A research framework for Web-based open decision support systems

With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). In this paper, we propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS and creates a uniform research framework for various decision support systems. The conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also analyze the basic functions and develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS function efficiently. Finally, an illustrative example is studied to support the proposed ideas.     

Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration

The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and learning from experience. An outline of a case-based system architecture is presented, and used to show that a focus on the retaining and reuse of past cases facilitates a gradual and evolutionary transition from an information system to a knowledge-based system.     

System dynamics in software project management: towards the development of a formal integrated framework

Successful software development is becoming increasingly important to many companies. However, most projects fail to meet their targets, highlighting the inadequacies of traditional project management techniques in this unique setting. Despite breakthroughs in software engineering, management methodologies have not improved, and the major opportunities for better results are now in this area. Poor strategic management and related human factors have been cited as a major cause for failures, which traditional techniques cannot incorporate explicitly. System dynamics (SD) aims to model the behaviour of complex socio-economic systems; there has been a number of applications to software project management. SD provides an alternative view in which the major project influences are considered and quantified explicitly. Grounded on a holistic perspective it avoids consideration of the detail required by traditional tools, looking at the key aspects of the general project behaviour. However, if SD is to play a key role in software project management it needs to be embedded within the traditional decision-making framework. The authors developed a conceptual integrated model, the SYDPIM, which has been tested and improved within a large on-going software project. Such a framework specifies the roles of SD models, how they are to be used within the traditional management process, how they exchange information with the traditional models, and a general method to support model development.     

Evaluation of techniques for manufacturing process analysis

In the last 20 years, several methodologies, models and tools have been developed for the analysis and optimisation of manufacturing systems in order to propose general improvements. Many of these techniques make extensive use of data modelling, simulation, decision-making support, expert systems and reference models. This paper presents the first outcome of a piece of research work to integrate manufacturing process analysis into an integrated modelling framework covering all aspects related to the shop-floor as it really is. The main methodologies and software tools have been identified and evaluated and the results tested on industrial examples. As a result of this evaluation it has been possible to identify the inefficiencies of the techniques. These problems are connected with integrating the different types of data to be analysed—such as quality, time, costs, resource capacity, productivity, flexibility or improvements—into a single analysis environment. The inefficiencies detected enable us to present a general framework for making better use of modelling techniques for manufacturing process analysis. Received July 2005 / Accepted January 2006     

Application of the analytical hierarchy process for real-time scheduling and part routing in advanced manufacturing systems

This paper presents a framework that employs the analytical hierarchy process (AHP) in advanced manufacturing systems for real-time scheduling and part routing. The proposed multicriteria decision-making framework brings a new perspective to real-time scheduling and part routing decisions, by implementing pairwise comparison of possible future states of a manufacturing system. The framework includes an extended finite state machine and a scheduler model to facilitate dynamic, short-term decision making. The scheduler model, which is developed on the basis of control theory, uses AHP to assess possible future states in a limited look-ahead horizon by comparing the performance measures of each state. The multicriteria decision-making framework developed in this study is implemented in a simulation environment to validate it for real-time manufacturing system control and investigate its performance under a range of look-ahead horizons. Simulation results indicate that the proposed framework performs better for a mid-range horizon for most of the commonly used performance measures.     

Intelligent multi-criteria fuzzy group decision-making for situation assessments

Organizational decisions and situation assessment are often made in groups, and decision and assessment processes involve various uncertain factors. To increase efficiently group decision-making, this study presents a new rational–political model as a systematic means of supporting group decision-making in an uncertain environment. The model takes advantage of both rational and political models and can handle inconsistent assessment, incomplete information and inaccurate opinions in deriving the best solution for the group decision under a sequential framework. The model particularly identifies three uncertain factors involved in a group decision-making process: decision makers’ roles, preferences for alternatives, and judgments for assessment-criteria. Based on this model, an intelligent multi-criteria fuzzy group decision-making method is proposed to deal with the three uncertain factors described by linguistic terms. The proposed method uses general fuzzy numbers and aggregates these factors into a group satisfactory decision that is in a most acceptable degree of the group. Inference rules are particularly introduced into the method for checking the consistence of individual preferences. Finally, a real case-study on a business situation assessment is illustrated by the proposed method.     

