A generic architecture to synchronise design models issued from heterogeneous business tools: towards more interoperability between design expertises

Product development involves many experts collaborating to the same design goal. Every expert has his own formalisms and tools leading to a high heterogeneity of information systems supporting design activities. Interoperability became a major challenge to avoid information incompatibility along the product life cycle. To synchronise heterogeneous representations of product will be a major step to integrate expert activities. In this paper, the authors propose a meta-model framework to connect together heterogeneous design models. This meta-model framework is used to formalise possible interactions between heterogeneous representations. Interaction formalisation is considered as a key point to synchronise heterogeneous models and to provide more interoperability between various computer assisted systems. The synchronisation loop is also presented as a major sequence of activities to manage collaborative design. Tools to support synchronisation are proposed. However, through a basic case study, authors highlight what can be automated and where human intervention is still expected.     

Network and multidimensional representations of the declarative knowledge of human-computer interface design experts

A two-part experiment investigated human computer interface (HCI) experts' organization of declarative knowledge about the HCI. In Part 1, two groups of experts in HCI design—human factors experts and software development experts—and a control group of non-experts sorted 50 HCI concepts concerned with display, control, interaction, data manipulation and user knowledge into categories. In the second part of the experiment, the three groups judged the similarity of two sets of HCI concepts related to display and interaction, respectively. The data were transformed into measures of psychological distance and were analyzed using Pathfinder, which generates network representations of the data, and multidimensional scaling (MDS), which fits the concepts in a multidimensional space. The Pathfinder networks from the first part of the experiment differed in organization between the two expert groups, with human factors experts' networks consisting of highly interrelated subnetworks and software experts' networks consisting of central nodes and fewer, less interconnected sub-networks. The networks also differed across groups in concepts linked with such concepts as graphics, natural language, function keys and speech recognition. The networks of both expert groups showed much greater organization than did the non-experts' network. The network and MDS representations of the concepts for the two expert groups showed somewhat greater agreement in Part 2 than in Part 1. However, the MDS representations from Part 2 suggested that software experts organized their concepts on dimensions related to technology, implementation and user characteristics, whereas the human factors experts' organized their concepts more uniformly according to user characteristics. The discussion focuses on (1) the differences in cognitive models as a function of the amount and type of HCI design experience and (2) the role of cognitive models in HCI design and in communications within a multidisciplinary design team.     

Aggregating linguistic expert knowledge in type-2 fuzzy ontologies

In many industrial contexts, knowledge and data provided by experts are imprecise as there seems to be an understanding that “experts do not need precise details as they understand anyway what is meant”. The imprecision inherent in the knowledge that experts acquire in their practice require decision support tools that can be tailored to the specific application contexts to aid complex decisions. As a specific example, expert knowledge expressed in linguistic terms is not precisely structured and concepts are not defined specifically enough in order to be easy to use and process. If we want to represent and use expert knowledge for knowledge-based systems on a general level, that is easily adaptable, we need to find ways to represent and process knowledge elements; our approach is to use interval-valued fuzzy sets, fuzzy ontology and aggregation operators. We show that these instruments will offer us a novel approach for aggregation of imprecise data to obtain actionable knowledge to aid complex decisions. The framework is described and the approach is shown through the context of a fuzzy wine ontology; the problem formulation resembles many features of important and complex decision making problems found in different industries. We describe the potential application of the framework in the case of paper machine maintenance. A web-based application is introduced to better demonstrate the benefits decision-makers can receive from the proposed framework. Additionally, we present an approach to utilize the framework in finding consensual solutions in situations involving several experts.     

Recursive approach to combine expert knowledge and data-driven RSW weldability certification decision making process

