Abstract Programmers are increasingly interested in developing applications that can be used internationally. Part of the internationalization effort is the ability to engineer applications to use dates and times that conform to local calendars yet can inter‐operate with dates and times in other calendars, for instance between the Gregorian and Islamic calendars. $\tau$ ZAMAN is a system that provides a natural language‐ and calendar‐independent framework for integrating multiple calendars. $\tau$ ZAMAN performs ‘runtime‐binding’ of calendars and language support. A running $\tau$ ZAMAN system dynamically loads calendars and language support tables from XML‐formatted files. Loading a calendar integrates it with other, already loaded calendars, enabling users of $\tau$ ZAMAN to add, compare, and convert times between multiple calendars. $\tau$ ZAMAN also provides a flexible, calendar‐independent framework for parsing temporal literals. Literals can be input and output in XML or plain text, using user‐defined formats, and in different languages and character sets. Finally, $\tau$ ZAMAN is a client/server system, enabling shared access to calendar servers spread throughout the Web. This paper describes the architecture of $\tau$ ZAMAN and experimentally quantifies the cost of using a calendar server to translate and manipulate dates. Copyright © 2006 John Wiley & Sons, Ltd.

Normal forms play a central role in the design of relational databases. Several normal forms for temporal relational databases have been proposed. These definitions are particular to specific temporal data models, which are numerous and incompatible. The paper attempts to rectify this situation. We define a consistent framework of temporal equivalents of the important conventional database design concepts: functional dependencies, primary keys, and third and Boyce-Codd normal forms. This framework is enabled by making a clear distinction between the logical concept of a temporal relation and its physical representation. As a result, the role played by temporal normal forms during temporal database design closely parallels that of normal forms during conventional database design. These new normal forms apply equally well to all temporal data models that have timeslice operators, including those employing tuple timestamping, backlogs, and attribute value timestamping. As a basis for our research, we conduct a thorough examination of existing proposals for temporal dependencies, keys, and normal forms. To demonstrate the generality of our approach, we outline how normal forms and dependency theory can also be applied to spatial and spatiotemporal databases.

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 the inequality predicates 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. The authors 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. Performance comparisons between the partition-based algorithm and other evaluation methods are provided. While the paper focuses on the important valid-time natural join, the techniques presented are also applicable to other valid-time joins.>

Spatial and temporal databases are important and well-established sub-disciplines of database research. Some 350 papers in temporal databases have appeared, authored by almost 300 researchers. The literature on spatial databases is also substantial; the bibliography of Samet's landmark book on spatial data structures lists 823 references.

A conceptual temporal data model that captures the time-dependent semantics of data while permitting multiple data models at the representation level is described. A conceptual notion of a bitemporal relation in which tuples are stamped with sets of two-dimensional chronons in transaction-time/valid-time space is defined. A tuple-timestamped first normal form representation is introduced to show how the conceptual bitemporal data model is related, by means of snapshot equivalence, with representational models. Querying within the two-level framework is discussed. An algebra is defined at the conceptual level and mapped to the sample representational model in such a way that new operators compute equivalent results for different representations of the same conceptual bitemporal relation.>