Designing and prototyping data-intensive applications in the Logresand Algres programming environment

The authors present an environment and a methodology for the design and rapid prototyping of data-intensive software applications, i.e., applications which perform substantial retrieval and update activity on persistent data. In the approach, the application is formally specified using Logres, a database language which combines object-oriented data modeling and rule-based programming. These specifications are translated into Algres, an extended relational algebra, thus yielding a rapid executable prototype. Algres programs embedded into a conventional programming language interface may be converted to conventional programs operating on a commercial relational system. This methodology helps automate the conversion from declarative requirements to imperative code, performing several tasks fully automatically and reducing the probability of human errors, while integrity constraints and application specifications are expressed in a declarative language, at a very high level of abstraction     

Support environment for active rule design

The lack of tools for rule generation, analysis, and run-time monitoring appears one of the main obstacles to the widespreading of active database applications. This paper describes a complete tool environment for assisting the design of active rules applications; the tools were developed at Politecnico di Milano in the context of the IDEA Project, a 4-years Esprit project sponsored by the European Commission which was launched in June 1992. We describe tools for active rule generation, analysis, debugging, and browsing; rules are defined in Chimera, a conceptual design model and language for the specification of active rules applications. We also introduce a tool for mapping from Chimera into Oracle, a relational product supporting triggers.Most of the tools described in this paper are fully implemented and currently in operation (beta-testing) within the companies participating to the IDEA Project, with the exception of two of them (called Argonaut-V and Pandora), which will be completed by the end of 1996.Research presented in this paper is supported by Esprit project P6333 IDEA, and by ENEL contract VDS 1/94: Integrity Constraint Management     

Spectroscopic studies of the biosorption of gold(III) by dealginated seaweed waste

Gold biosorption by dealginated seaweed waste has been studied to elucidate the mechanisms of metal uptake from solution. Dealginated seaweed was able to retain up to 1 mmol g(-1) of Au from solution at pH 3. FT-IR showed the presence of carboxylate groups on the surface of the biosorbent; however, the changes observed for the Au-bound samples suggested very little sorption to the carboxyl moieties. Colloidal Au formed on the surface of dealginated seaweed by reduction of Au(III) to Au(0) was observed using ESEM and four different types of particles were clearly identified. The Au distribution matched closely that obtained for S atoms indicating a possible link between these elements. EXAFS measurements showed that colloidal Au is present on the surface of the biosorbent. Evidence of gold reduction from Au(III) to Au(I) and Au(0) was also confirmed by the measured bond distances characteristic of the metal. The coordination number obtained by EXAFS indicated that approximately 75% of the Au on the sample was present in the colloidal form and the remaining Au was bound to S as nearest neighbor. The proposed mechanisms for Au removal from solution are reduction of Au species by components on the surface of the biosorbent to form colloidal metal followed by retention of the ionic Au(I) species at the sulfur containing sites. The results show that dealginated seaweed can be used for the cleanup of gold-containing effluents.     

Providing Flexible Process Support to Project-Centered Learning

While business process definition is becoming more and more popular as an instrument for describing human activities, there is a growing need for software tools supporting business process abstractions to help users organize and monitor their desktop work. Tools are most effective when they embed some knowledge about the process, e.g., in terms of the typical activities required by the process, so that users can execute the activities without having to define them. Tools must be lightweight and flexible, so as to enable users to create or change the process as soon as there is a new need. In this article, we first describe an application-independent approach to flexible process support by discussing the abstractions required for modeling, creating, enacting, and modifying flexible processes. Then, we show our approach at work in the context of project-centered learning. In this application, learners are challenged to perform concrete tasks in order to master specific subjects; in doing so, they have to conduct significant projects and cope with realistic (or even real-life) working conditions and scenarios. Often, students are geographically dispersed or under severe timing constraints, because these activities intertwine with their normal university activity. As a result, they need communication technology in order to interact and workflow technology in order to organize their work. The developed platform provides a comprehensible, e-learning-specific set of activities and process templates, which can be combined through a simple Web interface into project-centered collaboration processes. We discuss how the general paradigm of flexible processes was adapted to the learning concept, implemented, and experienced by students.     

What you always wanted to know about Datalog (and never dared toask)

Datalog, a database query language based on the logic programming paradigm, is described. The syntax and semantics of Datalog and its use for querying a relational database are presented. Optimization methods for achieving efficient evaluations of Datalog queries are classified, and the most relevant methods are presented. Various improvements of Datalog currently under study are discussed, and what is still needed in order to extend Datalog's applicability to the solution of real-life problems is indicated     

Evaluating recursive queries in distributed databases

The execution of logic queries in a distributed database environment is studied. Conventional optimization strategies, such as the early evaluation of selection conditions and the clustering of processing to manipulate and exchange large sets of tuples, are redefined in view of the additional difficulties due to logic queries, in particular to recursive rules. In order to allow efficient processing of these logic queries, several program transformation techniques that attempt to minimize distribution costs based on the idea of semijoins and generalized semijoins in conventional databases are presented. Although local computation of semijoins is not possible for the general case, classes of programs are indicated for which these transformations succeed in producing set-oriented computation. Processes evaluating the recursive program in a distributed network are described, and an efficient method for testing the termination of the computation is developed. The approach is compared with sequential as well as dataflow-oriented evaluation     

The formation and evolution of oxide particles in oxide-dispersion-strengthened ferritic steels during processing

The fabrication of oxide-dispersion-strengthened (ODS) steels is a multi-stage process involving powder ball milling, degassing and consolidation by hot isostatic pressing. Y is introduced by mechanical alloying (MA) with either Y₂O₃ or Fe₂Y so a high density of Y–Ti–O-based oxide nanoparticles is formed. The evolution of ～2 nm oxide nanoparticles and larger >5 nm grain boundary oxides has been studied at each step of the processing. The nanoparticle dispersions produced in material MA with Fe₂Y were comparable to those produced by alloying with Y₂O₃. Hence the majority of oxygen which forms the nanoparticles must be incorporated from the atmosphere during MA, rather than being introduced via the alloying additions. During mechanical alloying, a high density of subnanometer particles are formed (2.5 ± 0.5 × 10²⁴ m^?3). The oxygen content of the nanoparticles decreases slightly on annealing, and then the composition of the nanoparticles remains constant throughout subsequent processing stages. The nanoparticle size increases during processing up to ～2 nm radius, while the number density decreases to 4 ± 0.5 × 10²³ m^?3. Grain boundary oxides (>5 nm) have a Ti–Cr–O-rich shell, and contain no Y before consolidation, but have similar core composition to the matrix nanoparticles after consolidation. The formation of the larger grain boundary oxides is shown to take place during the degassing and consolidation stages, and this occurs at the expense of the nanoparticles in the matrix. This work provides a mechanistic understanding of the importance of controlling the oxygen content in the powder during MA, and the resulting impact on the formation of the ODS microstructure.     

A survey of parallel execution strategies for transitive closure and logic programs

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods. Recommended by: Patrick Valduriez