Data-driven techniques have shown promising results in the analysis and understanding of complex welding processes. Data analytics play a significant role to turn data into valuable insights to assist in the weldability certification decision-making for Resistance Spot Welding (RSW) as well. However, to successfully perform the associated data analytics, domain knowledge is essential to construct more ‘sense-making’ analytics models, as often the models cannot properly capture the nuances of the domain and do not properly indicate the relationship among the RSW concepts and parameters. Thus, machine learning models developed from rough experimental data often do not provide models meaningful and sensible to the domain expert. In this article, we employ a recursive approach between the domain experts and data-driven models so that the knowledge of the domain experts can be integrated into the weldability certification decision-making process. An ontology-based semantic knowledge framework supports this recursive communication while helping the experts to instil more confidence in the developed analytics models. The collaborative and recursive approach implemented in this study helps the domain experts to tap into their domain knowledge and form expert opinions using the formalized semantic RSW concepts and decision rules. The expert opinions are then used to learn new knowledge about the RSW domain and transform the RSW datasets by incorporating significant features that were not included in the earlier models. The transformed datasets help us to develop improved machine learning models, which in turn work as a new source of semantic knowledge, as we have discovered through our pilot implementation.     

Toward Reducing Human Involvement in Validation of Knowledge-Based Systems

Human experts employed in validation exercises for knowledge-based systems (KBSs) often have limited time and availability. Furthermore, they often have different opinions from each other as well as from themselves over time. We address this situation by introducing the use of validation knowledge used in prior validation exercises for the same KBS. We present a validation knowledge base (VKB) that is the collective best experience of several human experts. The VKB is constructed and maintained across various validation exercises, and its primary benefits are given as follows: 1) more reliable validation results by incorporating external knowledge and 2) decrease of the experts' workload. We also present the concept of validation expert software agents (VESAs), which represent a particular expert's knowledge. VESA is a software agent corresponding to a specific human expert. It models the validation knowledge and behavior of its human counterpart by analyzing similarities with the responses of other experts. After a learning period, it can be used to temporarily substitute for its corresponding human expert. We also describe experiments with a small prototype system to evaluate the usefulness of these concepts     

A dominance-based rough set approach to Kansei Engineering in product development

Keen competitions in the global market have led product development to a more knowledge-intensive activity than ever, which requires not only tremendous expert knowledge but also effective analysis of design information. Kansei Engineering as a customer-oriented methodology for product development, often has to analyse imprecise design information inherent with nonlinearity and uncertainty. This paper proposes a systematic approach to Kansei Engineering based on the dominance-based rough set theory. Two novel concepts known as category score and partition quality have been developed and incorporated into the proposed approach. The new approach proposed is able to identify and analyse two types of inconsistencies caused by indiscernibility relations and dominance principles respectively. The result of an illustrative case study shows that the proposed approach can effectively extract Kansei knowledge from imprecise design information, and it can be easily integrated into an expert system for customer-oriented product development.     

Predicting recurring concepts on data-streams by means of a meta-model and a fuzzy similarity function

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behavior to the volatile underlying data distributions, what has been called concept drift. It is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model.To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed.Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones.This work faces the aforementioned open issues by means of the MM-PRec system, that integrates a meta-model mechanism and a fuzzy similarity function. The theoretical proposal of MM-PRec is also validated in this paper by means of different experiments using both synthetic and real datasets.     

基于产生式系统的知识建模

如何建立系统完整的专家知识模型,并将其快速映射为面向计算机推理的人工智能语言是当前专家系统研究的重点和难点,而知识表示是其中的关键.本文将模型驱动的思想引入到专家系统领域,定义了一种基于不确定性产生式知识表示的元模型,设计并实现了相应的图形化建模工具和转换引擎,并基于此提出一种新的产生式系统应用框架.该框架在某健康信息评估专家系统中获得了成功的应用.     

Knowledge-based automation of a design method for concurrentsystems

This paper describes a knowledge-based approach to automate a software design method for concurrent systems. The approach uses multiple paradigms to represent knowledge embedded in the design method. Semantic data modeling provides the means to represent concepts from a behavioral modeling technique, called Concurrent Object-Based Real-time Analysis (COBRA), which defines system behavior using data/control flow diagrams. Entity-relationship modeling is used to represent a design metamodel based on a design method, called COncurrent Design Approach for Real-Time Systems (CODARTS), which represents concurrent designs as software architecture diagrams, task behavior specifications and module specifications. Production rules provide the mechanism for codifying a set of CODARTS heuristics that can generate concurrent designs based on semantic concepts included in COBRA behavioral models and on entities and relationships included in CODARTS design metamodels. Together, the semantic data model, the entity-relationship model, and the production rules, when encoded using an expert system shell, compose CODA, an automated designer's assistant. CODA is applied to generate 10 concurrent designs for four real-time problems. The paper reports the degree of automation achieved by CODA. The paper also evaluates the quality of generated designs by comparing the similarity between designs produced by CODA and human designs reported in the literature for the same problems. In addition, it compares CODA with four other approaches used to automate software design methods     

