广西肝癌与气候危险因素数据库查询系统

建立广西肝癌与气候危险因素数据库是广西肿瘤防治工作中的一项重要内容。本查询系统可以提供很直观的包括空间位置在内的很多信息。本查询系统在MAPGIS平台上进行设计工作,运用API函数和VC#2008,自主开发广西肝癌与气候危险因素数据库查询系统。详细介绍了系统的设计和实现。     

基于WebGIS的海水淡化工程空间信息可视化应用研究

针对海水淡化工程空间信息共享与可视化服务需求,本文借鉴WebGIS在海洋信息可视化领域的先进理念和成功经验,研究建立海水淡化工程空间数据库,设计开发海水淡化工程空间信息可视化系统。系统实现了地图浏览、专题查询及统计分析等功能,能够为用户及时、准确掌握海水淡化工程相关属性信息与空间信息提供一定支撑。     

基于网络的扎龙湿地数据发布系统*

主要研究网络电子地图发布的实现方法,以扎龙湿地网络GIS系统中的电子地图网络发布子系统原型为例,讨论了地理数据的空间可视化和空间分析的实现问题。在此基础上实现了一种基于矢量数据的网络地理信息系统服务器的实现方法,并探讨了在Java和Internet平台上实现这一方法的一些技术问题。该系统实现了网络环境下GIS的多种功能,提供了地图与空间属性信息数据库的交互式查询、可视化检索、比较分析和动态显示,以及与其他多媒体属性信息数据库的无缝连接的功能。     

基于C/S架构的徐州市绿地信息系统设计与实现

在确立绿地信息分类和组织原则的基础上构建了徐州市绿地信息数据库,结合该数据库在VB开发环境下利用SuperMap Objects组件开发了徐州市绿地地理信息系统,该系统不仅具有缓冲分析、属性查询、模糊查询等地理信息系统功能,还具有界面简单、易于操作、可视化强的特点。系统的使用为提高徐州市绿地管理的信息化水平,城市绿地的规划、建设和管理提供了可视化的支撑工具。     

STQing：一种时态GIS可视化查询语言

在GIS领域,时态属性日趋重要,很多数据库管理系统已经加入了时间和空间属性而成为空间数据库系统。本文在此基础上提出了时态可视化,并设计了一个可以适应时空变化能力的图标式可视化查询语言STQing。     

可视化查询语言CQL的实现

为使得许多非计算机专业的用户，尤其是未接受过数据库编程语言训练的用户，能够独立操纵一个数据库系统，有效地解决工作、生活中的问题，我们研究并提供了一种空间数据库可视化查询语言CQL。借助该语言，用户可以直观、方便地操纵数据库系统或查询数据库信息。本文介绍了CQL语言的语素、语义、语法及语用的定义，同时给出了CQL编辑器、编译器和查询结果可视化的设计方法。     

嵌入式空间数据库综合查询算法

嵌入式空间数据库一般作为嵌入式GIS的后端,为其提供对空间数据和属性数据的存储、搜索、查询等多项功能。其中,查询性能是直接影响嵌入式GIS运行效率的基本因素之一。该文对嵌入式空间数据库综合查询算法进行分类,提出并实现了先空间串行查询算法、先属性串行查询算法和并行查询算法,对该3种查询算法进行性能测试与比较,并给出了测试比较结果。     

嵌入式空间数据库综合查询算法

嵌入式空间数据库一般作为嵌入式GIS的后端,为其提供对空间数据和属性数据的存储、搜索、查询等多项功能.其中,查询性能是直接影响嵌入式GIS运行效率的基本因素之一.该文对嵌入式空间数据库综合查询算法进行分类,提出并实现了先空间串行查询算法、先属性串行查询算法和并行查询算法,对该3种查询算法进行性能测试与比较,并给出了测试比较结果.     

石油勘探可视化信息系统的设计与实现

本文介绍C/S模式下的石油勘探可视化信息系统的设计思想，体系结构及功能模块，运用数据库、图形库、可视化及网络等信息化技术，实现集油田勘探信息的管理，查询、发布、分析、应用为一体的信息化交互的石油勘探GIS系统，为油田勘探的决策者与管理者，生产科研工作者提供一个全新的高效率工作平台。     

基于GIS的高速公路沿线设施管理信息查询系统的设计与实现

将GIS技术引入到高速公路沿线设施管理信息系统,实现沿线设备设施的可视化管理.并就如何在SuperMap平台下实现系统的查询功能进行了探讨.介绍了量子图形数据库,进行关键字段关联的方法实现图形与属性的互查,以及目标对象综合信息查询方法.利用SuperMap Objects 的相关接口函数开发了高速公路沿线设施管理信息查询系统,使用户准确定位并查询感兴趣目标的空间信息、属性信息和综合信息,从而在宏观上把握高速公路上的设备设施,辅助相关人员进行管理和决策.     

