Long-term knowledge evolution modeling for empirical engineering knowledge

In this era of knowledge economy, appropriate management of the rapidly evolving knowledge is a real and urgent issue for factories and enterprises, in order to maintain the competitive edges. However, facing the onerous analysis required for understanding the long-term knowledge evolution, especially the evolving of empirical knowledge in the engineering field, effective and comprehensive modeling methods for knowledge evolution are absent. In this paper, a novel knowledge evolution modeling method is proposed for portraying the long-term evolution of empirical engineering knowledge (EEK) and assisting engineers in comprehending the evolving history. Three phases, EEK elicitation and formalization, EEK networks foundation, and family-tree evolution model construction, are included in the modeling method. This method is developed using natural language processing, semantic similarity calculation, fuzzy neural network prediction, clustering algorithm, and latent topic extraction techniques. To evaluate the performance of the proposed modeling method, an evolution model of empirical knowledge in computer-aided design (CAD) is constructed and then verified. Experimental results show that the proposed method outperforms the former approaches in feasibility and effectiveness, and hence opens up a better way of further understanding the long-term evolution course of EEK.     

Safety intelligence: An exploration of senior managers' characteristics

Senior managers can have a strong influence on organisational safety. But little is known about which of their personal attributes support their impact on safety. In this paper, we introduce the concept of ‘safety intelligence’ as related to senior managers' ability to develop and enact safety policies and explore possible characteristics related to it in two studies. Study 1 (N = 76) involved direct reports to chief executive officers (CEOs) of European air traffic management (ATM) organisations, who completed a short questionnaire asking about characteristics and behaviours that are ideal for a CEO's influence on safety. Study 2 involved senior ATM managers (N = 9) in various positions in interviews concerning their day-to-day work on safety. Both studies indicated six attributes of senior managers as relevant for their safety intelligence, particularly, social competence and safety knowledge, followed by motivation, problem-solving, personality and interpersonal leadership skills. These results have recently been applied in guidance for safety management practices in a White Paper published by EUROCONTROL.     

Improving the management of product lines by performing domain knowledge extraction and cross product line analysis

ContextIncrease in market competition is one of the main reasons for developing and maintaining families of systems, termed Product Lines (PLs). Managing those PLs is challenging, let alone the management of several related PLs. Currently, those PLs are managed separately or their relations are analyzed assuming explicit specification of dependencies or use of an underlying terminology. Such assumptions may not hold when developing the PLs in different departments or companies applying various engineering processes.ObjectiveIn this work we call for utilizing the knowledge gained from developing and maintaining different PLs in the same domain in order to recommend on improvements to the management of PLs.MethodThe suggested approach conducts domain knowledge extraction and cross PL analysis on feature diagrams – the main aid for modeling PL variability. The domain knowledge is extracted by applying similarity metrics, clustering, and mining techniques. Based on the created domain models, the approach performs cross PL analysis that examines relations in the domain models and generates improvement recommendations to existing PLs and overall management recommendations (e.g., merging or splitting PLs).ResultsThe approach outcomes were evaluated by humans in a domain of mobile phones. The evaluation results may provide evidence that the outcomes of the approach in general and its recommendations in particular meet human perception of the given domain.ConclusionWe conclude that through domain knowledge extraction and cross PL analysis the suggested approach may generate recommendations useful to the management of individual PLs, as well as to the overall management of different PLs in the same domain.     

An adaptive scheme for admission control in ATM networks

This paper presents a real time front-end admission control scheme for ATM networks. A call management scheme which uses the burstiness associated with traffic sources in a heterogeneous ATM environment to effect dynamic assignment of bandwidth is presented. In the proposed scheme, call acceptance is based on an on-line evaluation of the upper bound on cell loss probability which is derived from the estimated distribution of the number of calls arriving. Using this scheme, the negotiated quality of service will be assured when there is no estimation error. The control mechanism is effective when the number of calls is large, and tolerates loose bandwidth enforcement and loose policing control. The proposed approach is very effective in the connection oriented transport of ATM networks where the decision to admit new traffic is based on the a priori knowledge of the state of the route taken by the traffic.     

