A review of Bayesian belief networks in ecosystem service modelling

A wide range of quantitative and qualitative modelling research on ecosystem services (ESS) has recently been conducted. The available models range between elementary, indicator-based models and complex process-based systems. A semi-quantitative modelling approach that has recently gained importance in ecological modelling is Bayesian belief networks (BBNs). Due to their high transparency, the possibility to combine empirical data with expert knowledge and their explicit treatment of uncertainties, BBNs can make a considerable contribution to the ESS modelling research. However, the number of applications of BBNs in ESS modelling is still limited. This review discusses a number of BBN-based ESS models developed in the last decade. A SWOT analysis highlights the advantages and disadvantages of BBNs in ESS modelling and pinpoints remaining challenges for future research. The existing BBN models are suited to describe, analyse, predict and value ESS. Nevertheless, some weaknesses have to be considered, including poor flexibility of frequently applied software packages, difficulties in eliciting expert knowledge and the inability to model feedback loops.     

Elicitator: An expert elicitation tool for regression in ecology

Expert elicitation is the process of retrieving and quantifying expert knowledge in a particular domain. Such information is of particular value when the empirical data is expensive, limited or unreliable. This paper describes a new software tool, called Elicitator, which assists in quantifying expert knowledge in a form suitable for use as a prior model in Bayesian regression. Potential environmental domains for applying this elicitation tool include habitat modelling, assessing detectability or eradication, ecological condition assessments, risk analysis and quantifying inputs to complex models of ecological processes. The tool has been developed to be user-friendly, extensible and facilitate consistent and repeatable elicitation of expert knowledge across these various domains. We demonstrate its application to elicitation for logistic regression in a geographically based ecological context. The underlying statistical methodology is also novel, utilizing an indirect elicitation approach to target expert knowledge on a case-by-case basis. For several elicitation sites (or cases), experts are asked simply to quantify their estimated ecological response (e.g. probability of presence), and its range of plausible values, after inspecting (habitat) covariates via GIS.     

On the application of model-driven engineering in data reengineering

Model-Driven Engineering (MDE) emphasizes the systematic use of models to improve software productivity and some aspects of the software quality such as maintainability or interoperability. Model-driven techniques have proven useful not only as regards developing new software applications but also the reengineering of legacy systems. Models and metamodels provide a high-level formalism with which to represent artefacts commonly manipulated in the different stages of a software evolution process (e.g., a software migration) while model transformation allows the automation of the evolution tasks to be performed. Some approaches and experiences of model-driven software reengineering have recently been presented but they have been focused on the code while data reengineering aspects have been overlooked. The objective of this work is to assess to what extent data reengineering processes could also take advantage of MDE techniques.The article starts by characterising data-reengineering in terms of the tasks involved. It then goes on to state that MDE is particularly amenable as regards addressing the tasks previously identified. We present an MDE-based approach for the reengineering of data whose purpose is to improve the quality of the logical schema in a relational data migration scenario. As a proof of concept, the approach is illustrated for two common problems in data re-engineering: undeclared foreign keys and disabled constraints. This approach is organised following the three stages of a software reengineering process: reverse engineering, restructuring and forward engineering. We show how each stage is implemented by means of model transformation chains. A running example is used to illustrate each stage of the process throughout the article. The approach is validated with a real widely-used database. An assessment of the application of MDE in each stage is then presented, and we conclude by identifying the main benefits and drawbacks of using MDE in data reengineering.     

