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We investigate trade-offs associated with delegating authority over multiple interrelated decisions in a complex task structure. The empirical setting is a business process of a global Fortune 50 firm. The [...]

Phenomenological assumptions--assumptions about the fundamental qualities of the phenomenon being studied and how it relates to the environment in which it occurs--affect the dissemination of knowledge from subfields to the [...]

This paper introduces the concept of the design and engineering engine, which is a modular computational design system to support distributed multidisciplinary design and optimization of aircraft. In particular, this paper discusses the architecture and the functionalities of the multimodel generator module, which is a knowledge-based engineering application developed to model the geometry of both conventional and novel aircraft configurations and to automate the generation of dedicated models for low- and high-fidelity analysis tools. This paper demonstrates the capability of the knowledge-based engineering approach to record and automate complex engineering design processes, such as the generation of models for finite element analysis. The time reduction gained by process automation, together with the enabled use of high-fidelity analysis tools earlier in the design process, constitute significant achievements toward a broader exploitation of the multidisciplinary design and optimization methodology, as well as the development of novel aircraft configurations. DOI:10.2514/1.39028

This paper proposes a case-based reasoning (CBR) approach to construction hazard identification that facilitates systematic feedback of past knowledge in the form of incident cases and hazard identification. This paper focuses on two of the key components of the CBR approach: (1) a detailed knowledge representation scheme, developed based on the modified loss causation model, to codify incident cases and past hazard identification and (2) an intelligent retrieval mechanism that can automatically retrieve relevant past cases. The detailed knowledge representation scheme presented herein is designed to model both incident cases and hazard identification so that both types of knowledge repository can be retrieved simultaneously and adapted for use. The scheme also includes a linguistic structure used to facilitate indexing of cases. The retrieval mechanism is based on the concept of similarity scoring. In this paper, a novel scoring technique based on semantic networks is presented. A case study is presented to demonstrate and validate the proposed approach. DOI: 10.1061/(ASCE)CO.1943-7862.0000093 CE Database subject headings: Construction management; Risk management; Safety; Information technology (IT); Accidents; Safety; Knowledge-based systems.

Organizations in the construction industry cannot afford to make repetitive mistakes on major projects. Conversely, there are great benefits to repeating positive experiences from past projects. This need for institutional memory is amplified by the reality that in the course of normal turnover and retirement, people with years of experience leave their organizations. An effective lessons learned program is a critical element in the management of institutional knowledge; it will facilitate the continuous improvement of processes and procedures and provide a direct advantage in an even more competitive industry. This paper describes a research study that aimed to identify effective management practices and technologies for lessons learned programs in the construction industry. Data from 70 organizations that participated in this study are presented. Research found that even though more lessons learned programs are being initiated, the potential for savings and improvement has not been fully met. The study has also identified seven crucial characteristics of a successful program and verified that most organizations are stronger in some categories and weaker in others. The key to an effective program is to address all seven areas and guidance is provided for those wishing to implement lessons learned programs. DOI: 10.1061/(ASCE)CO.1943-7862.0000011 CE Database subject headings: Construction management; Information management; Knowledge-based systems.

Construction firms are now operating in a new era. Gaining and sustaining competitive advantage in this era primarily depends on effective and efficient management of knowledge assets. This paper proposes a performance evaluation model called fuzzy intellectual capital index (FICI) that can guide construction business executives to effectively and efficiently manage their knowledge assets. FICI incorporates an intellectual capital performance measurement model with fuzzy set theory to adequately handle imprecision, vagueness, and uncertainty that prevail in this process. FICI uses the fuzzy-weighted average algorithm to compute the intellectual capital performance of architectural/engineering/construction (A/E/C) firms. It is an internal reporting model that can guide executives of A/E/C firms to evaluate their firm's ability to achieve their strategic objectives and to pinpoint their firm's strengths and weaknesses in order to neutralize threats and to exploit opportunities presented by today's construction business environment. A real-world case study is presented to illustrate the implementation and utility of the proposed model. Implications for practitioners and directions for future research are discussed. DOI: 10.1061/(ASCE)CO.1943-7862.0000014 CE Database subject headings: Construction companies; Fuzzy sets; Performance characteristics; Knowledge-based systems; Information management; Construction industry.

The ability to assess the relevance of topics and related sources in information-rich environments is a key to success when scanning business environments. This paper introduces a hybrid system to support managerial information gathering. The system is made up of three components: 1) a hierarchical hyperbolic SOM for structuring the information environment and visualizing the intensity of news activity with respect to identified topics, 2) a spreading activation network for the selection of the most relevant information sources with respect to an already existing knowledge infrastructure, and 3) measures of interestingness for association rules as well as statistical testing that facilitates the monitoring of already identified topics. Embedding the system by a framework describing three modes of human information seeking behavior endorses an active organization, exploration and selection of information that matches the needs of decision-makers in all stages of the information gathering process. By applying our system in the domain of the hotel industry, we demonstrate how typical information gathering tasks are supported. Moreover, we present an empirical study investigating the effectiveness and efficiency of the visualization framework of our system. Index Terms--Alert system, environmental scanning, hyperbolic self-organizing maps, information foraging.

