User responsibility and exception handling in decision support systems

The role of user responsibility in decision support systems (DSS) has been omitted in recent studies of some important issues. One such important issue is exception handling. In this short note, the role of user responsibility in exception handling is examined. The importance of user responsibility is emphasized, a possible approach is outlined which supports user responsibility in exception handling through the consideration of mental models.     

Logic-based formula management strategies in an actuarial consulting system

In many decision support systems, multiple decision methods and models must be combined for solving a complex problem. Expertise is required for selecting, adapting and coordinating appropriate models. This paper describes the design and implementation of a knowledge-based model management system called the Actuarial Consulting System (ACS). The ACS supports actuaries in making pricing decisions in the domain of life insurance. Actuarial knowledge is organized using a graph formalism called Formula Derivation Network (FDN), represented in Prolog as a hierarchy of predicates. On the user level, a Problem Analyzer converts a problem specification by the user into a search problem on the stored collection of FDNs. Using different search strategies, including human expert rules, the Surface Planner generates an efficient solution strategy (sequence of models). At the lowest level, a Plan Executor retrieves or requests model data and issues appropriate function calls to a subroutine library.     

Integrating model management with data management in decision support systems

Because of the increasing complexity of the problems faced by decision makers and the nature of decision support systems, decision models are becoming more and more crucial to their success. In this paper, a general framework for model management, which can integrate model management and data management and handle issues in model management such as model creation, model modification and model use is proposed.     

结构可变更、界面可配置的通用基础信息模型

针对关系数据库设计中潜在逻辑结构变更的需求,利用模板-实例思想,在关系数据库基础上设计实现一套对基础信息统一表达处理模型,以实现新旧逻辑结构数据共存、逻辑结构立即变更立即生效的目的;与通用信息模型相结合,针对广泛自定义输入界面需求,利用表格布局方式和浏览器交互功能,设计实现可配置界面功能;通过配置即可完成逻辑结构定义、输入输出界面编辑功能;模型的原型已应用于某单位人力资源决策辅助系统中。     

Evolutionary design of user interfaces for workflow information systems

In this paper we argue that user interface design should evolve from iterative to evolutionary in order to support the user interface development life cycle in a more flexible way. Evolutionary design consists of taking any input that informs to the lifecycle at any level of abstraction and its propagation through inferior and superior levels (vertical engineering) as well as the same level (horizontal engineering). This lifecycle is particularly appropriate when requirements are incomplete, partially unknown or to be discovered progressively. We exemplify this lifecycle by a methodology for developing user interfaces of workflow information systems. The methodology involves several models (i.e., task, process, workflow, domain, context of use) and steps. The methodology applies model-driven engineering to derive concrete user interfaces from a workflow model imported into a workflow management system in order to run the workflow. Instead of completing each model step by step, any model element is either derived from early requirements or collected in the appropriate model before being propagated in the subsequent steps. When more requirements are elicited, any new element is added at the appropriate level, consolidated with the already existing elements, and propagated to the subsequent levels. A workflow editor has been developed to support the methodology.     

User modelling for graphical design in complex dynamic environments: concepts and prototype implementations

In this paper, it is proposed that using knowledge-based technology to generate different types of models of a target domain can better assist designers of support systems. The main argument is that designers' decisions should be based on knowledge about a domain rather than on their "common sense" judgments. This approach is illustrated by describing a canonical user model capturing operators' information and knowledge acquisition behaviour. In the present work, this model is used to help designers identify what process variables need to be associated with flow diagrams representing the structure of the technical system. Concepts underlying the user modelling approach as well as the knowledge elicitation and knowledge representation process are described. Finally, functionality provided by the user model is discussed as well as how the approach can be evaluated.     

VCR: Virtual community recommender using the technology acceptance model and the user’s needs type

A recommender system is a kind of automated and sophisticated decision support system that is needed to provide a personalized solution in a brief form without going through a complicated search process. There have been a substantial number of studies to make recommender systems more accurate and efficient, however, most of them have a common critical limitation – these systems are used as virtual salespeople, rather than as marketing tools. A crucial reason for this phenomenon is that the models suggested by prior studies only focus on a user’s behavioral outcomes without consideration of the embedded procedure. In this study, we propose a novel recommender system based on user’s behavioral model. Our proposed system, labeled VCR—virtual community recommender, recommends optimal virtual communities for an active user by case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extended models. In addition, it refines its recommendation results by considering the user’s needs type at the point of usage. To test the usefulness of our recommendation model, we conducted two-step validation–empirical validation for the collected data set, and practical validation to investigate the actual satisfaction level of users. Experimental results showed that our model outperformed all comparative models from the perspective of user satisfaction.     