Model-based reasoning: a principled approach for software engineering

The software engineering industry suffers from almost unmanageable complexity both in the products it produces and in the processes of production. One of the current shortcomings in the software production process is the weakness of the models used. This paper makes observations on the role of knowledge in engineering and examines the central role of models and simulation. We develop an argument for the application of certain new forms of modelling methods in software engineering in order to impose more discipline and give a principled framework for building models that can support the software life-cycle. The concept of a model is examined in depth and different characteristics and types of model are defined. This introduces the relatively new concept of qualitative models and their use in the field known as model-based reasoning. Unlike previous knowledge-based methods, model-based reasoning has several important advantages. Although very few model-based software projects exist, we illustrate how this approach can be developed by drawing on applications from traditional engineering. It is argued that, because qualitative modelling offers great power for addressing the issue of complexity, such models have considerable potential as high-level abstractions of software products. These could form the core of tools for the management and support of the software development process through the whole product life-cycle.     

Reusing knowledge in embedded systems modelling

Model‐based design is a promising technique to improve the quality of software and the efficiency of the software development process. We are investigating how to efficiently model embedded software and its environment to verify the requirements for the system controlled by the software. The software environment consists of mechanical, electrical and other parts; modelling it involves learning how these parts work, deciding what is relevant to model and how to model it. It is not possible to fully automate these steps. There are general guidelines, but given that every modelling problem differs, much is left to the modeller's own preference, background and experience. Still, when the next generation of a system is designed, the new system will have common elements with its previous version. Therefore, lessons learned from the current model could inform future models. We propose a framework for identifying the non‐formal elements of knowledge, insights and a model itself, which can support modelling of the next system generation. We will present the application of our framework on an action research case – modelling mechanical parts of a paper‐inserting machine.     

CITY DRAIN © – An open source approach for simulation of integrated urban drainage systems

In the last years design procedures of urban drainage systems have shifted from end of pipe design criteria to ambient water quality approaches requiring integrated models of the system for evaluation of measures. Emphasis is put on the improvement of the receiving water quality and the overall management of river basins, which is a core element of the Water Framework Directive (WFD) as well.Typically, it is not necessary to model the whole variety of effects on the receiving water but to focus on the few dominating ones. Only pollutants and processes that have a direct and significant influence on the selected impacts need to be described quantitatively, whereas all other processes can be neglected. Hence, pragmatism is required to avoid unnecessary complexity of integrated models. This is as well true for software being used in daily engineering work, requiring simplicity in handling and a certain flexibility to be adjusted for different scenarios.CITY DRAIN © was developed to serve these needs. Therefore it was developed in the Matlab/Simulink © environment, enabling a block wise modelling of the different parts of the urban drainage system (catchment, sewer system, storage devises, receiving water, etc.). Each block represents a system element (subsystem) with different underlying modelling approaches for hydraulics and mass transport. The different subsystems can be freely arranged and connected to each other in order to describe an integrated urban drainage system. The open structure of the software allows to add own blocks and/or modify blocks (and underlying models) according to the specific needs.The application of CITY DRAIN is shown within the integrated modelling case study Vils/Reutte. Further additional applications for CITY DRAIN, including batch simulations, real time control (RTC) and model based predictive control (MBPC) are presented and discussed.     

Model-based expert system to automatically adapt milling forces in Pareto optimal multi-objective working points

The objective of this paper is to present an open and modular expert rule-based system in order to automatically select cutting parameters in milling operations. The knowledge base of the system presents considerations of stability, machine drives efficiency and restrictions while adaptively controlling milling forces in suitable working points. Moreover, a novel classical cost function has been conceived and constructed to Pareto-optimise cutting parameters subjected to multi-objective purposes, namely: tool-life, surface roughness, material remove rate and stability rate parameter. Different Pareto optimal front solutions can be obtained modulating the weighting factors of the cost function. Additional rules have been added in order to manually and/or automatically modulate this cost function. Furthermore, a database which relates weighting factors, cutting conditions and cost function variables is produced for learning purposes. Chatter detection and suppression system automatically feedback to the system to take into account non-modelled disturbances. Finally, since the knowledge of the system is basically obtained from mathematical models, the possibility of combining experience and knowledge from expert engineers and operators is included. In this way, best practice from mathematical modelling and expert engineers and operators is joined in one system obtaining a full, automated system combining the best of each world.As a result, the expert rule-based system selects Pareto optimal cutting conditions for a broad range of milling processes, sorting out automatically different problems such as chatter vibrations, incorporating model reference adaptive control (MRAC) of forces. This procedure is intuitive, being executed in the same way as a human expert would do and it provides the possibility to interact with expert engineers and operators in order to take into account their experience and knowledge. Finally, the expert system is designed in modular form allowing incorporating new functionalities in rule based forms to them or just adding new modules to improve the performance of the milling system.     

