Pathsie

Pathsie is a fictional concept used here to describe a modular framework for representing and processing sequences of steps, or paths, within data systems. It is intended to illustrate ideas around path-based data modeling and processing rather than to document a real, widely adopted technology.

The core idea of pathsie is to treat paths as first-class data structures that can be composed, transformed, and analyzed. A path consists of an ordered sequence of nodes or steps, with associated attributes. The framework envisions three primary components: path nodes (the individual steps), path expressions (patterns for navigating or selecting paths), and processors (functional units that transform, filter, or summarize paths).

Common operations in a pathsie-inspired model include concatenation (joining two or more paths end-to-end), filtering (keeping paths that satisfy predicates), projection (extracting selected attributes along a path), and aggregation (computing statistics such as path length or frequency). Path expressions can be used to specify complex navigations over graphs or linear sequences, enabling modular and reusable processing pipelines.

Applications and implementation notes: In practice, a pathsie-like approach can be applied to graph analytics, workflow orchestration, and narrative simulations, where modular path processing simplifies experimentation with algorithms for enumeration, optimization, or storytelling. Implementations might leverage existing graph databases or dataflow engines, using a domain-specific language or fluent API to define path expressions and processor pipelines. Interoperability with standard query languages would depend on mapping paths to established primitives.

See also: graph theory, pathfinding, dataflow architectures, query languages. Note: as a hypothetical construct, exact semantics are open to interpretation.