Point-Versus Interval-Based Temporal Data Models

The association of timestamps with various data items such as tuples or attribute values is fundamental to the management of time-varying information. Using intervals in timestamps, as do most data models, leaves a data model with a variety of choices for giving a meaning to timestamps. Specifically, some such data models claim to be point-based while other data models claim to be interval-based. The meaning chosen for timestamps is important—it has a pervasive effect on most aspects of a data model, including database design, a variety of query language properties, and query processing techniques, e.g., the availability of query optimization opportunities. This paper precisely defines the notions of point-based and interval-based temporal data models, thus providing a new, formal basis for characterizing temporal data models and obtaining new insights into the properties of their query languages. Queries in point-based models treat snapshot equivalent argument relations identically. This renders point-based models insensitive to coalescing. In contrast, queries in interval-based models give significance to the actual intervals used in the timestamps, thus generally treating non-identical, but possibly snapshot equivalent, relations differently. The paper identifies the notion of time-fragment preservation as the essential defining property of an interval-based data model.