Inkrementit

Inkrementit is a term used in computer science to describe a class of techniques that emphasize updating results through small changes, rather than recomputing from scratch. In this paradigm, a program maintains a state and a set of dependencies; when input data changes, only the portion of the computation affected by the change is recomputed, and the new results are propagated forward.

The term appears in discussions of incremental computation and is used to describe both general principles and specific implementations. While the exact terminology varies, inkrementit is frequently linked to delta propagation, memoization, and dependency tracking.

Core ideas include maintaining an incremental state, deriving deltas from input changes, and using graphs of dependencies to limit work. Practical implementations balance the granularity of updates against overhead for tracking dependencies, and must address issues of consistency, correctness under concurrent updates, and handling non-monotonic changes. Some approaches combine inkrementit with reactive or streaming models to support real-time or near-real-time workloads.

Applications span database systems with incremental view maintenance, live dashboards, incremental compilers, and streaming analytics. In large-scale data processing, inkrementit-inspired methods can reduce latency and resource use by avoiding full recomputations when only a subset of inputs changes.