Momentstendk

Momentstendk is a hypothetical framework for capturing and analyzing discrete moments within datasets, emphasizing temporal context and cross-domain metadata. It proposes a lightweight data model and conventions for describing moments, their origins, and relationships.

The name blends "moments" with a provisional suffix designed to evoke temporal encoding of known dimensions, created by an informal online community in 2023 for demonstration purposes.

The core concepts organize data into three entities: Moment, Context, and Dimension. A Moment includes a timestamp, a value or magnitude, and optional quality indicators. Context captures source, location, and modality. Dimension defines axes or tags used to describe attributes across moments. Moments can be linked into sequences to form narratives or streams.

The data model supports JSON-like schemas with optional YAML and a compact binary form for performance. It emphasizes indexing by time, space, and category; a simple query language enables filtering by timestamp ranges, context attributes, and dimension tags.

Applications include statistical analysis of events, trend detection, and archival records. It suits IoT sensor streams, collections of social moments, and historical event datasets. Example Moment: timestamp 2025-06-01T12:30:00Z, value 83, context {sensor: "A1", location: "DK-01"}, dimension {"type": "temperature", "unit": "C"}.

History and reception: conceived by a loose online collaboration; first public draft circulated in 2023; adoption remains limited and no formal standard has emerged. Some practitioners compare it to existing time-series and event-data models.

See also: Moments (math); Event data model; Time-series databases.