A new variation of Overlapping B+-trees is presented, which provides efficient indexing of transaction time and keys in a two dimensional key-time space. Modification operations (i.e. insertions, deletions and updates) are allowed at the current version, whereas queries are allowed to any temporal version, i.e. either in the current or in past versions. Using this structure, snapshot and range-timeslice queries can be answered optimally. However, the fundamental objective of the proposed method is to deliver efficient performance in case of a general pure-key query (i.e. "history of a key"). The trade-off is a small increase in time cost for version operations and storage requirements.

Overlapping Linear Quadtrees is a structure suitable for storing consecutive raster images according to transaction time (a database of evolving images). This structure saves considerable space without sacrificing time performance in accessing every single image. Moreover, it can be used for answering efficiently window queries for a number of consecutive images (spatio-temporal queries). In this paper, we present three such temporal window queries: strict containment, border intersect and cover. Besides, based on a method of producing synthetic pairs of evolving images (random images with specified aggregation) we present empirical results on the I/O performance of these queries.

In this paper we present an indexing structure for ranges. The main idea is to map a bounded range to a point in one-dimensional space, using a standard B + -tree to index such a point. The indexing structure is storage-wise efficient, requiring O(N ), where N is the number of indexed ranges. Its maintainance is also efficient, requiring O(log N ) accesses per update. We also show that it is able to efficaciously answer a number of different queries, requiring, for instance, O(logN + L) I/Os to answer an intersection type of query, where L is the length of the range given in the query. An analytical performance study is also presented. Finally, we point out two immediate applications of this indexing approach, which are (1) answering stabbing queries and (2) the indexing of a valid-time (historical) database. Contents 1 Introduction 4 2 The MAP21 approach 4 3 Answering Range-based Queries 6 3.1 Location Query : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :...

Indexing temporal databases is a difficult problem because of their size and complexity. Several approaches have been presented in the literature regarding indexing of historical databases (holding valid time only) or rollback databases (holding transaction time only.) We present an indexing structure to index a bitemporal database. Such structure is based on two trees, one indexing valid time and the other transaction time, which share the leaves and thus save substantial space. Algorithms to satisfy a proposed a minimum set of bitemporal queries, as well as its I/O requirements, are presented. Contents 1 Introduction 3 2 Previous Research 4 3 Indexing Bitemporal Databases 5 3.1 Definitions and Assumptions of the Bitemporal Model : : : : : : : : : : : : : 5 3.2 An Indexing Structure for Bitemporal Data : : : : : : : : : : : : : : : : : : 7 3.3 An Example : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8 4 Feasibility and Algorithms 9 4.1 Basic Algorithms ...

Indexing temporal databases is a difficult problem because of their size and complexity. Several approaches have been presented in the literature regarding indexing of historical databases (holding valid time only) or rollback databases (holding transaction time only,) but none have addressed the issue of indexing a bitemporal database (holding both valid time and transaction time.) The goal of this paper is to present a new indexing structure for bitemporal databases. We also extend the set of single--dimensional Canonical Queries by Salzberg and Tsotras to bitemporal databases and use this new set, which we call Bitemporal Canonical Queries, to show the feasibility of the new index structures we propose. Contents 1 Introduction 3 2 Previous Research 4 3 Bitemporal Canonical Queries 6 3.1 Analysis of Previous Research Under Canonical Queries : : : : : : : : : : : 9 4 Indexing Bitemporal Databases 10 4.1 Definitions and Assumptions of the Bitemporal Model : : : : : : : : : : : : ...

A data warehouse provides strong capabilities for answering complex decision support queries. In particular, it is often desirable to query relations as they existed at some point in the past. Much of the research in temporal databases, materialized views, and snapshot databases has some bearing on this concept. In particular, differential files provide a means for storing past versions of relations while conserving storage space. Queries involving these differential files can be optimized in a variety of ways. 1 Introduction Data warehouses are designed to help create and answer complex queries by organizing varied and potentially distributed data in a common environment. Most of the time, the primary source of the data is not the warehouse itself. Instead, the primary source is some sort of legacy transactional system. These legacy systems may not lend themselves well to complex queries. One of the main difficulties in using transactional systems for decision support is that the dat...