XML has reached a widespread diffusion as a language for representing nearly any kind of data source, from relational databases to digital movies. Due to the growing interest toward XML, many tools for storing, processing, and querying XML data have appeared in the last two years. Three main problems affect XML query processing: path expression evaluation, nested query resolution, and preservation of document order. These issues, which are related to the hierarchical structure of XML and to the features of current XML query languages, require compile-time as well as run-time solutions. This paper describes a query algebra for XML data. The main purpose of this algebra, which forms the basis for the Xtasy database management system, is to combine good optimization properties with a good expressive power that allows it to model significant fragments of current XML query languages; in particular explicit support is given to efficient path expression evaluation, nested query resolution, and order preservation.

Abstract XML is nowadays considered the standard meta‐language for document markup and data representation. XML is widely employed in Web‐related applications as well as in database applications, and there is also a growing interest for it by the literary community to develop tools for supporting document‐oriented retrieval operations. The purpose of this article is to show the basic new requirements of this kind of applications and to present the main features of a typed query language, called Tequyla‐TX, designed to support them.

To deal with the evolution of data and applications and with the existence of multiple views for the same data, the object data model needs to be extended with two different sets of operations: object extension operations, to allow an object to dynamically change its type; and object viewing operations, to allow an object to be seen as if it had a different structure. Object extension and object viewing operations are related in that they are both identity-preserving operations, but different in that object extension may modify the behavior of the original object while object viewing creates a new view for the original object without modifying its behavior. A set of object viewing operations is defined in the context of a statically and strongly typed database programming language which supports objects with roles, and the relationships with object extension and role mechanisms are discussed. We then show how the object viewing operations can be used to give the semantics of a higher level mechanism to define views for object databases. Examples of the use of these operations are given with reference to the prototype implementation of the language Galileo 97.

Galileo, a programming language for database applications, is presented. Galileo is a strongly-typed, interactive programming language designed specifically to support semantic data model features (classification, aggregation, and specialization), as well as the abstraction mechanisms of modern programming languages (types, abstract types, and modularization). The main contributions of Galileo are (a) a flexible type system to model database structure and semantic integrity constraints; (b) the inclusion of type hierarchies to support the specialization abstraction mechanisms of semantic data models; (c) a modularization mechanism to structure data and operations into interrelated units (d) the integration of abstraction mechanisms into an expression-based language that allows interactive use of the database without resorting to a new stand-alone query language. Galileo will be used in the immediate future as a tool for database design and, in the long term, as a high-level interface for DBMSs.

Given r positive integers s1, s2, …, sr with an associated profit pi two problems are at the root of several interesting applications. The ‘M-partition problem’, that is determining all possible combinations of these numbers which sum to M, and the ‘Knapsack problem’, that is determining a combination of these numbers maximising the pi sum subject to the condition that the si sum is not greater than M. A solution is proposed for both the problems based on a tree search approach which leads to algorithms with simple structure, linear storage requirements and an average computation time lower than other proposed algorithms.

The rectangular cutting stock problem is to determine how to cut a specified number of each of certain types of rectangular pieces out of a given stock of rectangular sheets, the object being to minimise the waste. A special case of this general cutting problem encountered in many practical applications is one in which all cuts have to be accomplished from one edge of the rectangle to the opposite one, i.e. the cut has to be of a ‘guillotine’ type. For such applications the problem can be formulated as a mathematical optimisation program and the optimal solution can be found efficiently in terms of computer time only for problems of medium size. A heuristic method is described for an approximate solution of larger problems, which gives suboptimal results but presents an interesting tradeoff between computing effort and solution optimality.

A problem of relevant interest to some industrial processes is the one of allocating a specified number of two-dimensional regular or irregular generally different pieces on a stock of sheets of finite dimensions, the object being to minimize the amount of waste produced. The problem is present in industrial applications like shipbuilding, clothing manufacturing, or leather cutting. An approach is given where the solution is achieved with a heuristic search method typical of the artificial intelligence discipline. In this paper it is shown by the experimental results that this framework not only allows a simple formulation of the solution but represents an effective technique to obtain solutions competitive with the ones produced by hand.

A method of solving a version of the two-dimensional cutting-stock problem is presented. In this version of the problem one is given a number of rectangular sheets and an order for a specified number of each of certain types of rectangular shapes. The goal is to cut the shapes out of the sheets in such a way as to minimize the waste. However, in many practical applications computation time is also an important economic consideration. For such applications the goal may be to obtain the best solution possible without using excessive amounts of computation time. The method of solution described here avoids exhaustive search procedures by employing an approach utilizing a constrained dynamic programming algorithm to lay out groups of rectangles called strips. This paper also describes the results obtained when the algorithm was tested with some typical rectangular layout problems.