面向综合集成研讨环境的专家兴趣建模方法

现有兴趣模型难以直接描述综合集成研讨环境(CWME)中的专家兴趣。为此,提出一种面向CWME的专家兴趣建模方法。该方法采用非负矩阵分解技术自动生成研讨兴趣话题,通过分析专家发言特征词与兴趣话题的关系,生成专家兴趣信息,整合专家兴趣信息后得到层次化组织的专家兴趣模型。实验结果表明,应用该模型能够较好地实现研讨领域预测和针对具体专家的个性化信息推荐。     

Towards a quality meta-model for information systems

All models of information system (IS) quality postulate two constructs, namely ‘quality’ and ‘model’. These concepts are seldom explicitly discussed and defined in connection with IS. Together, they constitute an information system quality meta-model. Compared to lower level models, a meta-model is likely to be more applicable in a wider variety of contexts. This article aims, firstly, to validate and develop further an initial IS quality meta-model that emerged from two previous studies. Secondly, it is an account of a real quality modeling process, in connection with the development of an Education Management Information System (EMIS) in Tanzania, and one that can be referenced by other researchers. This case is used to discover how the meta-model can be used as part of system development process, with a view to instantiating system- and attribute-specific quality models. The study supports the general validity of a two-part and three-level quality meta-model. It further suggests that quality is by its nature relative and that the essence of quality is embodied in relationships between the information system and its context. The meta-model functions well as a safeguard that can prevent developers from neglecting important aspects of quality design. In addition, it generates relevant questions for future research.     

Two techniques of sensitivity and uncertainty analysis of fuzzy expert systems

Problems characterized by qualitative uncertainty described by expert judgments can be addressed by the fuzzy logic modeling paradigm, structured within a so-called fuzzy expert system (FES) to handle and propagate the qualitative, linguistic assessments by the experts. Once constructed, the FES model should be verified to make sure that it represents correctly the experts’ knowledge. For FES verification, typically there is not enough data to support and compare directly the expert- and FES-inferred solutions. Thus, there is the necessity to develop indirect methods for determining whether the expert system model provides a proper representation of the expert knowledge. A possible way to proceed is to examine the importance of the different input factors in determining the output of the FES model and to verify whether it is in agreement with the expert conceptualization of the model. In this view, two sensitivity and uncertainty analysis techniques applicable to generic FES models are proposed in this paper with the objective of providing appropriate tools of verification in support of the experts in the FES design phase. To analyze the insights gained by using the proposed techniques, a case study concerning a FES developed in the field of human reliability analysis has been considered.     

Trading off usability and security in user interface design through mental models

The aim of this paper is to establish the foundations for developing a mental model that bridges the gap between usability and security in user-centred designs. To this purpose, a meta-model has been developed to align design features with the users’ requirements through tacit knowledge elicitation. The meta-model describes the combinatorial relationships of Security, Usability and Mental (SUM) and how these components can be used to design a usable and secure system. The SUM meta-model led to the conclusion that there is no antagonism between usability and security. However, the degree of usable security depends on the ability of the designer to capture and implement the user’s tacit knowledge. In fact, the SUM meta-model seeks the dilution of the trading-off effects between security and usability through compensating synergism of the tacit knowledge. A usability security cognitive map has been developed for the major constituents of usability and security to clarify the interactions and their influences on the meta-model stipulations. The three intersecting areas of the three components’ relationships are manipulated to expand the Optimal Equilibrium Solution (OES) (δ) expanse. To put the SUM meta-model into practice, knowledge management principles have been proposed for implementing user-centred security and user-centred design. This is accomplished by using collaborative brainpower from various knowledge constellations to design a system within the user’s current and future perception boundaries. Therefore, different knowledge groups, processes, techniques, tactics and practices have been proposed for knowledge transfer and transformation during the mental model development.     

