based on the plurality of real values of the access path factor. Moreover, an optimal access path for the query may be identified from the first access path and the at least one second access path.

Implementations described herein relate to systems and methods to configure a data warehouse system. In some implementations, a method includes obtaining, by a configuration management system, historical query workload metadata associated with a data warehouse from the data warehouse system, determining, a first configuration setting associated with a configurable parameter for a first time period, wherein the first configuration setting is associated with a computing resource utilization at the data warehouse system different from a previous configuration setting, transmitting, to the data warehouse system, the first configuration setting for the configurable parameter, receiving, from the data warehouse system, during the first time period, query workload metadata, determining, whether the query workload metadata meets a threshold performance, and based on a determination that the query workload metadata does not meet the threshold performance, transmitting a backoff configuration setting for the configurable parameter to the data warehouse system.

Disclosed is a method for estimating database management system performance, in which a performance change ratio of a DBMS can be determined once a first knob group, a second knob group, and a data volume of active data in data managed by the DBMS are obtained, without actually configuring the second knob group in the DBMS, executing a job by the DBMS, and then observing the execution. In other words, the performance change ratio of the DBMS can be estimated without interacting with the DBMS. DBMS security can be ensured, performance measurement approaches are provided for self-tuning and self-management of the DBMS, and reliable and stable running of the DBMS is ensured.

A Generative Adversarial Network is used to train and/or tune a model used to analyze data in a database or data stream. The Generative Adversarial Network intermittently trains or tunes the model as the database is actively ingesting data and/or while the data stream is streaming. This intermittent refreshing of the model, performed by the Generative Adversarial Network, is sometimes described as “dynamic” or “dynamical.” Analytics type software is queried in order to perform normalization and/or model training.

A method, system and computer program product for providing support for identification of the problem root cause in a computing system. Knowledge base mapping monitoring programs with respective to one or more technical problem definitions and a predefined system configuration are provided. The technical problems are defined using a predefined data structure. An inquiry relating to the computing system is received from a requester. The inquiry indicates a technical problem statement of the computing system. The received inquiry is parsed for constructing a problem description in accordance with the predefined data structure. A set of monitoring programs of the knowledge base may be identified using the problem description. Using software and/or hardware configuration data of the computing system, the set of monitoring programs may be instantiated in a monitor system. The instantiated programs may be provided to the requester as support for identification of the problem root cause.

Methods and apparatus are provided for using machine learning to estimate query resource consumption in a massively parallel processing database (MPPDB). In various embodiments, the machine learning may jointly perform query resource consumption estimation for a query and resource extreme events detection together, utilize an adaptive kernel that is configured to learn most optimal similarity relation metric for data from each system settings, and utilize multi-level stacking technology configured to leverage outputs of diverse base classifier models. Advantages and benefits of the disclosed embodiments include providing faster and more reliable system performance and avoiding resource issues such as out of memory (OOM) occurrences.