Abstract: A computer-implemented method and system can be used to test a code modification for a microservice application. The code modification is analyzed using a machine learning model trained on historical test run results and code change data, Based on the analysis, a subset of test cases relevant to the code modification are predicted and selected from a test case repository. The selected subset of test cases can be executed to test the code modification. If the test cases are stored in natural language, natural language processing can be used to determine actionable words and assign weightages from the test case information. Test scripts can be developed based on the determined actionable words and assigned weightages.

Abstract: In certain embodiments, a method includes receiving, at a framework controller executing, a test execution request comprising test parameters and an expected test outcome, wherein the test execution request comprises a request for testing a microservice architecture implemented in a cloud infrastructure; providing, by the framework controller, the test execution request and the test parameters to a framework injector executing on the one or more processors; providing, by the framework controller, the expected test outcome to a framework monitor executing on the one or more processors; executing, by the framework injector, a test corresponding to the test execution request using a test injector of a plurality of test injectors implemented by the framework injector; monitoring, by the framework monitor, a response of the microservice architecture of the cloud infrastructure to the test; and providing, by the framework controller and from the framework monitor, a test result based on the response.

Abstract: A method performed by a cloud system includes, subsequent to the cloud system connecting to one of a cloud provider and a Software-as-a-Service (SaaS) application, scanning data stored therein for one or more users associated with a tenant of a plurality of tenants of the cloud system; detecting an incident in the data during the scanning; maintaining details of the incident in an in-memory data store; and providing a notification to the tenant of the incident.

Abstract: A system and methods are described for retrieving, displaying, and/or analyzing law firm analytic data in response to natural language queries. The system, at an interface or bot, receives queries as plaintext messages requesting information about or maintained by a law firm. The system generates intent scores for the message for each of multiple intents and determines an intent for the message based on a highest generated score exceeding a threshold. The system extracts entities from the message, applies an algorithm to the determined intent and extracted entities, and identifies a data source from which to retrieve information. The system then retrieves and displays information from the identified data source.

Abstract: A method performed by a cloud system includes, subsequent to the cloud system connecting to one of a cloud provider and a Software-as-a-Service (SaaS) application, scanning data stored therein for one or more users associated with a tenant of a plurality of tenants of the cloud system; detecting an incident in the data during the scanning; maintaining details of the incident in an in-memory data store; and providing a notification to the tenant of the incident.

Abstract: A method performed by a Cloud Access Security Broker (CASB) service includes scanning data stored in one of a cloud provider and a Software-as-a-Service (SaaS) application, wherein the data is for a user associated with a company of a plurality of companies; detecting an incident in a file or email in the data during the scanning; maintaining details of the incident in an in-memory data store, including a current snapshot of the file or email; and providing a notification to the tenant of the incident. The method can further include, subsequent to the incident and while the file or email is being updated, updating the details of the incident in the in-memory data store.

Abstract: The detection of utilized virtual machines through usage pattern analysis is described. In one example, a computing device can collect utilization metrics from a virtual machine over time. The utilization metrics can be related to one or more processing usage, disk usage, network usage, and memory usage metrics, among others. The utilization metrics can be used to determine a number of clusters, and the clusters can be used to organize the utilization metrics into groups. Depending upon the number or overall percentage of the utilization metrics assigned to individual ones of the plurality of clusters, it is possible to determine whether or not the virtual machine is a utilized or an idle virtual machine. Once identified, utilized virtual machines can be migrated in some cases. Idle virtual machines can be shut down to conserve processing resources and costs in some cases.

Abstract: The present invention includes a method of predicting and/or treating a recurrence of MS treating a patient with multiple sclerosis, the method comprising: obtaining a hematopoietic cell sample from a patient suspected of having a recurrence of multiple sclerosis (MS), wherein the patient was in relapse for MS; determining the number of CD19+, CD24+, CD38+ transitional B cells in the hematopoietic cell sample, and a level of expression of neurofilament light (NFL) and interleukin-1? (IL-1?), which is predictive of a recurrence of MS; and treating the MS patient with recurrence until there is an increase in CD19+, CD24+, CD38+ transitional B cells and/or a decrease in a level of expression of NFL and interleukin-1? (IL-1?) when compared to an untreated MS control sample, an unresponsive MS control sample, or an MS patient with long-term stable disease sample.

Abstract: A method performed by a Cloud Access Security Broker (CASB) service includes scanning data stored in one of a cloud provider and a Software-as-a-Service (SaaS) application, wherein the data is for a user associated with a company of a plurality of companies; detecting an incident in a file or email in the data during the scanning; maintaining details of the incident in an in-memory data store, including a current snapshot of the file or email; and providing a notification to the tenant of the incident. The method can further include, subsequent to the incident and while the file or email is being updated, updating the details of the incident in the in-memory data store.

Abstract: Methods and systems predict parameters in a dataset of an identified piece of (“information technology”) IT equipment. An automated method identifies datasets IT equipment in a same category of IT equipment as a piece of IT equipment identified as having incomplete dataset information. Each dataset of IT equipment parameters is used to construct generalized linear models of different classes of IT equipment within the category of IT equipment. The class of the identified IT equipment is determined. A predicted equipment parameter of incomplete information of the identified piece of IT equipment is computed using the generalized linear model associated with the class. The predicted equipment parameter can be used to complete the dataset of the identified piece of IT equipment.

