Showing papers by "Christian S. Jensen published in 1998"

The consensus glossary of temporal database concepts — February 1998 version

Abstract: This document1 contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes explanations of concepts as well as discussions of the adopted names.

Point-versus interval-based temporal data models

Abstract: The association of timestamps with various data items such as tuples or attribute values is fundamental to the management of time varying information. Using intervals in timestamps, as do most data models, leaves a data model with a variety of choices for giving a meaning to timestamps. Specifically, some such data models claim to be point based while other data models claim to be interval based. The meaning chosen for timestamps is important it has a pervasive effect on most aspects of a data model, including database design, a variety of query language properties, and query processing techniques, e.g., the availability of query optimization opportunities. The paper precisely defines the notions of point based and interval based temporal data models, thus providing a new formal basis for characterizing temporal data models and obtaining new insights into the properties of their query languages. Queries in point based models treat snapshot equivalent argument relations identically. This renders point based models insensitive to coalescing. In contrast, queries in interval based models give significance to the actual intervals used in the timestamps, thus generally treating non identical, but possibly snapshot equivalent relations differently. The paper identifies the notion of time fragment preservation as the essential defining property of an interval based data model.

Research issues in clinical data warehousing

Abstract: 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.

Transitioning Temporal Support in TSQL2 to SQL3

Abstract: This document summarizes the proposals before the SQL3 committees to allow the addition of tables with valid-time and transactiontime support into SQL/Temporal, and explains how to use these facilities to migrate smoothly from a conventional relational system to one encompassing temporal support. Initially, important requirements to a tdiscussed. The proposal then describes the language additions necessary emporal system that may facilitate such a transition are motivated and to add valid-time support to SQL3 while fulfilling these requirements. The constructs of the language are divided into four levels, with each level adding increased temporal functionality to its predecessor. A prototype system implementing these constructs on top of a conventional DBMS is publicly available.

Systematic Change Management in Dimensional Data Warehousing

Abstract: With the widespread and increasing use of data warehousing in industry, the design of effective data warehouses and their maintenance has become a focus of attention. Independently of this, the area of temporal databases has been an active area of research for well beyond a decade. This article identifies shortcomings of so-called star schemas, which are widely used in industrial warehousing, in their ability to handle change and subsequently studies the application of temporal techniques for solving these shortcomings. Star schemas represent a new approach to database design and have gained widespread popularity in data warehousing, but while they have many attractive properties, star schemas do not contend well with so-called slowly changing dimensions and with state-oriented data. We study the use of so-called temporal star schemas that may provide a solution to the identified problems while not fundamentally changing the database design approach. More specifically, we study the relative database size and query performance when using regular star schemas and their temporal counterparts for state-oriented data. We also offer some insight into the relative ease of understanding and querying databases with regular and temporal star schemas.

R-Tree Based Indexing of Now-Relative Bitemporal Data

Abstract: 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, leading to bitemporal data. Only little work, based mostly on R-trees, has addressed the indexing of bitemporal 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 two extended R -trees that permit the indexing of data regions that grow continuously over time, by also letting the internal bounding regions grow. Internal bounding regions may be triangular as well as rectangular. New heuristics for the algorithms that govern the index structure are provided. As a result, dead space and overlap, now also functions of time, are reduced. Performance studies indicate that the best extended index is typically 3‐5 times faster than the existing R-tree based indices.

Conceptual Modeling of Time-Varying Information

Abstract: A wide range of database applications manage information that varies over time. Many of the underlying database schemas of these were designed using one of the several versions, with varying syntax and semantics, of the Entity-Relationship (ER) model. In the research community as well as in industry, it is common knowledge that the temporal aspects of the mini-world are pervasive and important, but are also difficult to capture using the ER model. Not surprisingly, several enhancements to the ER model have been proposed in an attempt to more naturally and elegantly support the modeling of temporal aspects of information. Common to the existing temporally extended ER models, few or no specific requirements to the models were given by their designers. With the existing proposals, an ontological foundation, and novel requirements as its basis, this paper formally defines a graphical, temporally extended ER model. The ontological foundation serves to aid in ensuring a maximally orthogonal design, and the requirements aim, in part, at ensuring a design that naturally extends the syntax and semantics of the regular ER model. The result is a novel model that satisfies an array of properties not satisfied by any single previously proposed model1. Keywords—Conceptual modeling, database design, entity-relationship models, temporal databases, temporal data models, temporal semantics.

Spatio-temporal database support for legacy applications

Abstract: In areas such as finance, marketing, and property and resource management, many database applications manage spatio-temporal data. These applications typically run on top of a relational DBMS and manage spatio-temporal data either using the DBMS, which provides little support, or employ the services of a proprietary system that co-exists with the DBMS, but is separate from and not integrated with the DBMS. This wealth of applications may benefit substantially from built-in, integrated spatio-temporal DBMS support. Providing a foundation for such support is an important and substantial challenge. This paper initially defines technical requirements to a spatio-temporal DBMS aimed at protecting business investments in the existing legacy applications and at reusing personnel expertise. These requirements provide a foundation for making it economically feasible to migrate legacy applications to a spatio-temporal DBMS. The paper next presents the design of the core of a spatio-temporal extension to SQL–92, called STSQL, that satisfies the requirements. STSQL supports multiple temporal as well as spatial dimensions. Queries may “ignore” any dimension; this provides an important kind of upward compatibility with SQL–92. Queries may also view the tables in a dimensional fashion, where the DBMS provides so-called snapshot reducible query processing for each dimension. Finally, queries may view dimension attributes as if they are no different from other attributes.