Development of control mechanisms to support coherency of product model during cooperative design process

Product development became an increasingly collaborative and distributed activity. Collaborative design process gathers experts from different backgrounds and areas for a common objective about product development. An effective exchange support is expected to share and integrate design knowledge avoiding conflicts between designers. The management of heterogeneous product representation is a major step to integrate expert activities. To successfully manage this process, this paper proposes: (1) A research experimental platform for cooperative design in product development processes. (2) A new constraint based model to maintain complex relationships in multi-disciplinary cooperative design. (3) A model differentiation technique, which identifies differences and conflicts between models. (4) A Meta-rule concept, which controls the constraint network in design process, leading to a new notification mechanism to present conflict to all corresponding actors.     

Knowledge engineering revisited

About six years ago a paper on Knowledge Engineering was published in this Journal. This paper attempts to extend and elaborate on the ideas and concepts discussed earlier. Four major problems are addressed: Preservation of knowledge, proliferation of knowledge, dissemination of knowledge, and application of knowledge. The design principles for both rule-based expert systems and pattern-directed expert systems are discussed and compared.     

一个事件驱动的中间件平台

事件驱动具有异步多点通信的优点,引起了广泛的研究兴趣。提出了一个由基层和元层两层结构组成的自适应中间件框架,元层主要由接口元模型、组装元模型和感知元模型3个相互独立的模型组成。感知元模型负责数据在对象间流动,为应用提供运行时的环境。给出了感知元模型的设计和实现方法,基于有限状态自动机和时序逻辑提出了系统的形式化规范。为兼顾系统和应用两级并发,系统设计结合了事件和线程。图形用户接口系统在平台上的实现证明了平台在开发复杂的并发应用方面有着广阔的前景。     

矿山语义物联网自动语义标注方法

针对目前矿山领域异构数据融合时先验知识获取困难、物联网本体库实时性差、实例对象数据手动标注方式效率较低等问题,提出了一种矿山语义物联网自动语义标注方法。给出了传感数据语义化处理框架:一方面,确定本体的专业领域和范畴,通过重用流注释本体(SAO)构建领域本体,作为驱动语义标注的基础;另一方面,使用机器学习方法对感知数据流进行特征提取与数据分析,从海量数据中挖掘出概念间的关系;通过数据挖掘知识来驱动本体的更新与完善,实现本体的动态更新、拓展与更精确的语义标注,增强机器的理解力。以矿井提升系统主轴故障为例阐述从本体到实例化的语义标注过程:结合领域专家知识及本体重用,采用"七步法"建立矿井提升系统主传动故障本体;为了加强实例数据属性描述的准确性,使用主成分分析法(PCA)与K-means聚类方法对数据集进行降维和分组,提取出数据属性与概念的关系;通过基于语义Web的规则语言(SWRL)标注具体先行条件与后续概念的关系,优化领域本体。实验结果表明:在本体实例化过程中,可利用机器学习技术从传感数据中自动提取概念,实现传感数据的自动语义标注。     

Development of a product configuration system with an ontology-based approach

Product configuration is a crucial means to implement the mass customization paradigm by assembling a set of customizable components to satisfy both customers’ needs and technical constraints. With the aim of enabling efficient and effective development of product configuration systems by reusing configuration knowledge, an ontology-based approach to modeling product configuration knowledge is presented in this paper. The ontology-based product configuration models are hierarchically organized. At the lower level, a configuration meta-model is defined. Based on this meta-model, domain-specific configuration knowledge can be derived by reusing or inheriting the classes or relations in the meta-model. Configuration models are formalized using OWL (Ontology Web Language), an ontology representation language developed by W3C. As a result, configuration models have well-defined semantics due to the logic semantics of OWL, making it possible to automatically detect inconsistencies of configuration knowledge bases. Furthermore, configuration constraints are represented in SWRL, a rule language based on OWL. Finally, actual configuration processes are carried out using JESS, a rule engine for the Java platform, by mapping OWL-based configuration facts and SWRL-based configuration constraints into JESS facts and JESS rules, respectively. The proposed methodology is illustrated with an example for configuring the ranger drilling machine.     

Online updating belief rule based system for pipeline leak detection under expert intervention

A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for pipeline leak detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the pipeline leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for pipeline leak detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and pipeline operating data, and validated by testing data. All training and testing data are collected from a real pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system.