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continue to change as new information is added, these changes are viewed as modifications to the state, with the old, out-of-date data being deleted from the database.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 engineers who analyze traffic on high bandwidth networks must filter and aggregate either recorded traces of network packets or live traffic from the network itself. These engineers perform operations similar to database queries, but cannot use conventional data managers because of performance concerns and a semantic mismatch between the analysis operations and the operations supported by commercial DBMSs. Traffic analysis does not require fast random access, transactional update, or relational joins. Rather, it needs fast sequential access to a stream of traffic records and the ability to filter, aggregate, define windows, demultiplex, and remultiplex the stream. Tribeca is an extensible, stream-oriented DBMS designed to support network traffic analysis. It combines ideas from temporal and sequence databases with an implementation optimized for databases stored on high speed ID-1 tapes or arriving in real time from the network. The paper describes Tribeca's query language, executo...

It seems somehow fitting to begin this paper on databases that store historical information with a chronology, touching briefly on all work that I am aware of in this area. I discuss in some detail what I consider to be the ten most important papers and events in terms of their impact on the discipline of temporal databases. These are emphatically not meant to detract from the other excellent papers in temporal databases. My goal is to characterize the evolution of this field, as an introduction to the approximately 350 papers specifically relating time to databases that have appeared thus fax. I then identify and discuss areas where more work is needed. 1

This paper presents the model which is the basis for a system to manage various kinds of sequence data. The model separates the data from the ordering information, and includes operators based on two distinct abstractions of a sequence. The main contributions of the model are: (a) it can deal with different types of sequence data, (b) it supports an expressive range of sequence queries, (c) it draws from many of the diverse existing approaches to modeling sequence data. 1

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.

Joins are arguably the most important relational operators. Poor implementations are tantamount to computing the Cartesian product of the input relations. In a temporal database, the problem is more acute for two reasons. First, conventional techniques are designed for the optimization of joins with equality predicates, rather than inequality predicates which are prevalent in valid-time queries. Second, the presence of temporally-varying data dramatically increases the size of the database. These factors require new techniques to efficiently evaluate valid-time joins. We address this need for efficient join evaluation in databases supporting valid-time. A new temporal-join algorithm based on tuple partitioning is introduced. This algorithm avoids the quadratic cost of nested-loop evaluation methods; it also avoids sorting. The algorithm is then adapted to an incremental mode of operation, which is especially appropriate for temporal query evaluation. Performance comparisons ...

Transaction time databases retain and provide access to prior states of a database. An update “inserts ” a new record while preserving the old version. Immortal DB builds transaction time database support into a database engine, not in middleware. It supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the “clock times ” of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to transaction updates after commit. Versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing, which is essential for high performance historical queries. Experiments show that Immortal DB introduces little overhead for accessing recent database states while providing access to past states.

It is an established notion among financial analysts that price moves in patterns and these patterns can be used to forecast future price. As the definitions of these patterns are often subjective, every analyst has a need to define and search meaningful patterns from historical time series quickly and efficiently. However, such discovery process can be extremely laborious and technically challenging in the absence of a high level pattern definition language. In this paper, we propose a chart-pattern language (CPL for short) to facilitate pattern discovery process. Our language enables financial analysts to (1) define patterns with subjective criteria, through introduction of fuzzy constraints, and (2) incrementally compose complex patterns from simpler patterns. We demonstrate through an array of examples how real life patterns can be expressed in CPL. In short, CPL provides a high-level platform upon which analysts can define and search patterns easily and without any programming expertise. CPL is a domain-specific language embedded in Haskell. We show how various features of a functional language, such as pattern matching, higher-order functions, lazy evaluation, facilitate pattern definitions and implementation. Furthermore, Haskell's type system frees the programmers from annotating the programs with types.

Existing query optimization methods do not satisfy some of today's query processing requirements. Typically, only coarse or inaccurate estimates of database statistics are available prior to query evaluation. On the other hand, massive database sizes and growing demands for sophisticated processing result in long-running queries in extensible Object-Relational DBMS, particularly in decision support, and in data warehousing analysis applications. Therefore changes in system configuration and resource availability during query evaluation are not unexpected and can result in deteriorated query performance. Considering a parallel query evaluation environment, we propose dynamic reconfiguration of sub-optimal parallel query execution plans (QEPs) to adapt QEPs to the environment as well as to refined estimates of data and query characteristics. To ensure correct query evaluation in the face of modification of the QEP, we propose an algorithm to coordinate the steps in a reconfiguration. We ...