nexyron.entity_time_range

Generic Description

Scan timeline events for a selected list of subject node IDs over a time range.

Simple example:

CALL nexyron.entity_time_range([0, 1], 0, 60000, 'Reading')
YIELD node_id, source_id, kind, tag, start_ts, end_ts, weight
RETURN node_id, source_id, kind, tag, start_ts, end_ts, weight
ORDER BY start_ts

The from_ts and to_ts bounds are canonical i64 epoch-millisecond integers. currenttimestamp(), now(), timestamp(), date(...), and datetime(...) return that format, and plus/minus integer arithmetic uses milliseconds.

Consumer-Level Explanation

Use nexyron.entity_time_range when you already know which entities matter and want a bounded temporal scan for just those subjects. Use these procedures when cataloged temporal storage is the source of truth rather than an ad hoc list assembled inside one Cypher expression.

Conceptual Explanation

This is the targeted counterpart to nexyron.global_time_range. It keeps the same event-style output shape but narrows the scan to one selected entity set, which makes it more appropriate for user history, asset history, or case-specific investigations.

More Detailed Explanation

In practical queries, start by deciding the row grain you want after the call: one row per node, one row per path, one row per registry object, one row per artifact, or one row per summary. Then keep that grain explicit with YIELD and named projections. That is the difference between a useful planner-facing example and a vague call that downstream tooling cannot safely compose. For contract-driven procedures, the executable examples on these pages intentionally inspect procedure metadata unless the required named artifacts are created in the same example.

Advanced Example

CALL nexyron.entity_time_range([0, 1], currenttimestamp() - 604800000, currenttimestamp(), null)
YIELD node_id, source_id, kind, tag, start_ts, end_ts, weight
RETURN node_id, source_id, kind, tag, start_ts, end_ts, weight
ORDER BY node_id, start_ts DESC
LIMIT 20

Real Use Cases

Real Limitations And Tradeoffs