grVs
grVs, or graphical vector streams, are a conceptual framework for representing and processing dynamic, multi-dimensional data. They are not a specific software or hardware implementation but rather a theoretical construct used in fields like data visualization, signal processing, and machine learning. The core idea behind grVs is to capture the evolution of data points through a series of interconnected states or nodes. Each state can represent a particular characteristic or feature of the data at a given time or condition. The connections between these states signify transitions or relationships, allowing for the modeling of complex dependencies and temporal changes.
The "graphical" aspect refers to the visual or topological nature of the representation, where data is organized