A risk management ontology for Quality-by-Design based on a new development approach according GAMP 5.0

A new approach to the development of a risk management ontology is presented. This method meets the requirements of a pharmaceutical Quality by Design approach, good manufacturing practice and good automated manufacturing practice. The need for a risk management ontology for a pharmaceutical environment is demonstrated, and the term “ontology” is generally defined and described with regard to the knowledge domain of quality risk management.To fulfill software development requirements defined by good manufacturing practice regulations and good automated manufacturing practice 5.0 for the novel development approach, we used a V-model as a process model, which is discussed in detail. The development steps for the new risk management ontology, such as requirement specification, conceptualization, formalization, implementation and validation approach, are elaborated.     

An ontology change management approach for facility management

Facility management (FM) or technical property management is an approach to operate, maintain, improve and adapt buildings and infrastructures of organizations. A FM project requires the cooperation of many actors from different domains so it has to be automated in a constrained collaborative environment. This paper proposes a new approach for ontology change management applied on facility management of such projects. The industrial challenge is, firstly, to ensure consistency of a FM project knowledge from the construction phase to the technical property management phase (after delivery). Secondly, it has to provide to each actor of the project a personal up-to-date “view” of the building knowledge related to its business profile and allow its evolution. The scientific approach, called OntoVersionGraph, is a change management methodology for managing ontology life cycle including ontology evolution and versioning features, in conjunction with contextual view modeling. Its contribution is the impact management of changes between the ontology and its different views.     

A knowledge-based object modeling advisor for developing quality object models

Object models or class diagrams are widely used for capturing information system requirements in terms of classes with attributes and operations, and relationships among those classes. Although numerous guidelines are available for object modeling as part of requirements modeling, developing quality object models has always been considered a challenging task, especially for novice systems analysts in business environments. This paper presents an approach that can be used to support the development of quality object models. The approach is implemented as a knowledge-based system extension to an open source CASE tool to offer recommendations for improving the quality of object models. The knowledge component of this system incorporates an ontology of quality problems that is based on a conceptual model quality framework commonly found in object models, the findings of related empirical studies, and a set of analysis patterns. The results obtained from an empirical evaluation of the prototype demonstrate the utility of this system, especially with respect to recommendations related to the model completeness aspect of semantic quality.     

基于元模型的知识管理建模工具设计与开发

知识管理建模是构建组织知识管理系统的基础．组织知识管理建模远比信息管理建模复杂，迫切需要建模工具的支持．从知识内容和管理两个层面分析提出知识管理的建摸需求，以此为依据建立知识管理元模型．组织知识管理元模型包括组织战略、组织结构、组织流程和组织知识地图四个子模型．为了方便建模工具的开发，用组织知识管理对象建模语言集成各子模型对应的建模语言．在此基础上实现了一个知识管理建模工具．     

Provoking creativity: imagine what your requirements could be like

Requirements engineering isn't recognized as a creative process. However, as new systems and products emerge, stakeholders are increasingly creating and inventing ideas that they express as requirements. Requirements engineering, with its focus on elicitation, analysis, and management, has yet to fully grasp this trend. We applied techniques to encourage creative thinking during the requirements process for a software-based system in a naturally conservative domain - air traffic management (ATM). We applied unusual theories, such as analogical reasoning from cognitive science, to underpin the use of these techniques, and we report basic results and lessons learned. We focus on the creativity techniques we applied (see the sidebar) and demonstrate them with examples from the ATM domain.     

Ontology-based automated support for goal–use case model analysis

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. To ensure the quality of such modeled artifacts, a detailed model analysis needs to be performed. However, current requirements engineering approaches generally lack reliable support for automated analysis of consistency, correctness and completeness (3Cs problems) between and within goal models and use case models. In this paper, we present a goal–use case integration framework with tool support to automatically identify such 3Cs problems. Our new framework relies on the use of ontologies of domain knowledge and semantics and our goal–use case integration meta-model. Moreover, functional grammar is employed to enable the semiautomated transformation of natural language specifications into Manchester OWL Syntax for automated reasoning. The evaluation of our tool support shows that for representative example requirements, our approach achieves over 85 % soundness and completeness rates and detects more problems than the benchmark applications.     