Development of a geometric modelling strategy for roll pass optimal design

Roll pass design is one of the most important tasks in shape rolling operations that are employed to provide raw materials with appropriate cross-section profiles for various industrial applications. Currently, many approaches, such as experience-based trial-and-error strategies, finite element methods, and expert systems, are applied to improve both quality and efficiency of roll pass design. However, due to lack of a flexible geometrical modelling strategy, the application of extant approaches is largely limited. This study attempts to develop a novel approach for generic geometrical modelling to support optimal design of roll passes. Features of the proposed model are analysed to support its application. Furthermore, a parameters estimation approach based on genetic algorithm is also developed to facilitate the transformation between the generic model and other geometrical models, as well as to improve its flexibility and applicability. The results from the case study presented in the paper indicate that the new model is more flexible and efficient, and that the parameters estimation approach also can achieve high transformation accuracy and efficiency.     

软件体系结构层次的软件适应性预测模型

结合经验数据和专家知识,基于贝叶斯网建立了软件体系结构层次的结构特征、变化原因与软件适应性之间的因果关联模型,即软件体系结构层次的软件适应性预测模型,扩展贝叶斯网学习算法解决了该预测模型中较弱因果关系的发现问题.最后给出应用预测模型在软件体系结构层次上评估软件适应性的方法和实例.     

Eliciting and modelling expert knowledge

This paper evaluates the usefulness of various psychological techniques that can be utilized to elicit and model expert knowledge for subsequent representation in rule-based expert systems. Interviewing, protocol analysis and multidimensional scaling are described and evaluated as complementary methods of knowledge elicitation. In addition ‘context-focusing’ and card-sorting are introduced as short-cut methods for the knowledge engineer's ‘tool box’.It is argued that expert knowledge about uncertainty can be represented as subjective probabilities and that these assessments can (and therefore should) be checked for consistency and coherence as a pre-condition for realism.Finally, the issue of whether it is possible to improve upon expert judgement is discussed and evidence is reviewed which shows that, in repetitive decision-making situations, statistical models of the expert can out-perform the expert on whom the models are based. Statistical modelling has a valid but limited application as a replacement for expert judgement.     

Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use

Information technology has recently become the medium in which much professional office work is performed. This change offers an unprecedented opportunity to observe and record exactly how that work is performed. We describe our observation and logging processes and present an overview of the results of our long-term observations of a number of users of one desktop application. We then present our method of providing individualized instruction to each user by employing a new kind of user model and a new kind of expert model. The user model is based on observing the individual's behavior in a natural environment, while the expert model is based on pooling the knowledge of numerous individuals. Individualized instructional topics are selected by comparing an individual's knowledge to the pooled knowledge of her peers.     

An application of knowledge based modelling using scripts

Knowledge management (KM) takes an increasingly significant place in the companies. The field of the KM aims to answer the problems of memory within companies by proposing methodologies to formalise know-how during the different steps of production. The KM is a domain with many ramifications and applications. One of them, so called Knowledge Based Engineering, search how to record knowledge from experts to put them in CAD software. Our study relates to the possibility of using the knowledge of an expert in modelling and, more particularly, on the automatic modelling of filling systems in foundry.     

