Datecentric

Date-centric describes a design and analysis approach in which data is organized primarily around dates and times, emphasizing the temporal attributes of entities and events. It is used in contexts such as time-series analysis, event logging, archival records, and historical research, and is often contrasted with entity-centric designs that prioritize objects or individuals.

In a date-centric model, records typically include one or more timestamp fields such as event_date, start_time, end_time, or valid_time. The temporal dimension becomes central to querying, enabling straightforward retrieval by day, date ranges, or time-based trends. This orientation supports longitudinal studies and time-aware reporting, making it easier to reconstruct sequences of events.

Applications of date-centric design appear in financial market data, sensor logs, epidemiological timelines, project schedules, and version histories. It aligns with time-series databases and can be used alongside temporal databases that support concepts like valid time and transaction time. Date-centric approaches are often combined with event-sourcing or bitemporal models to capture both when something occurred and when it was recorded.

Benefits of a date-centric approach include intuitive support for temporally oriented queries, clearer reconstruction of sequences, and enhanced auditing capabilities. Limitations include the complexity of handling multiple dates per record, challenges with time zones, potential data duplication, and integration difficulties with non-date-centric data.

See also temporal database, time-series database, event sourcing, bitemporal database, chronology.