On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment.     

DSS中模型的三维层次表示法

该文将DSS中的模型分为三个层次：理论模型层属性模型层和数值模型层,文章分别讨论了每层模型的结构、作用和表示方法,以及各层次之间的相互关系和转换算法。利用三维层次模型表示方法,成功地开发了一个决策支持系统生成器。     

The Importance of Active Exploration,Optical Flow,and Task Alignment for Spatial Learning in Desktop 3D Environments

The Decision Support Systems (DSS) field has grown rapidly, drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure and poor linkage between research and practice. This article presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and also structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e., functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision makers need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are process models, which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across aspects and/or alternatives; information control techniques, which help in storage, retrieval, organization, and integration of data and knowledge; analysis and reasoning techniques, which support application of problem-specific expert reasoning procedures; representation aids, which assist in expression and manipulation of a specific representation of a decision problem; and judgment amplification/refinement techniques, which help in quantification and debiasing of heuristic judgments. Additional distinctions are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.     

Can DSS evolve without changing our view of the concept of ‘problem’?

DSS have almost exclusively been presented in the context of problem solving. But the term ‘problem’ is never defined. It is unfortunately a fairly ambiguous term whose meaning oscillates from the observation of an unsatisfactory, objective reality which must be corrected, to the subjective representation of one or more actors faced with a reality which he or they perceived as unsatisfactory. The first of these interpretations (i.e., a problem as an unsatisfactory objective reality) implicitly dominates the DSS literature and design methods, which does not avoid the hidden major stumbling block of ‘problem definition’. We believe that the adoption of the second interpretation (i.e., a problem as a subjective representation) is a better guarantee of the effectiveness of DSS and leads to different design methods (we propose one based on a systemic method oriented towards ‘soft problems’) and opens new horizons for potential application to DSS.     

Temporal semantics and natural language processing in a decision support system

This paper is an attempt to address in an integrated fashion three issues of interest in the decision support system (DSS) context. First, to deal with dynamic problem environments, a mechanism is proposed to represent data and decision models with explicit temporal aspects. Second, in order to provide a natural language interface to such a DSS, this representation of temporal knowledge is used to process queries including those with explicit temporal aspects. Third, the issue of computational efficiency has been addressed using the language of equational logic as a deduction oriented language for problem solving as well as describing environmental knowledge. An example describing the salient features of the system is presented.     

面向对象技术在DSS模型库中的应用研究

根据面向对象技术的原理和DSS模型库的原理,探讨了基于面向对象技术的DSS模型库管理系统的设计和实现方法。给出了基于面向对象技术的模型类的结构定义和表示方法,使用了模型字典来进行模型的存储和管理。该模型库使用Delphi语言实现,已应用于银行信贷决策支持系统中,取得了令人满意的效果。     

基于Agent的面向DSS的模型库系统研究

智能性是决策支持系统未来的发展方向,为了提高决策支持系统模型库的智能性,文章将Agent的智能性融人到模型库系统中,提出了一种基于Agent的模型库系统构建方案。主要研究基于Agent的模型表示、模型选择和模型组合技术,模型库系统的架构方案及各功能Agent模型,详细阐述了模型库系统各部分的工作原理,并在JADE平台完成了模型库系统原型的开发。实验证明：所设计的基于Agent的模型库系统能很好的解决模型表示、模型组合和模型选择问题,比传统模型库系统具有更高的智能性。     

SIMILARITY‐BASED RETRIEVAL WITH STRUCTURE‐SENSITIVE SPARSE BINARY DISTRIBUTED REPRESENTATIONS

We present an approach to similarity‐based retrieval from knowledge bases that takes into account both the structure and semantics of knowledge base fragments. Those fragments, or analogues, are represented as sparse binary vectors that allow a computationally efficient estimation of structural and semantic similarity by the vector dot product. We present the representation scheme and experimental results for the knowledge base that was previously used for testing of leading analogical retrieval models MAC/FAC and ARCS. The experiments show that the proposed single‐stage approach provides results compatible with or better than the results of two‐stage models MAC/FAC and ARCS in terms of recall and precision. We argue that the proposed representation scheme is useful for large‐scale knowledge bases and free‐structured database applications.     

