Temporal Databases

Abstract

A temporal database stores time‐varying data and has the capabilities to manipulate it. Two commonly used time dimensions for maintaining temporal data are valid time and transaction time. Temporal databases are classified as valid time, transaction time, or bitmeporal databases. Temporality in databases involve subtle issues like comparing database states at different times, temporal grouping, different ways to represent the same temporal data, consistency of the timestamps of different attributes, temporal integrity constraints, and designing temporal databases. A temporal atom is a timestamp and value pair in which The timestamp is valid and/or transaction time and value are the time‐varying data. For representing and manipulating temporal data, data models handle temporal atoms differently. In relational data model, which is the focus of this entry, attribute or tuple time stamping may be used. Temporal algebra and temporal calculus languages are defined accordingly. Proposals for extending SQL2 for temporal data manipulation exist, and SQl3 supports user‐defined data types that can be used for defining and manipulating temporal data. This entry covers temporal query languages and the temporal issues mentioned above.

Analysing River Systems with Time Series Data Using Path Queries in Graph Databases

“…A first version of this extension appeared in an extended abstract [24]. Well-known concepts in temporal databases are valid and transaction times [6]. In this paper we work with valid time, that is, the time where the edges are valid in the real world, opposite to transaction time, which reflects the time where the information is stored in the database.…”

“…Although this query can be addressed by a static graph model, the user's query must provide the interval in which the event could have occurred. A more interesting query could compute and return such interval(s) (along the lines of temporal database queries [6]). This is captured by the notion of CP, since CPs contain not only the paths of river segments involved but also the interval during which the event occurred simultaneously in that group of segments.…”

Modern Temporal Data Models: Strengths and Weaknesses

“…After 1982, the research on temporal database models began to make significant progress. The most important result of this phase was the book written by Tansel, Clifford, Gadia, Jajodia, Segev and Snodgrass [20]. The first part of the book describes several temporal data models: the two models mentioned above as well as Gadia's TempSQL [5], Snodgrass TQuel [17] and Lorentzos IXRM model [11].…”

Real-World Semantics of Conceptual Models

“…This allows us to think about the ways in which the physical computer impacts the domain. Numerous extensions to the ER notation have been defined, notably extensions of cardinality constraints [23,24] extensions that deal with time and history [25,26], extensions with aggregation structures [27,28,29,30,31] and, more recently, sets [32], and extensions with taxonomic structures [27,33,34,35]. Temporal extensions of the ER notation offer ways of expressing constraints on how entities, their relationships and their cardinality constraints can evolve over time.…”