`regularity_score`

Generic Description

Scores how consistent inter-event gaps are. It is documented separately because time construction, sequence ordering, window parameters, and as-of behavior materially change analytical meaning.

Simple example:

WITH [{ts: 1735689600000, value: 10.0, state: 'ok'}, {ts: 1735689660000, value: 13.0, state: 'ok'}, {ts: 1735689840000, value: 9.0, state: 'warn'}, {ts: 1735690200000, value: 16.0, state: 'ok'}] AS readings
RETURN regularity_score(readings) AS cadence_regularity

Consumer-Level Explanation

Use it to separate predictable telemetry from irregular or noisy event streams. Prefer this function when the temporal assumption should be visible to the planner, especially for event streams, freshness checks, and sequence features.

More Detailed Explanation

regularity_score works on ordered temporal or sequence-style values inside Cypher. The practical contract is to assemble the sequence deliberately, choose the time or window parameter explicitly, and return named fields so downstream planner tooling does not invent unsupported window syntax.

Advanced Example

This example names the event stream and operational context before applying regularity_score, which helps planner tooling preserve the intended sequence semantics.

WITH 'sensor-1' AS subject_id,
     [{ts: 1735689600000, value: 10.0, state: 'ok'}, {ts: 1735689660000, value: 13.0, state: 'ok'}, {ts: 1735689840000, value: 9.0, state: 'warn'}, {ts: 1735690200000, value: 16.0, state: 'ok'}] AS readings
RETURN subject_id,
       regularity_score(readings) AS cadence_regularity

Real Use Cases

Real Limitations And Tradeoffs