CSG-EESI: a new solid representation scheme and a conversion expertsystem

A representation scheme is proposed for describing 3-D mechanical parts and structural bodies which are formed from planes and quadratic faces. Since the method combines features of the constructive solid geometry (CSG) representation and an extension of the enhanced (EESI) spherical image representation (ESI), it is designated the CSG-EESI. In this scheme, the body model is roughly divided into two levels: the higher level corresponds to a restricted CSG tree that contains the structural information describing how the various subparts form the body; the lower level contains the geometric information for those simple subparts and represents them by an extension of enhanced spherical images. The scheme can be used both as the medium between pictorial models and relational models and as an internal model to facilitate the recognition of bodies. An expert system written in C-PROLOG on a VAX 11/750 is presented that converts BR-like models into the CSG-EESI representation is presented     

Mapping the intellectual structure of research in decision support systems through author cocitation analysis (1971–1993)

This study applies factor analysis of an author cocitation frequency matrix derived from a database file that consists of a total of 23,768 cited reference records taken from 944 citing articles. Factor analysis extracted eleven factors consisting of six major areas of DSS research (group DSS, foundations, model management, interface systems, multicriteria DSS, and implementation) and five contributing disciplines (multiple criteria decision making, cognitive science, organizational science, artificial intelligence, and systems science). This research provides hard evidence that the decision support system has made meaningful progress over the past two decades and is in the process of solidifying its domain and demarcating its reference disciplines. Especially, much progress has been made in the subareas of model management such as representation, model base processing, model integration, and artificial intelligence application to model management leading towards the development of a theory of models. To facilitate the transition from the pre- to post-paradigm period in DSS research, this study has completed important groundwork.     

Decision support in time series modeling by pattern recognition

This research is aimed at presenting a new, pattern recognition-based DSS scheme for the time series model identification. The scheme is based on two principles: pattern matching and inductive learning. Pattern matching is used to classify a pattern of the time series into one of the autoregressive moving-average models. The pattern is obtained from the extended sample autocorrelations of the time series. Inductive learning is used to enhance the capability of recognizing input patterns, and linear discriminants are used to discriminate one pattern from the others. To implement the idea, a decision support system named DSSTSM was designed and a prototype was developed on the microcomputer. Experimental results show that the combination of the pattern recognition principles with a DSS can yield a promising solution to the time series modeling.     

基于社交特征的多维度文本表示方法

Web文本表示方法作为所有Web文本分析的基础工作,对文本分析的结果有深远的影响。提出了一种多维度的Web文本表示方法。传统的文本表示方法一般都是从文本内容中提取特征,而文档的深层次特征和外部特征也可以用来表示文本。本文主要研究文本的表层特征、隐含特征和社交特征,其中表层特征和隐含特征可以由文本内容中提取和学习得到,而文本的社交特征可以通过分析文档与用户的交互行为得到。所提出的多维度文本表示方法具有易用性,可以应用于各种文本分析模型中。在实验中,改进了两种常用的文本聚类算法——K-means和层次聚类算法,并命名为多维度K-means MDKM和多维度层次聚类算法MDHAC。通过大量的实验表明了本方法的高效性。此外,我们在各种特征的结合实验结果中还有一些深层次的发现。     

Truncated Integration for Simultaneous Simulation of Sintering Using a Separated Representation

Recent developments of multidimensional solvers using separated representation make it possible to account for the multidimensionality of mechanical models in materials science when doing numerical simulations. This paper aims to extend the separated representation to inseparable equations using an efficient integration scheme. It focuses on the dependence of constitutive equations on material coefficients. Although these coefficients can be optimized using few experimental results, they are not very well known because of the natural variability of material properties. Therefore, the mechanical state can be viewed as a function depending not only on time and space variables but also on material coefficients. This is illustrated in this paper by a sensitivity analysis of the response of a sintering model with respect to variations of material coefficients. The considered variations are defined around an optimized value of coefficients adjusted by experimental results. The proposed method is an incremental method using an extension of the integration scheme developed for the Hyper Reduction method. During the incremental solution, before the adaptation of the representation, an assumed separation representation is used as a reduced-order model. We claim that a truncated integration scheme enables to forecast the reduced-state variables related to the assumed separated representation. The fact that the integrals involved in the formulation can not be written as a sum of products of one-dimensional integrals, this approach reduces the extent of the integration domain.     