Identifying competences for the design of systems supporting complex decision-making tasks: a managerial planning application

The work presented in this paper is addressed to the front-end phases of the development of a system supporting a complex decision-making task: high-level managerial planning in small-to-medium enterprises (SMEs). It attempts to identify characteristics of the competences possessed by experienced managers and used when taking high-level managerial planning decisions, and to assess their potential implications for system design. The work is based on the assumption that the design of effective systems supporting complex decision-making tasks in a specific domain, would require elicitation of competences possessed by experienced persons in the domain, especially those related to the mental processes followed when confronting the cognitive constraints involved in the specific decision-making environment. Such an investigation would lead to the development of systems improving decision making since they would (1) respond to the difficulties met by potential users in performing complex decision-making tasks; (2) reduce possible negative consequences of users' decision-making competences; and (3) at the same time they would achieve the required compatibility with users' mental processes. The research methodology was based on a planning scenario and on an analysis of verbal protocols obtained from a sample of small-enterprise managers confronted with this scenario. Results provide evidence about (1) the type of decisions and actions taken by experienced managers when confronted with the cognitive constraints involved in managerial planning situations; (2) phases and sequence of the process of arriving at planning decisions; and (3) data used and types of analyses performed. Some general implications are drawn from these results with respect to the configuration of a system aiming at supporting managerial planning. These refer to the type of support to be provided at various phases of the planning process, possible features of the human-computer interface, and generic or task/user specific aspects of the system.     

Research issues of Internet-integrated cognitive style

Cognitive style was once a popular research topic in the field of decision support systems (DSS), but because of the lack of usable results, it has not received much attention from the research community in recent years. This paper argues that it can be both promising and worthwhile to revive research efforts into cognitive style in the modern decision-making environment. Several reasons are offered to support this argument: First, the decision-making environment is now more integrated with technology, particularly the Internet, making it more uniform and easier to define. Second, the potential benefit of such studies is greater because more people are using Internet-based technology to make decisions. Third, data on the cognitive behavior of decision makers are captured and available for analysis because of the close integration between technology and the decision-making process. Research questions are raised and potential variables are proposed and discussed.     

A decision-theoretic approach to data mining

In this paper, we develop a decision-theoretic framework for evaluating data mining systems, which employ classification methods, in terms of their utility in decision-making. The decision-theoretic model provides an economic perspective on the value of "extracted knowledge", in terms of its payoff to the organization, and suggests a wide range of decision problems that arise from this point of view. The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make is formalized. We propose two ways by which independent data mining systems can be combined and show that the combined data mining system can be used in the decision-making process of the organization to increase payoff. Examples are provided to illustrate the various concepts, and several ways by which the proposed framework can be extended are discussed.     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

The dynamics of adaptive behavior: A research program

This paper provides a comprehensive overview of the motivations, methodology and current status of an ongoing research program whose long-term goal is to elucidate the essential principles of a theory of adaptive behavior. The thoroughly dynamical nature of both adaptive behavior itself and the causal mechanisms that support it is emphasized throughout. An initial mapping of the basic concepts of adaptive behavior into the language of dynamical systems theory is proposed, and some of the general consequences of this preliminary theoretical framework are discussed. The two key ideas of this framework are (1) that an agent and its environment should be understood as two coupled dynamical systems whose mutual interaction is jointly responsible for the agent's behavior, and (2) that an agent's need to maintain its existence in its environment defines a viability constraint on its behavioral dynamics. A constructive research methodology involving the use of evolutionary algorithms to evolve continuous-time recurrent neural networks for controlling the behavior of model agents is described, and several examples of this methodology are presented, including models of chemotaxis, walking, sequential decision-making and learning. Finally, a detailed dynamical analysis of one evolved walking circuit is presented. This analysis illustrates the kinds of insights that can be obtained by treating agents as dynamical systems and applying the tools of dynamical systems theory to their behavior.     

Knowledge precepts for design and evaluation of information visualizations

The design and evaluation of most current information visualization systems descend from an emphasis on a user's ability to "unpack" the representations of data of interest and operate on them independently. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging these gaps, propose a framework for the design and evaluation of information visualization systems, and demonstrate its use.     

Secure—I*: Engineering Secure Software Systems through Social Analysis

Engineering secure software systems requires a thorough understanding of the social setting within which the system-to-be will eventually operate. To obtain such an understanding, one needs to identify the players involved in the system's operation, and to recognize their personal preferences, agendas and powers in relation to other players. The analysis also needs to identify assets that need to be protected, as well as vulnerabilities leads to system failures when attacked. Equally important, the analyst needs to take rational steps to predict most likely attackers, knowing their possible motivations, and capabilities enabled by latest technologies and available resources. Only an integrated social analysis of both sides (attackers/protectors) can reveal the full space of tradeoffs among which the analyst must choose. Unfortunately, current system development practices treat design decisions on security in an ad-hoc way, often as an afterthought. This paper introduces a methodological framework based on i^*, for dealing with security and privacy requirements, namely, Secure-i^*. The framework supports a set of analysis techniques. In particular, attacker analysis helps identify potential system abusers and their malicious intents. Dependency vulnerability analysis helps detect vulnerabilities in terms of organizational relationships among stakeholders. Countermeasure analysis supports the dynamic decision-making process of defensive system players in addressing vulnerabilities and threats. Finally, access control analysis bridges the gap between security requirement models and security implementation models. The framework is illustrated with an example involving security and privacy concerns in the design of electronic health information systems.In addition, we discuss model evaluation techniques, including qualitative goal model analysis and property verification techniques based on model checking.     

