Time-referenced data are pervasive in most real-world databases. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query optimization and evaluation mechanisms must be provided, either within the DBMS proper or as a source level translation from temporal queries to conventional SQL. This paper proposes a new approach: using a middleware component on top of a conventional DBMS. This component accepts temporal SQL statements and produces a corresponding query plan consisting of algebraic as well as regular SQL parts. The algebraic parts are processed by the middleware, while the SQL parts are processed by the DBMS. The middleware uses performance feedback from the DBMS to adapt its partitioning of subsequent queries into middleware and DBMS parts. The paper describes the architecture and implementation of the temporal middleware component, termed TANGO, which is based on the Volcano extensible query optimizer and the XXL query processing library. Experiments with the system demonstrate the utility of the middleware's internal processing capability and its cost-based mechanism for apportioning the processing between the middleware and the underlying DBMS.

Most real-world databases contain substantial amounts of time-referenced, or temporal, data. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query representation, optimization, and processing mechanisms must be provided. This paper presents a foundation for query optimization that integrates conventional and temporal query optimization and is suitable for both conventional DBMS architectures and ones where the temporal support is obtained via a layer on top of a conventional DBMS. This foundation captures duplicates and ordering for all queries, as well as coalescing for temporal queries, thus generalizing all existing approaches known to the authors. It includes a temporally extended relational algebra to which SQL and temporal SQL queries may be mapped, six types of algebraic equivalences, concrete query transformation rules that obey different equivalences, a procedure for determining which types of transformation rules are applicable for optimizing a query, and a query plan enumeration algorithm.

Most real-world database applications contain a substantial portion of time references, or temporal data. Recent advances in temporal query languages show that such database applications could benefit substantially from built-in 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 coalescence for temporal queries, this foundation formalizes and generalizes existing approaches.

Most data managed by existing, real-world database applications is time referenced. Often, two temporal aspects of data are of interest, namely valid time, when data is true in the mini-world, and transaction time, when data is current in the database, resulting in so-called bitemporal data. Like spatial data, bitemporal data thus has associated two-dimensional regions. Such data is in part naturally now-relative: some data is true until the current time, and some data is part of the current database state. So, unlike for spatial data, bitemporal data regions may grow continuously. Existing indices, e.g., B/sup +/and R-trees, typically do not contend well with even small amounts of now-relative data. In contrast, the 4-R index presented in the paper is capable of indexing general bitemporal data efficiently. The different kinds of growing data regions are transformed into stationary regions, which are then indexed by R*-trees. Queries are also transformed to counter the data transformations, yielding a technique with perfect precision and recall. Performance studies indicate that the technique is competitive with the best existing index; and unlike this existing index, the new technique does not require extension of the DBMS kernel.

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. The 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.