An extensible notation for spatiotemporal index queries

Abstract: Temporal, spatial and spatiotemporal queries are inherently multidimensional, combining predicates on explicit attributes with predicates on time dimension(s) and spatial dimension(s). Much confusion has prevailed in the literature on access methods because no consistent notation exists for referring to such queries. As a contribution towards eliminating this problem, we propose a new and simple notation for spatiotemporal queries. The notation aims to address the selection-based spatiotemporal queries commonly studied in the literature of access methods. The notation is extensible and can be applied to more general multidimensional, selection-based queries.

Stratum approaches to temporal DBMS implementation

Abstract: Previous approaches to implementing temporal DBMSs have assumed that a temporal DBMS must be built from scratch, employing an integrated architecture and using new temporal implementation techniques such as temporal indexes and join algorithms. However, this is a very large and time-consuming task. The paper explores approaches to implementing a temporal DBMS as a stratum on top of an existing non-temporal DBMS, rendering implementation more feasible by reusing much of the functionality of the underlying conventional DBMS. More specifically, the paper introduces three stratum meta-architectures, each with several specific architectures. Based on a new set of evaluation criteria, advantages and disadvantages of the specific architectures are identified. The paper also classifies all existing temporal DBMS implementations according to the specific architectures they, employ. It is concluded that a stratum architecture is the best short, medium, and perhaps even long-term, approach to implementing a temporal DBMS.

Efficient differential timeslice computation

Abstract: Transaction-time databases support access to not only the current database state, but also previous database states. Supporting access to previous database states requires large quantities of data and necessitates efficient temporal query processing techniques. Previously, we presented a log based storage structure and algorithms for the differential computation of previous database states. Timeslices-i.e., previous database states-are computed by traversing a log of database changes, using previously computed and cached timeslices as outsets. When computing a new timeslice, the cache will contain two candidate outsets: an earlier outset and a later outset. The new timeslice can be computed by either incrementally updating the earlier outset or decrementally "downdating" the later outset using the log. The cost of this computation is determined by the size of the log between the outset and the new timeslice. The paper proposes an efficient algorithm that identifies the cheaper outset for the differential computation. The basic idea is to compute the sizes of the two pieces of the log by maintaining and using a tree structure on the timestamps of the database changes in the log. The lack of a homogeneous node structure, a controllable and high fill factor for nodes, and of appropriate node allocation in existing tree structures (e.g., B/sup +/ trees, Monotonic B/sup +/ trees, and Append only trees) render existing tree structures unsuited for our use. Consequently, a specialized tree structure, the pointer-less insertion tree, is developed to support the algorithm. As a proof of concept, we have implemented a main memory version of the algorithm and its tree structure.

Mapping Temporal ER Diagrams to Relational Schemas

Abstract: Many database applications manage information that varies over time, and most of the database schemas for these applications were designed using one of the several versions of the Entity-Relationship (ER) model. In the research community as well as in industry, it is common knowledge that temporal aspects of data are pervasive and important to the applications, but are also difficult to capture using the ER model. The research community has developed temporal ER models, in an attempt to provide modeling constructs that more naturally and elegantly support capturing the temporal aspects. Specifically, the temporal models provide enhanced support for capturing aspects such as lifespans, valid time, and transaction time of data. Because commercial database management systems support neither the ER model nor any temporal ER model as a model for data manipulation—but rather support various versions of the relational model for this purpose—we provide a two-step transformation from temporal ER diagrams, with built-in support for lifespans and valid and transaction time, to relational schemas. The first step of the algorithm translates a temporal ER diagram into relations in a surrogate-based relational target model; and the second step further translates this relational schema into a schema in a lexically-based relational target model.

Vacuuming Temporal Databases

Abstract: A wide range of real-world database applications, including financial and medical applications, are faced with accountability and trace-ability requirements. These requirements lead to the replacement of the usual update-in-place policy by an append-only policy, yielding so-called transaction-time databases. With logical deletions being implemented as insertions at the physical level, these databases retain all previously current states and are ever-growing. A variety of physical storage structures and indexing techniques as well as query languages have been proposed for transaction-time databases, but the support for physical deletion, termed vacuuming, has received precious little attention. Such vacuuming is called for by, e.g., the laws of many countries. Although necessary, with vacuuming, the database’s previously perfect and reliable recollection of the past may be manipulated via, e.g., selective removal of records pertaining to past states. This paper provides a semantic framework for the vacuuming of transaction-time databases. The main focus is to establish a foundation for the correct and user-friendly processing of queries and updates against vacuumed databases. Queries that may return results affected by vacuuming are intercepted, and the user is presented with the option of issuing similar queries that are not affected by vacuuming.