A system for multibody analysis based on object-relational database technology

An efficient support for computational technology and information management will be critical in future Engineering Information Systems (EIS). This paper shows how it is possible to provide efficient data management capabilities to the discipline of Multibody System (MBS) analysis. The MECHAMOS prototype of a MBS analysis tool has been developed that is based on an object-relational database management system, AMOS, which has been extended with additional mathematical functionality by the use of Matlab and MapleV as computational engines. The MBS model is represented as a database model in AMOS and the analysis is based on symbolic formulation of Kane's equations of motion. With this configuration, data is made fully accessible via the AMOSQL query language of AMOS. Furthermore, by providing the MBS application with general database technology, the data management will be more efficient in terms of handling large amounts of data and the development and maintenance of MBS analysis systems. Since MBS models are represented in a “neutral format”, the reusability of these models increases and they will be more persistent to changes in the EIS environment.     

ADAPTIVE DECISION SUPPORT SYSTEM FOR AUTOMATIC PHYSICAL EFFORT PLAN GENERATION—DATA-DRIVEN APPROACH

Mathematical models delivered using both expert knowledge and experimental data improve understanding of dynamic properties of the system under consideration. This is useful for different purposes, such as prediction, diagnosis, decision making, and system control. A data-driven approach has been found to be particularly useful in designing adaptive decision support systems. We demonstrate the usefulness of data-driven models in a custom application designed for sport training management. We have developed a system that makes use of expert knowledge together with measurement data (heart rate, electromyography, and acceleration) as well as environmental (Global Positioning System) in order to generate an optimal training plan. The system performs such tasks as modeling of the athlete's cardiovascular system, estimation of the athlete's parameters, and adaptation of the model to the athlete.     

Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry

This study aims to identify, using an extended Technology-Acceptance Model (TAM), the factors affecting the decision of using a web-based learning system among blue-collar workers in the automotive industry. A structural equation-modeling approach was applied to identify the variables that significantly affect the decision of using the system. Using LISREL 8.54, data collected from 546 blue-collar workers were used to test the proposed research model. Empirical testing of the extended TAM found all paths to be significant in the hypothesized directions, that is, the results of the study strongly support the application of extended TAM in predicting the blue-collar workers’ intention to use a web-based learning system. Among the factors, social influence is a much stronger predictor of user intention compared to others. The study concludes with the implications of this study for managers and recommendations for possible future research.     

一种支持MDA的模型转换方法

平台无关模型(PIM)到平台有关模型(PSM)的转换是模型驱动体系结构(MDA)中的关键技术，但目前还没有针对该转换的有效的解决方案。从软件工程的实施出发，以抽象代数理论为基础，同时考虑建模元素的语法结构和语义特性，该文提出了一种基于体系结构映射的、可支持模型间自动转换的方法。该方法充分利用软件体系结构在软件开发各阶段间保持的良好可追踪性，使其同时成为系统分析和模型转换的基础。以J2EE目标平台为例说明了该方法的应用。     

Emotion on FPGA: Model driven approach

Implementation of intelligent and bio-inspired algorithms in industrial and real applications is arduous, time consuming and costly; in addition, many aspects of system from high level behavior of algorithm to energy consumption of targeted system must be considered simultaneously in the design process. Advancement of hardware platforms such as DSPs, FPGAs and ASICs in recent years has made it increasingly possible to implement computationally complex intelligent systems; on the other hand, however, the design and testing costs of these systems are high. Reusability and extendibility features of the developed models can decrease the total cost and time-to-market of an intelligent system. In this work, model driven development approach is utilized for implementation of emotional learning as a bio-inspired algorithm for embedded purposes. Recent studies show that emotion is a mechanism for fast decision making in human and other animals, and can be assumed as an expert system. Mathematical models have been developed for describing emotion in mammals from cognitive studies. Here brain emotional based learning intelligent controller (BELBIC), which is based on mammalian middle brain, is designed and implemented on FPGA and the obtained embedded emotional controller (E-BELBIC) is utilized for controlling real laboratorial overhead traveling crane in model-free and embedded manner. Short time-to-market, easy testing and error handling, separating concerns, improving reusability and extendibility of obtained models in similar applications are some benefits of the model driven development methodology.     

A new class of MODWT-SVM-DE hybrid model emphasizing on simplification structure in data pre-processing: A case study of annual electricity consumptions

In recent years, electricity crisis still becomes noticeable in some countries due to a widening gap between demand and supply. Consequently, the future demand plays a significant role in efficient management and utilization of electricity. Pertaining to efficient supply handling to increase the power system reliability, an electricity demand forecasting is one of the most crucial tools. The forecasting technique is used by decision makers all over the world to predict the future demand as key information for a proper policy. In this research, the hybrid model consists of maximal overlap discrete wavelet transform (MODWT), support vector machine (SVM), and differential evolution (DE) optimization emphasizing on simplifying the complex structure in data pre-processing is proposed to forecast the thirty-two annual electricity consumptions and is compared with traditional forecasting models, hybrid model of MODWT and SVM, and combined model of SVM and DE optimization based on mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) measures as well as Friedman test and post hoc test. The empirical results indicate that the proposed model outperforms other forecasting models and provides more accurate forecasts than other candidate models at 0.05 significance levels and the nearly highest precision. Consequently, the proposed model is able to reduce the limitations of individual models regarding annual electricity consumptions and can be used as a promising tool in order to forecast annual electricity consumptions as well.     

