A wide range of database applications manage time-varying information. Existing database technology currently provides little support for managing such data. The research area of temporal databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query temporal data. This paper introduces the reader to temporal data management, surveys state-of-the-art solutions to challenging aspects of temporal data management, and points to research directions.

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.

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

Abstract not found

The databases of a wide range of applications, e.g., in data warehousing, store multiple states of time-evolving data. These databases contain a substantial part of now-relative data: data that became valid at some past time and remains valid until the current time. More specifically, two temporal aspects of data are frequently of interest, namely valid time, when data is true, and transaction time, when data is current in the database. The latter aspect is essential in all applications where accountability or trace-ability are required. When both aspects are captured, data is termed bitemporal. A number of indices have been devised for the efficient support of operations on time-varying data with one time dimension, but only little work, based mostly on R-trees, has addressed the indexing of two- or higher-dimensional temporal data. No indices exist that contend well with now-relative data, which leads to temporal data regions that are continuous functions of time. The paper proposes ...

In order to better support current and new applications, the major DBMS vendors are stepping beyond uninterpreted binary large objects, termed BLOBs, and are beginning to offer extensibility features that allow external developers to extend the DBMS with, e.g., their own data types and accompanying access methods. Existing solutions include DB2 extenders, Informix DataBlades, and Oracle cartridges. Extensible systems offer new and exciting opportunities for researchers and third-party developers alike. This paper reports on an implementation of an Informix DataBlade for the GR-tree, a new R-tree based index. This effort represents a stress test of the perhaps currently most extensible DBMS, in that the new DataBlade aims to achieve better performance, not just to add functionality. The paper provides guidelines for how to create an access method DataBlade, describes the sometimes surprising challenges that must be negotiated during DataBlade development, and evaluates the extensibility of the Informix Dynamic Server.

Medical informatics has been an important area for the application of computing and database technology for at least four decades. This area may benefit from the functionality offered by data warehousing. However, the special nature of clinical applications poses different and new requirements to data warehousing technologies, over those posed by conventional data warehouse applications. This article presents a number of exciting new research challenges posed by clinical applications, to be met by the database research community. These include the need for complex-data modeling features, advanced temporal support, advanced classification structures, continuously valued data, dimensionally reduced data, and the integration of very complex data. In addition, the support for clinical treatment protocols and medical research are interesting areas for research. 1. Introduction Modern businesses use a multitude of different computer systems to manage their daily business processes such as s...

The ability to model time-varying natures is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping. In this paper, we introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory. 1.

. Database support of time-varying phenomena typically assumes that entities change in a linear fashion. Many phenomena, however, change cyclically over time. Examples include monsoons, tides, and travel to the workplace. In such cases, entities may appear and disappear on a regular basis or their attributes or location may change with periodic regularity. This paper introduces an approach for modeling cycles based on cyclic intervals. Intervals are an important abstraction of time, and the consideration of cyclic intervals reveals characteristics about these intervals that are unique from the linear case. This work examines binary cyclic relations, distinguishing sixteen cyclic interval relations. We identify their conceptual neighborhood graph, showing which relations are most similar and demonstrating that this set of sixteen relations is complete. The results of this investigation provide the basis for extended data models and query languages that address cyclically var...

The analysis of the semantics of temporal data and queries plays a central role in the area of temporal databases. Although many different algebræ and models have been proposed, almost all of them are based on a point-based (snapshot) semantics for data. On the other hand, in the areas of linguistics, philosophy, and, recently, artificial intelligence, an oft-debated issue concerns the use of an interval-based versus a point-based semantics. In this paper, we first show some problems inherent in the adoption of a point-based semantics for data, then argue that these problems arise because there is no distinction drawn in the data between telic and atelic facts. We then introduce a three-sorted temporal model and algebra including coercion functions for transforming relations of one sort into relations of the other at query time which properly copes with these issues.