A retraining methodology for enhancing agent intelligence

Data mining has proven a successful gateway for discovering useful knowledge and for enhancing business intelligence in a range of application fields. Incorporating this knowledge into already deployed applications, though, is highly impractical, since it requires reconfigurable software architectures, as well as human expert consulting. In an attempt to overcome this deficiency, we have developed Agent Academy, an integrated development framework that supports both design and control of multi-agent systems (MAS), as well as “agent training”. We define agent training as the automated incorporation of logic structures generated through data mining into the agents of the system. The increased flexibility and cooperation primitives of MAS, augmented with the training and retraining capabilities of Agent Academy, provide a powerful means for the dynamic exploitation of data mining extracted knowledge. In this paper, we present the methodology and tools for agent retraining. Through experimented results with the Agent Academy platform, we demonstrate how the extracted knowledge can be formulated and how retraining can lead to the improvement – in the long run – of agent intelligence.     

Automatic fuzzy-rule assessment and its application to the modelling of nitrogen leaching for large regions

 Diffuse nutrient emissions from agricultural land is one of the major sources of pollution for ground water, rivers and coastal waters. The quantification of pollutant loads requires mathematical modelling of water and nutrient cycles. The deterministic simulation of nitrogen dynamics, represented by complicated highly non-linear processes, requires the application of detailed models with many parameters and large associated data bases. The operation of those models within integrated assessment tools or decision support systems for large regions is often not feasible. Fuzzy rule based modelling provides a fast, transparent and parameter parsimonious alternative. Besides, it allows regionalisation and integration of results from different models and measurements at a higher generalised level and enables explicit consideration of expert knowledge. In this paper an algorithm for the assessment of fuzzy rules for fuzzy modelling using simulated annealing is presented. The fuzzy rule system is applied to simulate nitrogen leaching for selected agricultural soils within the 23687 km² Saale River Basin. The fuzzy rules are defined and calibrated using results from simulation experiments carried out with the deterministic modelling system SWIM. Monthly aggregated time series of simulated water balance components (e.g. percolation and evapotranspiration), fertilization amounts, resulting nitrogen leaching and crop parameters are used for the derivation of the fuzzy rules. The 30-year simulation period was divided into 20 years for training and 10 years for validation, with the latter taken from the middle part of the period. Three specific fuzzy rule systems were created from the simulation experiments, one for each selected soil profile. Each rule system includes 15 rules as well as one prescribed rules from expert knowledge and 7 input variables. The performance of the fuzzy rule system is satisfactory for the assessment of nitrate leaching on annual to long term time steps. The approach allows rapid scenario analysis for large regions and has the potential to become part of decision support systems for generalised integrated assessment of water and nutrients in macroscale regions.     

Using programming expertise for controlling software synthesis

Achieving efficient software implementations requires a great deal of knowledge, intelligence, and expertise on the part of programmers. One way to enhance software productivity is to incorporate the knowledge and skills of expert programmers into software synthesis systems to automate software development processes. Although many software synthesis systems have been developed, automatic control of synthesis remains a difficult problem. Understanding the role of expertise in software synthesis, and making it more explicit, can help us not only to gain autonomy in controlling the synthesis processes but also to better justify the design, implementations, selection of data structures or algorithms employed in constructing code. Our project aims at making synthesis as autonomous as possible by advances in intelligent control mechanisms to reduce user interaction in the synthesizer. In our earlier work, a blackboard control framework for controlling synthesis processes was introduced. This paper describes how the control framework language was designed and how knowledge in the knowledge bases of the framework was acquired and constructed. We present an example that shows how programming expertise can be used to increase the degree of autonomy in synthesis control, in particular by automating the selection of an appropriate data structure implementation.     