Stability analyses of group decision making

The importance of multicriteria models in a group decision making is increasingly being emphasized by researchers. One of the most significant of these models is the analytic hierarchy process (AHP). Through the AHP, decision makers are able to conduct a series of pairwise comparisons on pairs of criteria and priority indices can thereby be derived. However, the judgmental process can often be subjective as researchers have not put adequate emphasis on the stability and reliability of group weights observed through the process.

This paper develops a method of replication coupled with the use of the quality confidence intervals in order to generate invigorating debates on a particular issue before weight assignments are made. The replicated assignments are used to determine the group's priority indices. The aim is to enhance the ability of decision makers to make the same decisions when provided with similar environmental conditions. Ultimately, this will provide greater reliability to the derived outcomes. Obviously, the use of multicriteria modelling in subjective assessments would be meaningless if the decisions made were not consistent under the same conditions. In other words, these models would provide no guidance or benefit to decision makers.

In the paper, two case studies relating to the ranking of factors for selection of advanced technologies and issues for achieving competitiveness are analyzed utilizing an experimental group. Paired t-tests show that there are no differences between the rankings observed for the three replications for each respective case for almost all the criteria. Thus, the procedure provided here offers utility in group decision making.     

A two-phase case-based distance approach for multiple-group classification problems

Classification is a procedure to separate data or alternatives into two or more classes. In practice, the need to classify alternatives involving multiple criteria into distinct classes is considerable. Therefore, determining how to assist decision makers in classifying alternatives into multiple classes is an important issue in the field of multiple-criteria decision aids. This study proposes a two-phase case-based distance approach used to assist decision makers to classify alternatives into multiple groups. By incorporating the advantages of the case-based distance method, the proposed two-phase approach can classify alternatives by evaluating a set of cases selected by decision makers, reduce the number of misclassifications, improve multiple solution problems, and lessen the impact of outliers. An interactive classification procedure is also proposed to provide flexibility in such a way that decision makers can check and adjust classification results iteratively.     

Consensus reaching for group decision making with multi-granular unbalanced linguistic information: A bounded confidence and minimum adjustment-based approach

In group decision making problems, there exist the situations that decision makers may use unbalanced linguistic term sets that are not uniformly and symmetrically distributed to provide their linguistic assessments over alternatives. Moreover, due to the difference in knowledge and culture backgrounds, it is also possible that multi-granular linguistic term sets may also be used by decision makers. How to manage multi-granular unbalanced linguistic information in consensus-based group decision making has becoming an important topic in linguistic decision making. In this paper, we first revise Herrera’s unbalanced linguistic term sets and propose a simplified linguistic computational model to fuse multi-granular unbalanced linguistic terms. Afterwards, for multi-criteria group decision making problems with multi-granular unbalanced linguistic information, we develop two optimization models to generate adjustment advice for decision makers who have to change his/her opinions in consensus reaching process, which consider both the bounded confidence levels and minimum adjustment of decision makers’ linguistic assessments. Moreover, an algorithm is further proposed to help decision makers reach consensus in group decision making. Eventually, an application example for ERP system supplier selection and some simulation results are presented to illustrate and justify the consensus reaching algorithm.     

Using Gower Plots and Decision Balls to rank alternatives involving inconsistent preferences

Ranking alternatives involving inconsistent preferences is one of the most important topics in decision-making. Determining how to assist decision makers in understanding the decision context and adjusting inconsistencies in judgment are two important issues in ranking alternatives. This study proposes a visualization approach which will assist decision makers in ranking alternatives involving inconsistent preferences. Gower Plots are adopted to detect alternatives involving inconsistencies. An adjusting model is developed to provide suggestions for simultaneously improving ordinal and cardinal inconsistencies. A Decision Ball model is applied to visualize the decision context. By a graphical and interactive interface, decision makers can iteratively detect inconsistencies, choose the preferred way to adjust inconsistencies, observe relationships among alternatives, and then rank alternatives.     