Abstract: Systems and methods include obtaining statistics based on monitoring in a cloud-based system for a given time period; and, responsive to determining an arrangement of counters for N counters, storing each of M counters for the given time period as a plurality of records with each record including a record type, a possible offset to a next record in terms of a counter identifier (ID), and a counter value, wherein N and M are integers and M<<N, and wherein the arrangement is determined such that most frequently used counters occupy lower counter IDs. The systems and methods can further include updating the arrangement of the counters for the N counters, to perform an optimization such that the most frequently used counters occupy lower counter IDs.

Abstract: Systems and methods include receiving a record associated with an incident that was detected by the CASB system in a Software-as-a-Service (SaaS) application; determining a hash based on a plurality of levels for the record; determining if the record exists in a data store based on the hash, and if the record exists, deleting an old record; and inserting the record in the data store based on the hash, wherein the data store is maintained in-memory and includes records at leaf nodes in a multi-level hash based on the plurality of levels.

Abstract: The present invention concerns methods to identify RIPK1 modulators capable of modulating RIPK1 activity, RIPK1 interacting molecules that modulate RIPK1 activity and pharmaceutical compositions comprising RIPK1 modulators.

Abstract: A system can provide a visual representation of an inventory of data entities for a distributed computing system. Inventory data including cost and operational data for data entities such as data centers, servers, and virtual machines, can be converted into a format file. The format file can be used to create a tree of nodes and node summaries corresponding to the data entities. A user interface can display hierarchical and isolated views of the tree revealing parent child relationships between data entities within a computing system infrastructure. Node summaries including cost and utilization data can be displayed to reveal how specific sub-costs such as labor and licensing, are driven by data entities in one level of the infrastructure and pushed to respective parent or child data entities in other levels. Views of the tree can be used to determine areas of inefficiency or reduced value within the computing system.

Abstract: A system and methods are described for retrieving, displaying, and/or analyzing law firm analytic data in response to natural language queries. The system, at an interface or bot, receives queries as plaintext messages requesting information about or maintained by a law firm. The system generates intent scores for the message for each of multiple intents and determines an intent for the message based on a highest generated score exceeding a threshold. The system extracts entities from the message, applies an algorithm to the determined intent and extracted entities, and identifies a data source from which to retrieve information. The system then retrieves and displays information from the identified data source.

Abstract: Systems and methods include receiving a record associated with an incident that was detected by the CASB system in a Software-as-a-Service (SaaS) application; determining a hash based on a plurality of levels for the record; determining if the record exists in a data store based on the hash, and if the record exists, deleting an old record; and inserting the record in the data store based on the hash, wherein the data store is maintained in-memory and includes records at leaf nodes in a multi-level hash based on the plurality of levels

Abstract: The detection of utilized virtual machines through usage pattern analysis is described. In one example, a computing device can collect utilization metrics from a virtual machine over time. The utilization metrics can be related to one or more processing usage, disk usage, network usage, and memory usage metrics, among others. The utilization metrics can be used to determine a number of clusters, and the clusters can be used to organize the utilization metrics into groups. Depending upon the number or overall percentage of the utilization metrics assigned to individual ones of the plurality of clusters, it is possible to determine whether or not the virtual machine is a utilized or an idle virtual machine. Once identified, utilized virtual machines can be migrated in some cases. Idle virtual machines can be shut down to conserve processing resources and costs in some cases.

Abstract: Systems and methods include determining log data for a time period at a plurality of senders, wherein each sender is a node in the cloud-based system, and the log data is associated with one or more cloud services; providing the log data to one or more storage clusters, via one or more distributors, for the time period; responsive to all of the plurality of senders performing the providing, moving to a next time period and repeating the determining and the providing; detecting a given sender is a faulty data source or a slow data source; and moving the given sender to a deferred processing list where the given sender does not hold up the moving to the next time period.

Abstract: Methods and systems assist data center customer to plan virtual data center (“VDC”) configurations, create purchase recommendations to achieve either an expansion or contraction of a VDC, and optimize the data center cost. Methods generate recommendations on lower cost combinations of virtual machine (“VM”) guest OS licenses, server computer hardware and VM software to optimize the costs are generated, generate data center customer plans for additional VMs with Quest OS for a projected period of time, provide recommendations on lower cost combination of guest OS licenses, server hardware, and VM software to optimize the cost. Methods also report any underutilized licensed servers and provide recommendations for cost savings when volume licenses can be replaced by instance based software licenses. Methods may generate VM placement recommendations to data center customers while the customers attempt to manually migrate VMs to different server computers.

Abstract: The detection of idle virtual machines through usage pattern analysis is described. In one example, a computing device can collect utilization metrics from a virtual machine over time. The utilization metrics can be related to one or more processing usage, disk usage, network usage, and memory usage metrics, among others. The utilization metrics can be separated into a set of training metrics and a set of validation metrics, and a number of clusters can be determined based on the set of training metrics. The clusters can be used to organize the set of validation metrics into groups. Depending upon the number or overall percentage of the utilization metrics assigned to individual ones of the plurality of clusters, it is possible to determine whether or not the virtual machine is an idle virtual machine. Once identified, idle virtual machines can be shut down to conserve processing resources and costs.