基于本体的军事装备知识建模及分析

针对本体建模过程中不同受众具有不同应用需求的问题,结合本体建模七步法和软件开发螺旋模型提出基于需求的螺旋反馈法.首先,明确领域知识范畴和用户需求,进行本体总体设计;其次,领域专家依据本体构建原则评估知识体系;再次,组装知识体系模型,定义本体属性,创建实例,完成本体详细设计;最后,用户评估反馈,进行新一轮的增量迭代,直至...     

Designing personal attentive user interfaces in the mobile public safety domain

In the mobile computing environment, there is a need to adapt the information and service provision to the momentary attentive state of the user, operational requirements and usage context. This paper proposes to design personal attentive user interfaces (PAUI) for which the content and style of information presentation is based on models of relevant cognitive, task, context and user aspects. Using the police work environment as the application domain, relevant attributes of these aspects are identified based on literature and domain analyses. We present a user-centered design (UCD) method for the iterative development and validation of the proposed PAUI. Application of this approach provided requirements for (1) adaptation to users’ attentive state, (2) notification, (3) information processing and task switching support and (4) user modeling. We aim at refining and validating the models and requirements through continuing empirical evaluation.     

Coupling semantic and statistical techniques for dynamically enriching web ontologies

With the development of the Semantic Web technology, the use of ontologies to store and retrieve information covering several domains has increased. However, very few ontologies are able to cope with the ever-growing need of frequently updated semantic information or specific user requirements in specialized domains. As a result, a critical issue is related to the unavailability of relational information between concepts, also coined missing background knowledge. One solution to address this issue relies on the manual enrichment of ontologies by domain experts which is however a time consuming and costly process, hence the need for dynamic ontology enrichment. In this paper we present an automatic coupled statistical/semantic framework for dynamically enriching large-scale generic ontologies from the World Wide Web. Using the massive amount of information encoded in texts on the Web as a corpus, missing background knowledge can therefore be discovered through a combination of semantic relatedness measures and pattern acquisition techniques and subsequently exploited. The benefits of our approach are: (i) proposing the dynamic enrichment of large-scale generic ontologies with missing background knowledge, and thus, enabling the reuse of such knowledge, (ii) dealing with the issue of costly ontological manual enrichment by domain experts. Experimental results in a precision-based evaluation setting demonstrate the effectiveness of the proposed techniques.     

A Latent Semantic Indexing-based approach to multilingual document clustering

The creation and deployment of knowledge repositories for managing, sharing, and reusing tacit knowledge within an organization has emerged as a prevalent approach in current knowledge management practices. A knowledge repository typically contains vast amounts of formal knowledge elements, which generally are available as documents. To facilitate users' navigation of documents within a knowledge repository, knowledge maps, often created by document clustering techniques, represent an appealing and promising approach. Various document clustering techniques have been proposed in the literature, but most deal with monolingual documents (i.e., written in the same language). However, as a result of increased globalization and advances in Internet technology, an organization often maintains documents in different languages in its knowledge repositories, which necessitates multilingual document clustering (MLDC) to create organizational knowledge maps. Motivated by the significance of this demand, this study designs a Latent Semantic Indexing (LSI)-based MLDC technique capable of generating knowledge maps (i.e., document clusters) from multilingual documents. The empirical evaluation results show that the proposed LSI-based MLDC technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision, and is capable of maintaining a good balance between monolingual and cross-lingual clustering effectiveness when clustering a multilingual document corpus.     

Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain

Model-driven development (MDD) is an approach for supporting the development of software systems, in which high-level modeling artifacts drive the production of time and effort-consuming low-level artifacts, such as the source code. Previous studies of the MDD effectiveness showed that it significantly increases development productivity, because the development effort is focused on the business domain rather than technical issues. However, MDD was exploited in the context of agent-based development in a limited way, and most of the existing proposals demonstrated the effectiveness of using MDD in this context by argumentation or examples, lacking disciplined empirical analyses. In this paper, we explore the use of MDD for agent-based modeling and simulation in the adaptive traffic signal control (ATSC) domain, in which autonomous agents are in charge of managing traffic light indicators to optimize traffic flow. We propose an MDD approach, composed of a modeling language and model-to-code transformations for producing runnable simulations automatically. In order to analyze the productivity gains of our MDD approach, we compared the amount of design and implementation artifacts produced using our approach and traditional simulation platforms. Results indicate that our approach reduces the workload to develop agent-based simulations in the ATSC domain.     