基于信任关系和互补判断矩阵的群决策方法

随着信息和网络技术的不断发展,基于社会网络的群决策问题受到越来越多研究者的关注.针对社会网络环境下模糊互补判断矩阵的群决策问题,研究群体共识调整过程和方案选择方法.首先,融合决策者之间的社会关系、身份地位、知识能力3个方面信息来构建决策者两两之间的信任关系;其次,提出一种尽可能减少元素间共识补偿的共识度度量方法,在此基础上建立基于信任关系的共识调整模型,并从理论上证明该模型的有效性;最后通过信任关系矩阵的特征向量中心度分别求出专家的重要性权重,用以集结专家的偏好信息和对方案进行排序选择,算例分析表明了所提出方法的有效性.     

A fuzzy group-preferences analysis method for new-product development

This paper reports a new idea-screening method for new product development (NPD) with a group of decision makers having imprecise, inconsistent and uncertain preferences. The traditional NPD analysis method determines the solution using the membership function of fuzzy sets which cannot treat negative evidence. The advantage of vague sets, with the capability of representing negative evidence, is that they support the decision makers with the ability of modeling uncertain opinions. In this paper, we present a new method for new-product screening in the NPD process by relaxing a number of assumptions so that imprecise, inconsistent and uncertain ratings can be considered. In addition, a new similarity measure for vague sets is introduced to produce a ratings aggregation for a group of decision makers. Numerical illustrations show that the proposed model can outperform conventional fuzzy methods. It is able to provide decision makers (DMs) with consistent information and to model situations where vague and ill-defined information exist in the decision process.     

A fuzzy group-preferences analysis method for new-product development

This paper reports a new idea-screening method for new product development (NPD) with a group of decision makers having imprecise, inconsistent and uncertain preferences. The traditional NPD analysis method determines the solution using the membership function of fuzzy sets which cannot treat negative evidence. The advantage of vague sets, with the capability of representing negative evidence, is that they support the decision makers with the ability of modeling uncertain opinions. In this paper, we present a new method for new-product screening in the NPD process by relaxing a number of assumptions so that imprecise, inconsistent and uncertain ratings can be considered. In addition, a new similarity measure for vague sets is introduced to produce a ratings aggregation for a group of decision makers. Numerical illustrations show that the proposed model can outperform conventional fuzzy methods. It is able to provide decision makers (DMs) with consistent information and to model situations where vague and ill-defined information exist in the decision process.     

An interactive method for fuzzy multiple attribute group decision making

In this paper, we develop an interactive method for multiple attribute group decision making under fuzzy environment. The method can be used in situations where the information about attribute weights is partly known, the weights of decision makers are expressed in exact numerical values or triangular fuzzy numbers, and the attribute values are triangular fuzzy numbers. The method transforms fuzzy decision matrices into their expected decision matrices, constructs the corresponding normalized expected decision matrices by two simple formulas, and then aggregates these normalized expected decision matrices into a complex decision matrix. Moreover, the decision makers are asked to provide their preferences gradually in the course of interactions. By solving linear programming models, the method diminishes the given alternative set gradually, and finally finds the most preferred alternative. By using the method, the decision makers can provide and modify their preference information gradually in the process of decision making so as to make the decision result more reasonable. The method can not only reflect the importance of the given arguments and the ordered positions of the arguments, but also relieve the influence of unfair arguments on the decision result. Finally, a practical problem is used to illustrate the developed method.     

Twitter-aided decision making: a review of recent developments

Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.

     

An approach to group decision making with heterogeneous incomplete uncertain preference relations

For practical group decision making problems, decision makers tend to provide heterogeneous uncertain preference relations due to the uncertainty of the decision environment and the difference of cultures and education backgrounds. Sometimes, decision makers may not have an in-depth knowledge of the problem to be solved and provide incomplete preference relations. In this paper, we focus on group decision making (GDM) problems with heterogeneous incomplete uncertain preference relations, including uncertain multiplicative preference relations, uncertain fuzzy preference relations, uncertain linguistic preference relations and intuitionistic fuzzy preference relations. To deal with such GDM problems, a decision analysis method is proposed. Based on the multiplicative consistency of uncertain preference relations, a bi-objective optimization model which aims to maximize both the group consensus and the individual consistency of each decision maker is established. By solving the optimization model, the priority weights of alternatives can be obtained. Finally, some illustrative examples are used to show the feasibility and effectiveness of the proposed method.