A framework for selecting appropriate remotely sensed data dimensions for environmental monitoring and management

Two major factors are likely to impact the utilisation of remotely sensed data in the near future: (1)an increase in the number and availability of commercial and non-commercial image data sets with a range of spatial, spectral and temporal dimensions, and (2) increased access to image display and analysis software through GIS. A framework was developed to provide an objective approach to selecting remotely sensed data sets for specific environmental monitoring problems. Preliminary applications of the framework have provided successful approaches for monitoring disturbed and restored wetlands in southern California.

A framework for specification-based testing

Test templates and a test template framework are introduced as useful concepts in specification-based testing. The framework can be defined using any model-based specification notation and used to derive tests from model-based specifications-in this paper, it is demonstrated using the Z notation. The framework formally defines test data sets and their relation to the operations in a specification and to other test data sets, providing structure to the testing process. Flexibility is preserved, so that many testing strategies can be used. Important application areas of the framework are discussed, including refinement of test data, regression testing, and test oracles.

Reliability factors in business software: volatility, requirements and end-users

Many business-oriented software applications are subject to frequent changes in requirements. This paper shows that, ceteris paribus, increases in the volatility of system requirements decrease the reliability of software. Further, systems that exhibit high volatility during the development phase are likely to have lower reliability during their operational phase. In addition to the typically higher volatility of requirements, end-users who specify the requirements of business-oriented systems are usually less technically oriented than people who specify the requirements of compilers, radar tracking systems or medical equipment. Hence, the characteristics of software reliability problems for business-oriented systems are likely to differ significantly from those of more technically oriented systems.

A framework for table driven testing of Java classes

With the advent of object-oriented languages and the portability of Java, the development and use of class libraries has become widespread. Effective class reuse depends on class reliability which in turn depends on thorough testing. This paper describes a class testing approach based on modeling each test case with a tuple and then generating large numbers of tuples to thoroughly cover an input space with many interesting combinations of values. The testing approach is supported by the Roast framework for the testing of Java classes. Roast provides automated tuple generation based on boundary values, unit operations that support driver standardization, and test case templates used for code generation. Roast produces thorough, compact test drivers with low development and maintenance cost. The framework and tool support are illustrated on a number of non-trivial classes, including a graphical user interface policy manager. Quantitative results are presented to substantiate the practicality and effectiveness of the approach. Copyright (C) 2002 John Wiley Sons, Ltd.

The role of coordination analysis in software integration projects

What sort of component coordination strategies emerge in a software integration process? How can such strategies be discovered and further analysed? How close are they to the coordination component of the envisaged architectural model which was supposed to guide the integration process? This paper introduces a framework in which such questions can be discussed and illustrates its use by describing part of a real case-study. The approach is based on a methodology which enables semi-automatic discovery of coordination patterns from source code, combining generalized slicing techniques and graph manipulation

Prototyping Behavioural Specifications in the.NET framework

Over the last decade, software architecture emerged as a critical design step in Software Engineering. This encompassed a shift from traditional programming towards the deployment and assembly of independent components. The specification of the overall system structure, on the one hand, and of the interactions patterns between its components, on the other, became a major concern for the working developer. Although a number of formalisms to express behaviour and supply the indispensable calculational power to reason about designs, are available, the task of deriving architectural designs on top of popular component platforms has remained largely informal. This paper introduces a systematic approach to derive, from behavioural specifications written in Ccs, the corresponding architectural skeletons in the Microsoft .Net framework in the form of executable C] code. Such prototyping process is automated by means of a specific tool developed in Haskell