Pluralistic multi-agent decision support system: a framework and an empirical test

Recent research in decision support systems (DSSs) has focused on building active cooperative intelligent systems. Research in agent-based decision support is a promising stream in this direction. This paper proposes a framework for a pluralistic multi-agent decision support system (MADSS). The distinguishing feature of the proposed approach is its organization around human decision making process. The framework builds upon the decision support pyramid with agents organized into groups according to the phases of the problem solving model. We outline the design principles and develop architecture for MADSS. The framework is illustrated through an investment MADSS prototype. The results of the empirical test are presented.     

基于PMML的移动通信客户流失模型交换

移动通信领域迫切需要在地理分布的经营分析系统之间交换标准的数据挖掘模型。尽管预测模型标记语言已经成为数据挖掘模型交换格式的业界标准,但并没形成可用的框架来指导标准交换模型的生产过程。该文提出了支持挖掘模型交换和移动通信客户流失分析的决策树算法框架。利用该框架构建了流失预警系统,并使用模拟客户数据验证了其有效性。对标准交换模型进行了适当扩展,以支持对移动通信数据更加有效的流失分析。     

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision-making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision-making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.     

A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support

In this paper, a distance-based group decision-making (GDM) methodology is proposed to solve unconventional multi-person multi-criteria emergency decision-making problems. In this model, some decision-makers are first identified to formulate a group decision-making framework. Then a standard multi-criteria decision-making (MCDM) process is performed on specific decision-making problems and different decision results are obtained from different decision-makers. Finally, these different decision results are aggregated into a group consensus to support the final decision-making. For illustration and verification purposes, a numerical example and a practical unconventional emergency decision case are presented. Experimental results obtained demonstrate that the proposed distance-based multi-criteria GDM methodology can improve decision-making objectivity and emergency management effectiveness.     

A Dempster-Shafer theory and uninorm-based framework of reasoning and multiattribute decision-making for surveillance system

Closed-circuit television and sensor-based intelligent surveillance systems have attracted considerable attentions in the field of public security affairs. To provide real-time reaction in the case of a huge volume of the surveillance data, researchers have proposed event-reasoning frameworks for modeling and inferring events of interest. However, they do not support decision-making, which is very important for surveillance operators. To this end, this paper incorporate a function of decision-making in an event-reasoning framework, so that our model not only can perform event-reasoning but also can predict, rank, and alarm threats according to uncertain information from multiple heterogeneous sources. In particular, we propose a multiattribute decision-making model, in which an object being watched is modeled as a multiattribute event, where each attribute corresponds to a specific source, and the information from each source can be used to elicit a local threat degree of different malicious situations with respect to the corresponding attribute. Moreover, to assess an overall threat degree of an object being observed, we also propose a method to fuse the conflict threat degrees regarding all the relevant attributes. Finally, we demonstrate the effectiveness of our framework by an airport security surveillance scenario.     

The impact of information and communication technologies on the costs of democracy

Recent developments in information and communication technologies (ICTs) can change the way strategic organizational decisions are negotiated and voted upon. We propose an analytical framework that investigates the effect of such technologies on the costs of the decision-making process in organizations by incorporating a technology variable in an already well-known model of public decision-making. The framework is applicable to any large group decision setting where collective decisions are made by voting, such as shareholders’ meetings, local and federal government meetings. We apply the framework to derive policies that will help to decrease the costs related to the democratic process. The first policy requires that governments should invest more in technologies that support multi-lateral communication and negotiation among citizens, and the second requires that citizens should use better voting mechanisms than simple majority voting to make decisions.     

基于RIA和Skyline的城市风暴潮灾害三维展示系统

风暴潮灾害展示系统可为城市风暴潮灾害辅助决策提供可视化场景和数据应用支持,现有风暴潮灾害系统功能简单,开发技术陈旧。为满足城市风暴潮灾害辅助决策对二维和三维数据展示与分析的需求,依据跨平台、富用户体验和技术新颖的建设目的,设计了城市风暴潮灾害三维展示系统,并以上海浦东新区临港新城数据为例实现了系统应用。介绍城市风暴潮灾害三维展示系统的功能,基于ArcGIS、Skyline TerraSuite、Flex和Java的系统技术框架与建设流程,以及针对三维建模与展示、二维与三维联动及富客户体验等关键问题的解决方案,为城市风暴潮灾害辅助决策相关系统开发提供参考。