融合CBR和本体的决策支持系统框架及应用

分析了将本体与CBR方法结合开发智能决策支持系统的优点,设计了基于该方案的决策支持系统的总体框架,着重介绍了基于本体的案例组织和表示方法以及引入本体后案例检索过程中的相似度计算方法,基于该方案,设计和开发了电子政务项目审批决策支持系统。应用结果表明,利用本体对案例包含的知识进行提取和表示,再利用这些知识对案例进行组织和检索,有效地提高了案例检索的查准率和查全率。     

应用遗传算法的DSS模型设计研究

在DSS模型设计中，采用遗传程序设计可以更灵活地处理遗传算法中的表示问题。本文通过讨论遗传算法的特性，DSS模型库的特点而研究了DSS模型遗传程序设计的方法。     

广义模型服务器的设计与实现

模型的管理与运行一直是决策支持系统的核心,为了有效地实现广义模型的管理,组合和运行,文章设计和实现了一个广义模型服务器。它基于TCP／IP协议,采用三层客户／服务器机制,对模型的表示和运行进行了规范化,通过命令语言在客户端进行模型的管理和运行。该服务器可以对模型、算法、实例、工具等决策支持资源进行有效地管理,它既可广泛地应用于各领域的决策支持系统的开发,也可单独作为决策支持工具进行各种领域问题的辅助决策,文中系统地介绍了该服务器的设计和实现的理论和技术问题。     

Constructive Volume Geometry

We present an algebraic framework, called Constructive Volume Geometryn (CVG), for modelling complex spatial objects using combinational operations. By utilising scalar fields as fundamental building blocks, CVG provides high-level algebraic representations of objects that are defined mathematically or built upon sampled or simulated datasets. It models amorphous phenomena as well as solid objects, and describes the interior as well as the exterior of objects. We also describe a hierarchical representation scheme for CVG, and a direct rendering method with a new approach for consistent sampling. The work has demonstrated the feasibility of combining a variety of graphics data types in a coherent modelling scheme.     

Streaming association rule (SAR) mining with a weighted order-dependent representation of Web navigation patterns

This paper considers a problem of finding predictive and useful association rules with a new Web mining algorithm, a streaming association rule (SAR) model. We first adopt a weighted order-dependent scheme (assigning more weights for early visited pages) rather than taking a traditional Boolean scheme (assigning 1 for visited and 0 for non-visited pages). This way, we intend to improve the limited representation of navigation patterns in previous association rule mining (ARM) algorithms. We also note that most traditional association rule models are not scalable because they require multiple scans of all records to re-calibrate a predictive model when there are new updates in original databases. The proposed SAR model takes a “divide-and-conquer” approach and requires only single scan of data sets to avoid the curse of dimensionality. Through comparative experiments on a real-world data set, we show that prediction models based on a weighted order-dependent representation are more accurate in predicting the next moves of Web navigators than models based on a Boolean representation. In particular, when combined with several heuristics developed to eliminate redundant association rules, SAR models show a very comparable prediction accuracy while maintaining a small fraction of association rules compared to traditional ARM models. Finally, we quantify and graphically show the significance or contribution of each pages to forming unique rule sets in each database segments.     

Letter to the Editor

This paper examines how an object called a decision support system (DSS) is a system from the point of view of GST (general systems theory) and how GST is related with the development of DSS in a positive way. Since a DSS is a complicated system, it cannot be described by one model. We will proceed the consideration in the following different levels: functional level, structural level, process level and physical level. We will show that on each level GST provides a useful framework to deal with a DSS and the general formulation by GST directs what is to be done in the development of DSS. On the progress of the development we have found GST can help us with conceptualizing a DSS which is most important in finding a correct research direction. The presented models are the basic framework of our current system, a product of GST.