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continue to change as new information is added, these changes are viewed as modifications to the state, with the old, out-of-date data being deleted from the database.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

Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously which describe moving objects. Two fundamental abstractions are moving point and moving region, describing objects for which only the time-dependent position, or position and extent, are of interest, respectively. We propose to represent such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types is needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a "moving real" to represent the time-dependent distance of two moving points. It then becomes crucial to achieve (i) orthogonality in the design of the type system, i.e., type constructors can be applied uniformly, (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently, and (iii) closure and consistency between structure and operations of non-temporal and related temporal types. Satisfying these goals leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield apowerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exempli es the use of the abstract data types using SQL. The paper o ers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.

The Entity-Relationship (ER) model, using varying notations and with some semantic variations, is enjoying a remarkable, and increasing, popularity in both the research community#the computer science curriculum#and in industry. In step with the increasing diffusion of relational platforms, ER modeling is growing in popularity. It has been widely recognized that temporal aspects of database schemas are prevalent and difficult to model using the ER model. As a result, how to enable the ER model to properly capture time-varying information has, for a decade and a half, been an active area in the database-research community. This has led to the proposal of close to a dozen temporally enhanced ER models. This paper surveys all temporally enhanced ER models known to the authors. It is the first paper to provide a comprehensive overview of temporal ER modeling and it, thus, meets a need for consolidating and providing easy access to the research in temporal ER modeling. In the presentation of each model, the paper examines how the time-varying information is captured in the model and presents the new concepts and modeling constructs of the model. A total of 19 different design properties for temporally enhanced ER models are defined, and each model is characterized according the these properties.

We consider the problems of computing aggregation queries in temporal databases, and of maintaining materialized temporal aggregate views efficiently. The latter problem is particularly challenging since a single data update can cause aggregate results to change over the entire time line. We introduce a new index structure called the SBtree, which incorporates features from both segment-trees and B-trees. SB-trees support fast lookup of aggregate results based on time, and can be maintained efficiently when the data changes. We also extend the basic SB-tree index to handle cumulative (also called moving-window) aggregates. For materialized aggregate views in a temporal database or warehouse, we propose building and maintaining SB-tree indices instead of the views themselves. 1.

We consider the monitoring of a flow of incoming documents. More precisely, we present here the monitoring used in a very large warehouse built from XML documents found on the web. The flow of documents consists in XML pages (that are warehoused) and HTML pages (that are not). Our contributions are the following: ffl a subscription language which specifies the monitoring of pages when fetched, the periodical evaluation of continuous queries and the production of XML reports. ffl the description of the architecture of the system we implemented that makes it possible to monitor a flow of millions of pages per day with millions of subscriptions on a single PC, and scales up by using more machines. ffl a new algorithm for processing alerts that can be used in a wider context. We support

Much recent research has focused on.the need for, and definitions of, historical (or temporal) database and information systems to serve expanding information needs in a variety of applications. Almost all of this research has assumed that the domain of time itself was well-understood, and some representation for it simply needed to be included in the model to provide the needed temporal dimension. In this paper we present a simple, set-theoretic structure for a time domain which is independent of any particular calendric system. We concentrate on the general structure and operations necessary to support the needs arising in modelling time in information systems. 1.

Temporal logic is obtained by adding temporal connectives to a logic language. Explicit references to time are hidden inside the temporal connectives. Different variants of temporal logic use different sets of such connectives. In this chapter, we survey the fundamental varieties of temporal logic and describe their applications in information systems. Several features of temporal logic make it especially attractive as a query and integrity constraint language for temporal databases. First, because the references to time are hidden, queries and integrity constraints are formulated in an abstract, representationindependent way. Second, temporal logic is amenable to efficient implementation. Temporal logic queries can be translated to an algebraic language. Temporal logic constraints can be efficiently enforced using auxiliary stored information. More general languages, with explicit references to time, do not share these properties. Recent research has proposed various implementation t...

We propose constraint databases as an intermediate level facilitating the interoperability of spatiotemporal data models. Constraint query languages are used to express translations between different data models. We illustrate our approach in the context of a number of temporal, spatial, and spatiotemporal data models. 1 Introduction Very large temporal and spatial databases are a common occurrence nowadays. Although they are usually created with a specific application in mind, they often contain data of potentially broader interest, e.g., historical records or geographical data. By database interoperability we mean the problem of making the data from one database usable to the users of another. Data sharing between different applications and different sites is often An early version of some of the results in this paper appeared in [CR97]. The work of the first author was supported by NSF grant IRI-9632870. The work of the second author was supported by NSF grants IRI-9632871 and ...

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

On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complexdata found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answere...