基于工作流的知识流建模与控制

知识在多个参与者之间的产生、传播与应用称为知识流.在知识密集型组织中,对业务过程的控制和对知识资产的管理具有紧密的依赖关系.工作流管理是实现业务过程控制的重要技术.当前的工作流过程元模型不支持对知识管理机制的表示.为此,提出了一个扩展的工作流过程元模型,以支持业务过程控制与知识管理的集成.在此基础上,对知识流的建模与控制进行了深入的研究.提出了一种知识流建模方法,通过5类知识流单元对知识传递与重用、人员协作与交流进行表示.针对知识流中的动态因素,研究了基于资源约束、知识需求变化和时间约束的知识流控制方法,以实现自适应的知识流控制,并给出了有关算法.为工作流技术与知识管理技术的有效结合提供了一个有益的途径.     

Building Linear Programming Models from Structural Model Components

Model management research investigates the formulation, analysis and interpretation of models. This paper focuses on the formulation aspects of modeling so that the task can be supported by decision support systems (DSS) environments. Given the knowledge intensive nature of the formulation process, the development of a modeling tool requires explicating the knowledge pertaining to modeling. This involves comprehending not only the static knowledge about model components (e.g. decision variables, coefficients, associated indices and constants), but also the process knowledge required to construct models from model pieces. The proposed top-down approach configures equations by exploiting the structural modeling knowledge inherent in equation components. The possible representation of equations at various abstraction levels is introduced, the aim being to uncover the structural model components together with the process knowledge required for their appropriate configuration. As part of developing this conceptual model, the role of semantic and syntactic information in model building is investigated. The paper proposes an approach where the formulation semantics are captured as a simple 'action-resource' view which composes models by identifying and piecing together the equation components. The process of equation construction is illustrated with examples from the linear programming (LP) modeling domain. The proposed top-down approach is contrasted with a bottom-up method.     

Metamodeling of Smart Environments: from design to implementation

A smart environment is a physical environment enriched with sensing, actuation, communication and computation capabilities aiming at acquiring and exploiting knowledge about the environment so as to adapt itself to its inhabitants’ preferences and requirements. In this domain, there is the need of tools supporting the design and analysis of applications. In this paper, the Smart Environment Metamodel (SEM) framework is proposed. The framework allows to model applications by exploiting concepts specific to the smart environment domain. SEM approaches the modeling from two different points of view, namely the functional and data perspectives. The application of the framework is supported by a set of general guidelines to drive the analysis, the design and the implementation of smart environments. The effectiveness of the framework is shown by applying it to the modeling of a real smart office scenario that has been developed, deployed and analyzed.     

A Meta-Modeling Approach for Autonomous Driving Scenario Based on STTD

In the current autonomous driving scenario modeling and simulation field, autonomous driving modeling driven by Spatio-Temporal Trajectory Data (STTD) is a key problem, which is significant to improve the safety of the system. In recent years, great progress has been achieved in the modeling and application of STTD, and the application of this data in specific fields has attracted wide attention. However, because STTD has diversity and complexity as well as massive, heterogeneous, dynamic characteristics, the research in the safety-critical field modeling still faces challenges, including unified metadata of spatio-temporal trajectories, meta-modeling methods based on STTD, data processing based on the data analysis of spatio-temporal trajectories, and data quality evaluation. In view of the modeling requirements in the field of autonomous driving, a meta-modeling approach is proposed to construct spatio-temporal trajectory metadata based on Meta Object Facility (MOF) meta-modeling system. According to the characteristics of spatio-temporal trajectory data and autonomous driving domain knowledge, a meta-model of spatio-temporal trajectory data is constructed. Then, we study the modeling approach of autonomous driving safety-critical scenarios based on the spatio-temporal trajectory data meta-modeling technology system, use the modeling language ADSML for automatic instantiation of safety-critical scenarios, and construct a library of safety-critical scenarios, aiming to provide a feasible approach for the modeling of such safety-critical scenarios. Combined with the scenarios of lane changing and overtaking, the effectiveness of the meta-modeling method for autonomous driving safety scenarios driven by spatio-temporal trajectory data is demonstrated, which lays a solid foundation for the construction, simulation, and analysis of the model.