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...

We define formal notions of temporal domain and temporal database, and use them to survey a wide spectrum of temporal query languages. We distinguish between an abstract temporal database and its concrete representations, and accordingly between abstract and concrete temporal query languages. We also address the issue of incomplete temporal information. 1 Introduction A temporal database is a repository of temporal information. A temporal query language is any query language for temporal databases. In this paper we propose a formal notion of temporal database and use this notion in surveying a wide spectrum of temporal query languages. The need to store temporal information arises in many computer applications. Consider, for example, records of various kinds: financial [37], personnel, medical [98], or judicial. Also, monitoring data, e.g., in telecommunications network management [4] or process control, has often a temporal dimension. There has been a lot of research in temporal dat...

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.

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 ...

We examine the problem of processing temporal joins in the presence of indexing schemes. Previous work on temporal joins has concentrated on non-indexed relations which were fully scanned. Given the large data volumes created by the ever increasing time dimension, sequential scanning is prohibitive. This is especially true when the temporal join involves only parts of the joining relations (e.g., a given time interval instead of the whole timeline). Utilizing an index becomes then beneficial as it directs the join to the data of interest. We consider temporal join algorithms for three representative indexing schemes, namely a B+-tree, an R*-tree and a temporal index, the Multiversion B+-tree (MVBT). Both the B+-tree and R*-tree result in simple but not efficient join algorithms because neither index achieves good temporal data clustering. Better clustering is maintained by the MVBT through record copying. Nevertheless, copies can greatly affect the correctness and effectiveness of the join algorithms. We identify these problems and propose efficient solutions and optimizations. An extensive comparison of all index based temporal joins, using a variety of datasets and query characteristics shows that the MVBT based join algorithms are consistently faster. In particular the link-based algorithm has the most robust behavior. In our experiments it showed a ten-fold improvement over the R*-tree joins while it was between six and thirty times faster than the B+-tree joins. 1

. We establish an exact correspondence between temporal logic and a subset of TSQL2, a consensus temporal extension of SQL--92. The translation from temporal logic to TSQL2 developed here enables a user to write high-level queries which can be evaluated against a spaceefficient representation of the database. The reverse translation, also provided, makes it possible to characterize the expressive power of TSQL2. We demonstrate that temporal logic is equal in expressive power to a syntactically defined subset of TSQL2. 1 Introduction In this paper, we bring together two research directions in temporal databases. The first direction is concerned with temporal extensions to calculus-based query languages such as SQL (e.g., [GN93, NA93, Sar93]). The issues addressed include space-efficient storage, effective implementation techniques, and handling large amounts of data. This approach includes the consensus temporal query language TSQL2 [Sno95], whose practical implementations should be fo...

Despite the surge of research in continuous stream processing, there is still a semantical gap. In many cases, continuous queries are formulated in an enriched SQL-like query language without specifying the semantics of such a query precisely enough. To overcome this problem, we present a sound and precisely defined temporal operator algebra over data streams ensuring deterministic query results of continuous queries. In analogy to traditional database systems, we distinguish between a logical and physical operator algebra. While our logical operator algebra specifies the semantics of each operation in a descriptive way over temporal multisets, the physical operator algebra provides adequate implementations in form of stream-to-stream operators. We show that query plans built with either the logical or the physical algebra produce snapshot-equivalent results. Moreover, we introduce a rich set of transformation rules that forms a solid foundation for query optimization, one of the major research topics in the stream community. Examples throughout the paper motivate the applicability of our approach and illustrate the steps from query formulation to query execution.

We present a temporal object data model, query language and system that support temporal database applications. We then show how equivalent temporal constructs and operations could be provided in existing object-oriented database management systems (OODBMS) and describe how we did this in the O2 system. A comparison of the two resulting systems highlights the current limitations to the notions of extensibility supported in existing OODBMS. 1 Introduction There is an on-going debate as to whether it is necessary to extend data models with specific temporal constructs and operations to support temporal databases, or, it is sufficient to model temporal properties using date/time attributes (e. g. [Sno95a]). In the case of object-oriented database systems (OODBMS), the latter case is argued more strongly given the inherent extensibility of these systems. We believe that it is important to study both approaches to determine their relative merits and appreciate how far one can go in support...

Most real-world database applications contain a substantial portion of time-referenced, or temporal, data. Recent advances in temporal query languages show that such database applications could benefit substantially from builtin temporal support in the DBMS. To achieve this, temporal query representation, optimization, and processing mechanisms must be provided. This paper presents a general, algebraic foundation for query optimization that integrates conventional and temporal query optimization and is suitable for providing temporal support both via a stand-alone temporal DBMS and via a layer on top of a conventional DBMS. By capturing duplicate removal and retention and order preservation for all queries, as well as coalescing for temporal queries, this foundation formalizes and generalizes existing approaches.

Audio and video data represent streams of data with inherent temporal properties. In this paper we consider a video database as a collection of partial ordered sets where temporal relationships exist between elements from the same video stream. Video production introduces dependencies between different time coordinate systems. In this paper we give a formal definition of the contents of a video database. We also define mapped video object sets and operations on such sets that can be used for querying the temporal properties of video data and that can be used for mapping video objects between different time coordinate systems. At last, we illustrate how the proposed foundation can be used in searching and browsing.