Although “ now ” is expressed in SQL and CURRENT_TIMESTAMP within queries, this value cannot be stored in the database. How ever, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as “ now ” “ until-changed, ” “**,” “@,” and “-”. Time variables are very desirable, but their used also leads to a new type of database, consisting of tuples with variables, termed a variable database.

The issue of periodicity is generally understood to be a desirable property of temporal data that should be supported by temporal database models and their query languages. Nevertheless, there has so far not been any systematic examination of how to incorporate this concept into a temporal DBMS. In this paper we describe two concepts of periodicity, which we call strong periodicity and near periodicity, and discuss how they capture formally two of the intuitive meanings of this term. We formally compare the expressive power of these two concepts, relate them to existing temporal query languages, and show how they can be incorporated into temporal relational database query languages, such as the proposed temporal extension to SQL, in a clean and straightforward manner.

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both into the structure of the extended relations of these temporal models and into the extended relational algebra or calculus that they define. Because of these differences, it has been difficult to compare the proposed models and to make judgments as to which of them might in some sense be equivalent or even better . In this paper we define temporally grouped and temporally ungrouped historical data models and propose two notions of historical relational completeness , analogous to Codd's notion of relational completeness, one for each type of model. We show that the temporally ungrouped models are less expressive than the grouped models, but demonstrate a technique for extending the ungrouped models with a grouping mechanism to capture the additional semantic power of temporal grouping. For the ungrouped models, we define three different languages, a logic with explicit reference to time, a temporal logic, and a temporal algebra, and motivate our choice for the first of these as the basis for completeness for these models. For the grouped models, we define a many-sorted logic with variables over ordinary values, historical values, and times. Finally, we demonstrate the equivalence of this grouped calculus and the ungrouped calculus extended with a grouping mechanism. We believe the classification of historical data models into grouped and ungrouped models provides a useful framework for the comparison of models in the literature, and furthermore, the exposition of equivalent languages for each type provides reasonable standards for common, and minimal, notions of historical relational completeness.

Critical to the design of an historical database model is the representation of the “existence” of objects across the temporal dimension — for example, the “birth,” “death,” or “rebirth” of an individual, or the establishment or dis-establishment of a relationship. The notion of the “lifespan” of a database object is proposed as a simple framework for expressing these concepts. An object's lifespan is simply those periods of time during which the database models the properties of that object. In this paper we propose the historical relational data model (HRDM) and algebra that is based upon lifespans and that views the values of all attributes as functions from time points to simple domains. The model that we obtain is a consistent extension of the relational data model, and provides a simple mechanism for providing both time-varying data and time-varying schemes.

Article On an algebra for historical relational databases: two views Share on Authors: James Clifford Computer Applications and Information Systems, Graduate School of Business Administration, New York University Computer Applications and Information Systems, Graduate School of Business Administration, New York UniversityView Profile , Abdullah Uz Tansel Baruch College, Statistics and Computer Information Systems, City University of New York Baruch College, Statistics and Computer Information Systems, City University of New YorkView Profile Authors Info & Claims SIGMOD '85: Proceedings of the 1985 ACM SIGMOD international conference on Management of dataMay 1985 Pages 247–265https://doi.org/10.1145/318898.318922Online:01 May 1985Publication History 133citation606DownloadsMetricsTotal Citations133Total Downloads606Last 12 Months12Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

An optimizing translation mechanism for the dynamic interaction between a logic-based expert system written in PROLOG and a relational database accessible through SQL is presented. The mechanism makes use of an intermediate language that decomposes the optimization problem and makes the proposed approach target-language independent. It can either facilitate expert system - database interaction, e.g., when integrating expert systems into business systems, or augment existing databases with (external) deductive capabilities.

The concept of a historical database is introduced as a tool for modeling the dynamic nature of some part of the real world. Just as first-order logic has been shown to be a useful formalism for expressing and understanding the underlying semantics of the relational database model, intensional logic is presented as an analogous formalism for expressing and understanding the temporal semantics involved in a historical database. The various components of the relational model, as extended to include historical relations, are discussed in terms of the model theory for the logic IL s , a variation of the logic IL formulated by Richard Montague. The modal concepts of intensional and extensional data constraints and queries are introduced and contrasted. Finally, the potential application of these ideas to the problem of natural language database querying is discussed.