A framework for ontology based decision support system for e-learning modules, business modeling and manufacturing systems

This paper identifies how ontology models can be vigorously used to define semantics and relationships in representing objects/modules for e-learning, business modeling support and manufacturing processing details. Further extraction of these relations by intelligent decision-support systems using data mining as a tool is discussed. The paper envisages the possibility of establishing a common solution platform for product development and customization leading to increased profitability and better resource utilization. It showcases ways to link these different ontological models leading to cross platform compatibility. It also tries to explore manufacturer-customer relationship and using them to provide quality analysis methods for further improvement in the product processing model.     

A conceptual framework for society-oriented decision support

Inspired by the operation of human social organisation, this paper presents a new architecture—a pyramid-committee—for developing society-oriented intelligence, whose structure imitates the organisation of human society in its decision making. The system takes a pyramid-like hierarchical structure with links in the pyramid forming a semi-lattice, which relate not only to nodes in the same layer, but also to others in different layers. The output of the system is a result of the negotiation and balancing of different interests. For such a system to function, the main difficulties concern the complicated relationships between different factors or agents. Focussing on the airport environment audit, we discuss the development of a model framework and the role of neural networks.     

A framework for decision support systems evaluation

In this paper, we present a framework for the Decision Support Systems evaluation problem. Using the Gorry-Scott Morton's framework for information systems, we develop several evaluation methods that structure the evaluation process. The framework determines the best methods of evaluation that are suitable to the characteristics of the Decision Support System concerned. Finally, we use the framework to evaluate two widely used Decision Support Systems.     

A general framework for time-aware decision support systems

In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of-the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations.     

An ontology based multi-agent system to support HABIO outsourcing framework

In recent years the widespread application of outsourcing to help improve finance and maintain competitiveness is growing rapidly, and has been accompanied by a growth of academic research in this field. Most of the outsourcing literature has focused primarily on the social and cultural impacts, the organisational and management perspectives of outsourcing and very few tools have been developed to assist managers in making outsourcing decisions. Incorrect decision-making in outsourcing can lead to devastating consequences for their business. To guide decision makers in managing the complexity of outsourcing a holistic approach to outsourcing has been developed by taking business, information and organisational perspectives (HABIO). The purpose of this paper is to demonstrate how HABIO can be applied to help managers evaluate the impact of their decision-making by providing them with a set of ‘what-if’ scenarios, which are enacted by intelligent agents. The case example, discussed in this paper, demonstrates that HABIO can be applied effectively using a multi-agent system to analyse the outsourcing decision for a call centre sector in the retail industry. A prototype of the multi-agent system is presented where intelligent agents are endowed with specific domain ontologies, appropriate to solve a specific problem, and with a reasoning mechanism to achieve specific goals and to collaborate together to produce a set of “what-if scenarios” to the outsourcing managers.     

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.     

A labelling framework for probabilistic argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature.     

Context-aware decision support systems based on typical knowledge integration models

A methodology for designing context-aware decision support systems based on typical knowledge integration models is proposed. These models describe the functional capabilities of the system at different stages of its usage. The models are used to specify requirements for information and knowledge from the side of the context-aware system. The comparison of these requirements with the user requirements for the system functional capabilities and user restrictions allows us to obtain the functional capabilities that are available for a given user.     

分布式本体集成框架下虚拟本体集合动态生成算法

为了有效组织和管理网格环境内存在的各类分布异构的本体资源,提出了基于网关服务的分布式本体集成框架.从体系结构角度对本体集成过程中涉及到的功能模块进行分析和划分,并基于网格服务设计了关键组件.在此基础上从提高计算资源利用率和满足用户多样性需求的角度出发,提出了虚拟本体集合动态生成算法.该算法将缓存技术与虚拟本体相结合,为本体数据的自动生成和再生成提供了较完整的系统方法,增强了本体资源的复用性.实验结果表明,该算法可以提高计算资源利用率,实现本体资源的复用率.     

面向虚拟采办的智能决策支持系统概念框架

提出一种面向虚拟采办全寿命周期、全系统、全方位决策的智能决策支持系统SBA—IDSS概念框架．作为真实世界采办最终需求的抽象描述,它属于与实现无关的规范性模型体系,由它定义应用领域、用户概念和环境特征．同时它是数字世界中拟实现的初始工程模型,包括与实现无关的设计可行性模型、规划模型及功能顶层分解．该框架支持系统的自组织、自适应智能行为,预期可按需组成模型与仿真、文件、知识、通信及数据驱动的各类实用决策支持系统．该框架通过一项SBA实例的验证,得到可用性的正面评价．     

基于本体的异构数据集成方法

本体具有很强的表达概念语义和推理的能力,为解决多数据源数据集成中语义异构的问题,本体被逐渐应用于其中.给出了一种基于混合本体的信息集成方法,通过建立局部本体、全局本体,以及它们之间的映射,使对全局本体的全局查询能够分解为对底层数据源的查询,重点讨论了该方法中本体的映射,在映射中加入了属性转换,解决了其它映射形式只能表达简单的映射或映射不易维护的问题,并且提高了系统的查全率和查准率.     

