Abstract: In accordance with some aspects of the present disclosure, an apparatus is disclosed. The apparatus includes a processor and a memory, wherein the memory includes programmed instructions that when executed by the processor, cause the apparatus to receive a request to join a plurality of entity data structures using a first join order, determine a first performance cost of the first join order, determine a second performance cost of a second join order, determine whether the second performance cost is lower than the first performance cost, in response to determining that the second performance cost is lower than or exceeds the first performance cost, select the second join order or the first join order, respectively, join the plurality of entity data structures using the selected join order, and send the joined plurality of entity data structures.

Abstract: There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: In accordance with some aspects of the present disclosure, an apparatus is disclosed. The apparatus includes a processor and a memory, wherein the memory includes programmed instructions that when executed by the processor, cause the apparatus to receive a request to join a plurality of entity data structures using a first join order, determine a first performance cost of the first join order, determine a second performance cost of a second join order, determine whether the second performance cost is lower than the first performance cost, in response to determining that the second performance cost is lower than or exceeds the first performance cost, select the second join order or the first join order, respectively, join the plurality of entity data structures using the selected join order, and send the joined plurality of entity data structures.

Abstract: There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Abstract: Methods and systems are presented for dynamically configuring a software application to log different data variables without requiring re-deploying the software application. When a software application is executed, the software application may obtain and/or generate data variables. During runtime of the software application while processing a request, the software application may access a log script external to the software application. The software application may select a subset of data variables for logging, and may record only the subset of data variables in a log file. The log script may be modified without requiring the software application to be modified or re-deployed such that the software application may log different subset of data variables while processing different processes based on different versions of the log script.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.