BAM framework for OMS using open source technologies

No início da década de 90, as empresas começaram a sentir a necessidade de melhorar o acesso à informação das suas actividades para auxiliar na tomada de decisões. Desta forma, no mundo da informática, emergiu o sector Business Intelligence (BI) composto inicialmente por data warehousing e ferramentas de geração de relatórios. Ao longo dos anos o conceito de BI evoluiu de acordo com as necessidades empresariais, tornando a análise das actividades e do desempenho das organizações em aspectos críticos na gestão das mesmas. A área de BI abrange diversos sectores, sendo o de geração de relatórios e o de análise de dados aqueles que melhor preenchem os requisitos pretendidos no controlo de acesso à informação do negócio e respectivos processos. Actualmente o tempo e a informação são vantagens competitivas e por esse mesmo motivo as empresas estão cada vez mais preocupadas com o facto de o aumento do volume de informação estar a tornar-se insustentável na medida que o tempo necessário para processar a informação é cada vez maior. Por esta razão muitas empresas de software, tais como Microsoft, IBM e Oracle estão numa luta por um lugar neste mercado de BI em expansão. Para que as empresas possam ser competitivas, a sua capacidade de previsão e resposta às necessidades de mercado em tempo real é requisito principal, em detrimento da existência apenas de uma reacção a uma necessidade que peca por tardia. Os produtos de BI têm fama de trabalharem apenas com dados históricos armazenados, o que faz com que as empresas não se possam basear nessas soluções quando o requisito de alguns negócios é de tempo quase real. A latência introduzida por um data warehouse é demasiada para que o desempenho seja aceitável. Desta forma, surge a tecnologia Business Activity Monitoring (BAM) que fornece análise de dados e alertas em tempo quase real sobre os processos do negócio, utilizando fontes de dados como Web Services, filas de mensagens, etc. O conceito de BAM surgiu em Julho de 2001 pela organização Gartner, sendo uma extensão orientada a eventos da área de BI. O BAM define-se pelo acesso em tempo real aos indicadores de desempenho de negócios com o intuito de aumentar a velocidade e eficácia dos processos de negócio. As soluções BAM estão a tornar-se cada vez mais comuns e sofisticadas.

Framework genérica de business intelligence para suporte financeiro

Actualmente, não existem ferramentas open source de Business Intelligence (BI) para suporte à gestão e análise financeira nas empresas, de acordo com o sistema de normalização contabilística (SNC). As diferentes características de cada negócio, juntamente com os requisitos impostos pelo SNC, tornam complexa a criação de uma Framework financeira genérica, que satisfaça, de forma eficiente, as análises financeiras necessárias à gestão das empresas. O objectivo deste projecto é propor uma framework baseada em OLAP, capaz de dar suporte à gestão contabilística e análise financeira, recorrendo exclusivamente a software open source na sua implementação, especificamente, a plataforma Pentaho. Toda a informação contabilística, obtida através da contabilidade geral, da contabilidade analítica, da gestão orçamental e da análise financeira é armazenada num Data mart. Este Data mart suportará toda a análise financeira, incluindo a análise de desvios orçamentais e de fluxo de capitais, permitindo às empresas ter uma ferramenta de BI, compatível com o SNC, que as ajude na tomada de decisões.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Convex optimization framework for intermediate deadline assignment in soft and hard real-time distributed systems

It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation.

A framework for the development of parallel and distributed real-time embedded systems

Embedded real-time applications increasingly present high computation requirements, which need to be completed within specific deadlines, but that present highly variable patterns, depending on the set of data available in a determined instant. The current trend to provide parallel processing in the embedded domain allows providing higher processing power; however, it does not address the variability in the processing pattern. Dimensioning each device for its worst-case scenario implies lower average utilization, and increased available, but unusable, processing in the overall system. A solution for this problem is to extend the parallel execution of the applications, allowing networked nodes to distribute the workload, on peak situations, to neighbour nodes. In this context, this report proposes a framework to develop parallel and distributed real-time embedded applications, transparently using OpenMP and Message Passing Interface (MPI), within a programming model based on OpenMP. The technical report also devises an integrated timing model, which enables the structured reasoning on the timing behaviour of these hybrid architectures.

RadiaLE: A framework for designing and assessing link quality estimators in wireless sensor networks

Stringent cost and energy constraints impose the use of low-cost and low-power radio transceivers in large-scale wireless sensor networks (WSNs). This fact, together with the harsh characteristics of the physical environment, requires a rigorous WSN design. Mechanisms for WSN deployment and topology control, MAC and routing, resource and mobility management, greatly depend on reliable link quality estimators (LQEs). This paper describes the RadiaLE framework, which enables the experimental assessment, design and optimization of LQEs. RadiaLE comprises (i) the hardware components of the WSN testbed and (ii) a software tool for setting-up and controlling the experiments, automating link measurements gathering through packets-statistics collection, and analyzing the collected data, allowing for LQEs evaluation. We also propose a methodology that allows (i) to properly set different types of links and different types of traffic, (ii) to collect rich link measurements, and (iii) to validate LQEs using a holistic and unified approach. To demonstrate the validity and usefulness of RadiaLE, we present two case studies: the characterization of low-power links and a comparison between six representative LQEs. We also extend the second study for evaluating the accuracy of the TOSSIM 2 channel model.