A decision support framework for metrics selection in goal-based measurement programs: GQM-DSFMS

Software organizations face challenges in managing and sustaining their measurement programs over time. The complexity of measurement programs increase with exploding number of goals and metrics to collect. At the same time, organizations usually have limited budget and resources for metrics collection. It has been recognized for quite a while that there is the need for prioritizing goals, which then ought to drive the selection of metrics. On the other hand, the dynamic nature of the organizations requires measurement programs to adapt to the changes in the stakeholders, their goals, information needs and priorities. Therefore, it is crucial for organizations to use structured approaches that provide transparency, traceability and guidance in choosing an optimum set of metrics that would address the highest priority information needs considering limited resources. This paper proposes a decision support framework for metrics selection (DSFMS) which is built upon the widely used Goal Question Metric (GQM) approach. The core of the framework includes an iterative goal-based metrics selection process incorporating decision making mechanisms in metrics selection, a pre-defined Attributes/Metrics Repository, and a Traceability Model among GQM elements. We also discuss alternative prioritization and optimization techniques for organizations to tailor the framework according to their needs. The evaluation of the GQM-DSFMS framework was done through a case study in a CMMI Level 3 software company.     

基于混合本体的保险公司数据集成研究

参照"Mediator/Wrapper"体系结构,利用混合本体的概念设计了基于本体的保险企业数据集成模型,以六元组的方式为例完成了对保险公司新型人寿保险信息的混合本体定义.利用本体技术通过领域建模描述该领域的概念及其相互关系,可以有效地完成保险公司异构数据的集成和深层的数据共享,提供统一的信息查询及用户视图,进而提高保险公司管理支持和决策的可靠性和准确性.     

Semantic business process integration based on ontology alignment

Innovation and agility should be provided to businesses by efficient collaboration (i.e., communication and sharing) between them. However, semantic heterogeneity between business processes is a serious problem for automatically supporting cooperation processes (e.g., knowledge sharing and querying-based interactions) between businesses. In order to overcome this problem, we propose a novel framework based on aligning business ontologies for integrating heterogeneous business processes. We can consider two types of alignment processes; (i) manual alignment for building a whole business process ontology in a business process management (BPM) system and (ii) automated alignment between business processes of different BPM systems. Thereby, the optimal integration between two business processes has to be discovered to maximize the summation of a set of partial similarities between semantic components consisting of the business processes. In particular, the semantic component are extracted from semantic annotations of business processes. For evaluating the proposed system, we have conducted experimentations by using 22 business process management systems, which are organized as six business alliances. We have assumed that business processes in a same BPM system should be built with a common ontologies. The proposed alignment method has shown about 71.3% of precision (65.4% of recall). In addition, we found out that alignment results are dependent on some characteristics of ontologies (e.g., depth and number of classes).     

基于ontology的分布式异构数据库集成研究

在分析比较了几种异构、分布数据访问技术的基础上,设计实现了一个分布式异构数据库集成系统,该系统与以往系统不同的是采用ontology技术解决语义冲突问题,运用XML技术屏蔽结构异构,以Web Service来实现网络的平滑传输,从而可以更好地实现分布式异构数据库的集成.     

Inductive inference and argumentation methods in modern intelligent decision support systems

Improvement of the classification quality for the generalization problem is considered. In order to improve the classification models produced by generalization algorithms, it is proposed to use argumentation methods based on defeasible reasoning with justification degrees. New methods and algorithms are proposed, and experimental results on various test data sets are described, including instances with noisy initial data.     

A generic framework for multi-criteria decision support in eco-friendly urban logistics systems

In this paper, we present a multi-objective shortest path evolutionary algorithm for comprehensive solutions to real-world manifestations of the classical vehicle routing problem. The shift from being a purely academic pursuit is highlighted by the introduction of a generic optimization framework which accommodates a variety of attributes that commonly occur in industrial applications. Specifically, the paper's main contribution are as follows: (1) consideration for the following real-world constraints: (a) time windows at customer locations, (b) simultaneous pickup and delivery demands, (c) a heterogeneous fleet of vehicles, and (d) the heterogeneity of traffic congestion levels in urban transportation networks; (2) assimilation of all the above attributes into a multi-objective program which aims to minimize environmental impact, while simultaneously addressing the overall operational costs of the routing solution and service quality concerns; a feat that has not been fully realized by known intelligent systems according to the authors’ best knowledge. In order to showcase the efficacy of the proposed algorithm, it is first tested on existing benchmark instances and then applied on a pair of real-world industrial examples from Singapore. These industrial examples serve as a source of new benchmarks which facilitate the study of different routing constraints and their effects on the economic and environmental viability of urban logistics systems.