StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics https://doi.org/10.1109/TVCG.2020.3030352
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StackGenVis/frontend/node_modules/vega-dataflow
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README.md

vega-dataflow

Reactive dataflow processing.

Defines a reactive dataflow graph that can process both scalar values and streaming relational data. A central Dataflow instance manages and schedules a collection of Operator instances, each of which is a node in a dataflow graph. Each operator maintains a local state value, and may also process streaming data objects (or tuples) passing through. Operators may depend on a set of named Parameters, which can either be fixed values or live references to other operator values.

Upon modifications to operator parameters or input data, changes are propagated through the graph in topological order. Pulse objects propagate from operators to their dependencies, and carry queues of added, removed and/or modified tuples.

This module contains only the core reactive dataflow processing engine. Other modules provide a library of Operator types for data stream query processing, including data generation, sampling, filtering, binning, aggregation, cross-stream lookup, visual encoding, and spatial layout.

For more information about data stream transforms, see the Vega transform documentation.