A temporal database (see Temporal Database) contains time-varying data. Time is an important aspect of all real-world phenomena. Events occur at specific points in time; objects and the relationships among objects exist over time. The ability to model this temporal dimension of the real world is essential to many computer applications, such as accounting, banking, econometrics, geographical information systems, inventory control, law, medical records, multi-media, process control, reservation systems, and scientific data analysis. Conventional databases represent the state of an enterprise at a single moment of time. Although the contents of the database continue to change as new information is added, these changes are viewed as modifications to the state, with the old, out-of-date data being deleted from the database. The current contents of the database may be viewed as a snapshot of the enterprise. When a conventional database is used, the attributes involving time are manipulated solely by the application programs, with little help

A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two heretofore separate research areas. We first characterize the time domain, then investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. Temporal and real-time DBMS implementation is examined. We conclude with a summary of the major accomplishments of the research to date, and list several research questions that should be addressed next. Keywords: object-oriented database, relational databases, query language, temporal data model, time-constrained database, transaction time, user-defined time, valid time 1 Introduction Time is an important aspect of all real-world phenomena. Events occur at specific points in time; objects and the relationships among objects exist over time. The ability to model this temporal dimension of the real worl...

The relational algebra is a procedural query language for relational databases. In this paper we survey extensions of the relational algebra that can query databases recording time-varying data. Such an algebra is a critical part of a temporal DBMS. We identify 26 criteria that provide an objective basis for evaluating temporal algebras, Seven of the criteria are shown to be mutually unsatisfiable, implying there can be no perfect temporal algebra, Choices made as to which of the incompatible criteria are satisfied characterize existing algebras Twelve time-oriented algebras are summarized and then evaluated against the criteria. We demonstrate that the design space has in some sense been explored in that all combinations of basic design decisions have at least one representative algebra. Coverage of the remaining criteria provides one measure of the quality of each algebra We argue that all of the criteria are independent and that the criteria identified as compatible are indeed so, Finally, we list plausible properties proposed by others that are either subsumed by other criteria, are not well defined, or have no objective basis for being evaluated. The algebras realize many different approaches to what appears initially to be a straightforward design task.

Abstract- We present an access method for timeslice queries that reconstructs a past state s(t) of a time-evolving collection of objects, in O(log,, n + Is(t)l/b) I/O ‘8, where Is(t)1 denotes the size of the collection at time t, n is the total number of changes in the collection’s evolution and b is the size of an I/O transfer. Changes include the addition, deletion or attribute modification of objects; they are assumed to occur in increasing time order and always affect the most current state of the collection (thus our index supports transaction-time.) The space used is 0 n/b) while the update processing is constant per change, i.e., independent of n. This is the first I I O-optimal access method for this problem using O(n/b) space and O(1) updating (in the expected amortized sense due to the use of hashing.) This performance is also achieved for interval intersection temporal queries. An advantage of our approach is that its performance can be tuned to match particular application needs (trading space for query time and vice versa). In addition, the Snapshot Index can naturally migrate data on a write-once optical medium while maintaining the same performance bounds.

To add time support to the relational model, both first normal form (1NF) and non1NF approaches have been proposed. Each has associated difficulties. Remaining within 1NF when time support is added may introduce data redundancy. The non1NF models may be incapable of directly using existing relational storage structures or query evaluation technologies. This paper describes a new, conceptual temporal data model that better captures the time-dependent semantics of the data, while permitting multiple data models at the representation level. This conceptual model effectively moves the distinction between the various existing data models from a semantic basis to a physical, performancerelevant basis. We define a conceptual notion of a bitemporal relation where tuples are stamped with sets of two-dimensional chronons in transaction-time/valid-time space. Next, we describe five representation schemes that support both valid and transaction time; these representations include both 1NF and non-...

A standard relation has two dimensions: attributes and tuples. A temporal relation contains two additional orthogonal time dimensions, namely, valid time and transaction time. Valid time records when facts are true in the modeled reality, and transaction time records when facts are stored in the temporal relation. Although, in general, there are no restrictions between the valid time and transaction time associated with each fact, in many practical applications, the valid and transaction times exhibit more or less restricted interrelationships that define several types of specialized temporal relations. The paper examines five different areas where a variety of types of specialized temporal relations are present. In application systems with multiple, interconnected temporal relations, multiple time dimensions may be associated with facts as they flow from one temporal relation to another. For example, a fact may have an associated transaction time indicating when it was stored in a previous temporal relation. The paper investigates several aspects of the resulting generalized temporal relations, including the ability to query a predecessor relation from a successor relation. The presented framework for generalization and specialization allows researchers as well as database and system designers to precisely characterize, compare, and thus better understand temporal relations and the application systems in which they are embedded. The framework’s comprehensiveness and its use in understanding temporal relations are demonstrated by placing previously proposed temporal data models within the framework. The practical relevance of the defined specializations and gener-alizations is illustrated by sample realistic applications in which they occur. The additional semantics of specialized relations are especially useful for improving the performance of query processing.

Although “now ” is expressed in SQL as CURRENT_TIMESTAMP within queries, this value cannot be stored in the database. However, 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 use also leads to a new type of database, consisting of tuples with variables, termed a variable database. This article proposes a framework for defining the semantics of the variable databases of the relational and temporal relational data models. A framework is presented because several reasonable meanings may be given to databases that use some of the specific temporal variables that have appeared in the literature. Using the framework, the article defines a useful semantics for such databases. Because situations occur where the existing time variables are inadequate, two new types of modeling entities that address these shortcomings, timestamps that we call now-relative and now-relative indeterminate, are introduced and defined within the framework. Moreover, the article provides a foundation, using algebraic

Normal forms play a central role in the design of relational databases. Several normal forms for temporal relational databases have been proposed. These definitions are particular to specific temporal data models, which are numerous and incompatible.

We define temporal extensions to a uniform, behavioral and functional object model by providing an extensible set of structural and behavioral abstractions to model various notions of time for different applications. We discuss the temporal semantics of inheritance by defining a lifespan behavior on objects in a collection. Finally, we give an elaborative example and show that temporal objects can be queried without adding any extra construct to the underlying query language. 1 Introduction Most of the applications for which object management systems (OMSs) are expected to provide support exhibit some form of temporality. Some examples are the following: in engineering databases, there is a need to identify different versions of a design as it evolves; in multimedia systems, the video images are timed and synchronized with audio; in office information systems, documents are ordered based on their temporal relationships. In this paper we present temporal extensions to the TIGUKAT 1 O...

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