Abstract: A system and a method for training non-parametric Machine Learning (ML) model instances in a collaborative manner is disclosed. A non-parametric ML model instance is trained at each of a plurality of data processing nodes to obtain a plurality of non-parametric ML model instances. Each non-parametric ML model instance developed at each data processing node is shared with each of remaining data processing nodes of the plurality of data processing nodes. Each non-parametric ML model instance is processed through a trainable parametric combinator to generate a composite model at each of the plurality of data processing nodes. The composite model is trained at each of the plurality of data processing nodes, over the respective local dataset, using Swarm learning to obtain trained composite models.

Abstract: Example techniques for execution of query plans are described. In an example, a query plan may include a first sub-plan and a second sub-plan that are to provide the same output. One among the first sub-plan and the second sub-plan may be selected during execution of the query plan based on a runtime performance parameter of a component to be involved in execution of the first sub-plan and a runtime performance parameter of a component to be involved in execution of the second sub-plan.

Abstract: A method and system for data centric heap profiling is disclosed. In one embodiment, a method, implemented in a computing device, for data centric heap profiling includes generating a type table for data structure types in source code using a compiler of the computing device. The method also includes identifying each heap allocation site and a corresponding data structure type in the source code using the compiler. The method further includes generating a data centric view of a heap o fan application compiled from the source code based on the each heap allocation site and the corresponding data structure type using a debugger of the computing device when a snapshot of the heap is requested during an execution of the application.

Abstract: A method and system for data centric heap profiling is disclosed. In one embodiment, a method, implemented in a computing device, for data centric heap profiling includes generating a type table for data structure types in source code using a compiler of the computing device. The method also includes identifying each heap allocation site and a corresponding data structure type in the source code using the compiler. The method further includes generating a data centric view of a heap o fan application compiled from the source code based on the each heap allocation site and the corresponding data structure type using a debugger of the computing device when a snapshot of the heap is requested during an execution of the application.