A hybrid AI/OR decision support tool for backbone communications network design

Much of existing DSS literature views the role of human expertise as primarily that of selecting appropriate formal models for solving a problem or synthesizing sequences thereof. Once a model (or model sequence) is determined, values of decision variables are determined by the model(s) alone. Hence, automated methods for facilitating model selection and synthesis have received considerable attention. However, a single model is often not an accurate abstraction of reality. Also, results from multiple formal models often have to be combined heuristically to obtain practical solutions. Thus, in this paper we explore the premise that human expertise needs to interact with formal models during the process of searching for solution values. Specifically, we describe a hybrid decision support tool for the design of backbone communication networks, a problem recognized as being of considerable complexity. An internal representation of the design process that employs a blackboard, a truth maintainence system and dependency directed backtracking, allows human expertise and formal models to jointly determine decision variable values in a uniform manner. The design tool has been implemented using a combination of Lisp and Fortran. Computational experiments indicate that incorporating human expertise during the search process results in superior complete solutions and added flexibility in satisfying ad hoc requirements. We conjecture that this hybrid search approach is not limited to the telecommunication network design problem and can be extended to other applications.     

Rho: A decision support system for pricing in law firms

There is no single schedule of legal fees and the way they are calculated; a rational model of fee determination is acutely needed. This paper presents a decision support system called Rho that allows the user to enter the internal and external data into a set of decision models to determine the strategic pricing for law firms. The paper describes the main features of the system, the optimization model that plays a critical role in the decision process, an illustration of how the system was applied in a real setting, an assessment of the proposed approach and future research issues.     

Data driven model mismatch detection based on statistical band of Markov parameters

In modern process industries, model accuracy is critical for safe operation as well as control performance of process industrial systems. It is in practical intractable to locate the corresponding system models that contain significant mismatches and sometimes re-identification may be needed to implement to hundreds of process loops. As such, a novel data driven methodology is proposed to detect the model-plant mismatches. Subspace approach and moving window scheme are integrated to estimate the Markov parameters of the process models. Then, the statistical bands of the Markov parameters are calculated using routine operation data. Thus, the model mismatch can be detected by evaluating the bias between the band of the normal case and that of the monitored case. The mismatch models can be isolated, which facilitates the decision when and where to take the re-identification. The proposed method avoids extra efforts and costs caused by full-scale experiments to the process. Simulations on a distillation process is employed to demonstrate the efficiency of the proposed approach.     

地理信息系统应用模型实现技术研究

地理信息系统的应用模型是其空间决策支持系统的核心,因而对应用模型和模型库管理系统的开发以及应用模型实现技术的研究,已成为地理信息系统深入应用的关键所在。通过对地理信息系统技术发展及其功能的回顾,分析了目前GIS应用模型实现的一系列有关技术问题,并针对存在的问题,提出了关于模型的有效管理,模型的共享和安全,GIS与应用模型的集成,模型库与数据库的通信机制,模型的可视化及其互操作等问题的解决方案与途径,最后以长江水体的二维污染扩散模型为例,阐述了模型实现的方法和技术。     

模型驱动的装备保障优化决策支持系统

以数据、模型、用户界面等要素为基础,提出了以优化模型为驱动的装备保障优化决策支持系统设计思路,拓展设计了系统总体功能结构.构建了包含算法库、模型库和应用库的模型库,引入了脚本模型,以顺序、选择或循环结构程序组合优化模型,实现复杂装备保障决策问题的求解和决策.实例应用表明系统极大地提高了部队装备精确保障能力.     

Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area,Korea

A decision tree approach was applied and validated for analysis of landslide susceptibility using a geographic information system (GIS). The study area was the Pyeongchang area in Gangwon Province, Korea, where many landslides occurred in 2006 and where the 2018 Winter Olympics are to be held. Spatial data, such as landslides, topography, and geology, were detected, collected, and compiled in a database using remote sensing and GIS. The 3994 recorded landslide locations were randomly split 50/50 for training and validation of the models. A decision tree model, which is a type of data-mining classification model, was applied and decision trees were constructed using the chi-squared (χ²) automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. Also, as a reference, a frequency-ratio model was applied using the same database. The relationships between the detected landslide locations and their factors were identified and quantified by frequency-ratio and decision tree models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indices and maps. Then, the resulting landslide-susceptibility maps were validated using area-under-the-curve (AUC) analysis with the landslide area data that had not been used for training the model. The decision tree models using the CHAID and QUEST algorithms had accuracies of 81.56% and 80.91%, respectively, which were somewhat better than the results for the frequency-ratio model (80.15%). These results indicate that decision tree models using the CHAID and QUEST algorithms can be useful for landslide susceptibility analysis.