incrementad

Incrementad is a theoretical framework and collection of techniques for incremental computation and data synchronization. It describes systems that maintain derived results as input data changes, updating only the affected parts rather than recomputing from scratch. The core idea is to model computations as dependency graphs and to propagate changes efficiently through that graph.

The term combines 'increment' with the suffix -ad, used in some tech terms to denote a domain or tool. It arose in academic discussions of incremental computation and has been used in several papers and prototypes since the early 2010s to contrast with batch recomputation.

Architecturally, incrementad emphasizes incremental evaluation, memoization of intermediate results, and diffs or deltas. It often employs a change log, versioned data, and event streams to trigger updates. Implementations may rely on static dependency analysis or dynamic tracking to identify impacted nodes and to schedule updates in parallel where safe.

Applications include real-time analytics, incremental build systems, live spreadsheets, graph-based data integration, and machine learning pipelines that require frequent re-training with partial data. In practice, performance depends on the granularity of changes, the size of the dependency graph, and the cost of maintaining the incremental state.

Limitations include complexity of correct change propagation, potential overhead for small updates, and challenges with non-deterministic inputs. Related concepts are incremental computation, delta encoding, and incremental learning. See also: incremental maintenance, streaming algorithms.