On-Line Analytical Processing (OLAP) is a powerful method for analysing large data warehouse data. Typically, the data for an OLAP database is collected from a set of data repositories such as e.g. operational databases. This data set is often huge, and it may not be known in advance what data is required and when to perform the desired data analysis tasks. Sometimes it may happen that some parts of the data are only needed occasionally. Therefore, keeping the OLAP database constantly up-to-date is not only a highly demanding task but it also may be overkill in practice.This suggests that in some applications it would be more feasible to form the OLAP cubes only when they are actually needed. We present such a system. As the data sources may well be heterogeneous, we propose an XML language for data collection. Our system also has a facility, where the user may pose a query against a universal OLAP cube using the MDX language. The query is analysed to determine which data is required for the desired OLAP cube.

An On-Line Analytical Processing (OLAP) user often follows a train of thought, posing a sequence of related queries against the data warehouse. Although their details are not known in advance, the general form of those queries is apparent beforehand. Thus, the user can outline the relevant portion of the data posing generalised queries against a cube representing the data warehouse.Since existing OLAP design methods are not suitable for non-professionals, we present a technique that automates cube design given the data warehouse, functional dependency information, and sample OLAP queries expressed in the general form. The method constructs complete but minimal cubes with low risks related to sparsity and incorrect aggregations. After the user has given queries, the system will suggest a cube design. The user can accept it or improve it by giving more queries. The method is also suitable for improving existing cubes using respective real MDX queries.

Abstract This paper is an introduction to natural language interfaces to databases (NLIDBS). A brief overview of the history of NLIDBS is first given. Some advantages and disadvantages of NLIDBS are then discussed, comparing NLIDBS to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems NLIDBS have to confront follows, for the benefit of readers less familiar with computational linguistics. The discussion then moves on to NLIDB architectures, portability issues, restricted natural language input systems (including menu-based NLIDBS), and NLIDBS with reasoning capabilities. Some less explored areas of NLIDB research are then presented, namely database updates, meta-knowledge questions, temporal questions, and multi-modal NLIDBS. The paper ends with reflections on the current state of the art.

A new form of database integrity constraint is introduced which describes the structure present in data when that structure takes the form of a graph, tree or list. This type of integrity constraint is of particular benefit in reducing the overhead when detecting termination in the case of recursive queries.