A new tool for variable multiple endmember spectral mixture analysis (VMESMA)

Spectral mixture analysis is a widely used method to determine the sub‐pixel abundance of vegetation, soils and other spectrally distinct materials that fundamentally contribute to the spectral signal of mixed pixels. In this paper we present a computing and environmental analysis tool, named VMESMA, which extends the possibilities of conventional spectral unmixing. The basis is the categorization of the scene into different units or scene sub‐areas and software guidance for endmember selection, allowing for a better adaptation of the model to the conditions of the main cover types. For each pixel an individual combination of endmembers may be selected by automated matching to model quality criteria. This hierarchical assessment can incorporate a priori knowledge from different data sources, including information derived from the unmixing results. Based on an iterative feedback process, the unmixing performance may be improved at each stage until an optimum level is reached. VMESMA allows an immediate estimate of the proportions, which is very robust against external factors (e.g. illumination) and canopy shade. An application of VMESMA on hyperspectral data has been conducted to evaluate the possibilities to map residual sludge and sludge derivatives for two consecutive years with changing land surface conditions. The method offered greater flexibility and new possibilities to improve the understanding and modelling of the scene characteristics.     

Good modelling practice in drinking water treatment,applied to Weesperkarspel plant of Waternet

Good modelling practice increases the credibility and impact of the information and insight that modelling aims to generate. It is known to be crucial for model acceptance and it is a necessity to amass a long-term, systematic thorough knowledge base for both science and decision making. This paper shows how ten steps in model development and evaluation can also be applied to numerical modelling of drinking water treatment, using models of drinking water treatment processes of the Weesperkarspel treatment plant of Waternet. The Weesperkarspel plant consists of ozonation, pellet softening, biological activated carbon filtration and slow sand filtration. For the different processes models were developed that were used for operational improvements. The modelling resulted in new insights and knowledge about the treatment processes and improved operation of the processes. From scenario studies for the pellet softening it was concluded that chemical dosing can be diminished when by-pass ratio is increased and that pellet size can be controlled by measuring the difference in pressure guaranteeing fluidisation of the pellet bed. In addition, ozone dosage can be optimised by modelling ozone exposure, bromate formation and biologically degradable natural organic matter (NOM) under varying influent water quality.     

Transformations to Restructure and Re–engineer COBOL Programs

In this paper we describe a COBOL-program restructuring tool currently under development. The tool is constructed using program transformations executed by the TAMPR program transformation system. We discuss the COBOL knowledge embodied in the transformations and how they restructure an example COBOL program developed in the mid–1970s. While the tool is not yet a robust commercial product, early use for restructuring COBOL programs demonstrates the power and flexibility of this transformation–based approach.     

Semantic model for knowledge representation in e-business

Semantic knowledge representation is the key for the development of present intelligent systems. Furthermore, the Semantic Web provide a formal representation and knowledge-driven set of technologies to enable automation of business processes. Despite the new trends on business process automation based on semantic technologies and Web Services, a number of business processes still require human intervention. In this paper we address the problem of ontology-based process representation based on expert’s knowledge, considering both tasks executed by means of web services and tasks executed by persons. In this way, fully-automated and semi-automated processes (guided by a person) could be represented and executed. The proposed model is based on the set of situations that can be identified by an expert in a given process and the actions required in each situation. For validating our approach, a sales supporting system based on the proposed architecture has been developed. The proof-of concept system has been running for a remarkably time, supporting the sales process for non-expert sellers.     

Enhancing development team flexibility in IS projects

Information system development projects face tremendous challenges because of business changes and technology changes. Research has shown that software team flexibility has a positive effect on project outcomes, but specific requirements for enhancing flexibility are lacking. Drawing from the input-mediator-outcome (IMO) team effectiveness framework, this research investigates the contextual inputs and team processes that lead to development team flexibility and how well team flexibility improves project outcomes. A survey was developed to consider a model derived from the IMO framework. One hundred fourteen members of information systems development project teams in China responded to the survey. Partial least squares analysis was used served to analyze the data. Results indicate that a participatory culture and cooperative norms are an effective foundation for improving required processes that include project coordination of the project and knowledge sharing activities. In turn, the improved process performance extends responses to changes in technology and the business climate. The improved flexibility in meeting change is predictive of outcomes related to project performance and quality of the final product.