Diagnostic knowledge-based systems are used in a variety of application domains to support classification decisions. The effectiveness of such systems often decreases as the application environment or user preferences change over time. Hence, frequent adjustments to the system knowledge by a human expert become necessary. We study the problem of determining the optimal amount of effort that should be exerted to maintain the system over a planning horizon (finite or infinite). Using the receiver operating characteristic curve to derive a measure for system performance, we maximize system value by balancing system benefits with maintenance costs. The problem is cast as an optimal control model in which the goal is to choose the timing and extent of maintenance that must be expended to maximize system value. We find that the optimal solution usually possesses a steady-state component. The maintenance problem is also solved as a discrete, impulse control problem, as well as one where maintenance effort has a nonlinear impact on system performance. Key words: diagnostic systems; optimal maintenance; knowledge-based systems History: Accepted by Ramayya Krishnan, information systems; received May 1, 2007. This paper was with the authors 6 months for 1 revision. Published online in Articles in Advance September 17, 2008., 1. Introduction The use of knowledge-based systems (KBS) to replicate or support the knowledge of a human expert has become commonplace. Some decision benefits attributed to KBS include increased quality, [...]

Construction equipment constitutes a significant portion of investment in fixed assets by large contractors. To make the right decisions on equipment repair, rebuilding, disposal, or equipment fleet optimization to maximize the return of investment, the contractors need to predict the residual value of heavy construction equipment to an acceptable level of accuracy. Current practice of using rule-of-thumb or statistical regression methods cannot satisfactorily capture the dynamic relationship between the residual value of a piece of heavy equipment and its influencing factors, and such rules or models are difficult to integrate into a decision support system. This paper introduces a data mining based approach for estimating the residual value of heavy construction equipment using a predictive data mining model, and its potential benefits on the decision making of construction equipment management. Compared to the current practice of assessing equipment residual values, the proposed approach demonstrates advantages of ease of use, better interpretability, and adequate accuracy. DOI: 10.1061/(ASCE)0887-3801 (2008)22:3(181) CE Database subject headings: Construction equipment; Data analysis; Information management; Decision making; Knowledge-based systems.

In the South Platte River Basin of Colorado, seasonally flooded wetland ponds are being developed for groundwater recharge and wildlife habitat. The selection and evaluation of pond sites is primarily done by local experts. This study presents an approach that combines the use of knowledge-based systems, multicriterion decision analysis techniques, and geographic information systems to develop a representation of the pond evaluation process. The multicriterion decision analysis technique is used to calculate a surrogate certainty factor; that is, a suitability factor, for processing rules within a forward-chaining knowledge-based system to determine the overall suitability of a specific site. Spreadsheet software was used to implement the rule processing, with site data and spatial analysis results provided through links with a geographic information system. The spreadsheets were designed to provide flexibility for users to change thresholds, rating scales, and relative importance weights. Scenario testing and testing of the system by local experts demonstrate its potential use for pond site evaluations. CE Database subject headings: Recharge basins; Aquatic habitats; Knowledge-based systems; Geographic information systems; Ponds.

The multidisciplinary design optimization process can be supported by partial automation of analysis and optimization steps. Design-and-engineering engines are a useful concept to structure this automation. Within the design-and-engineering engines, a product is parametrically defined using knowledge-based engineering. This parametric product model needs to be initiated before global multidisciplinary optimization can be performed. The 'feasilization' is done by the initiator component in the design-and-engineering engines that simulates the heuristic methods normally used by designers to estimate the first values for the parameters and variables describing their designs. The initiation of values for structural parameters and variables is elaborated for a sample composite-stiffened panel structure. It is shown that the initiator function of the design-and-engineering-engine concept can be implemented on the basis of optimization techniques using simplification of the design requirements, simplified representations of the design options, and the class of so-called schematic models to mimic the designer's job in the preliminary sizing phase of the design. An implementation of the initiator is used in a sample design-and-engineering engine for aircraft vertical tail design. DOI: 10.2514/1.24688

With a better understanding about the product market, industry practitioners can make more informed decisions for product specification, selection, and procurement. However, current approaches that support Architectural/Engineering/Construction (A/E/C) information acquisition do not provide a comprehensive market scan. While the Internet provides an ever-growing resource for product information, existing approaches have limited capabilities to make use of this virtual market. Therefore, the writers have developed a knowledge-assisted approach to specifically address the potential of the virtual market--help retrieve A/E/C product information, particularly information about different manufacturers, from the Internet. The paper introduces the building blocks of the developed approach, which includes domain knowledge utilization, information retrieval (IR) techniques, and strategies that incorporate domain knowledge into knowledge-supported IR approaches. Specifically, domain knowledge represented in the form of a thesaurus was used with query expansion strategies under the framework of an adaptation of the extended IR Boolean model. It was concluded from the prototype validation that domain knowledge can be extremely helpful when acquiring A/E/C product information from online resources using the developed approach. DOI: 10.1061/(ASCE)0733-9364(2007)133:11(871) CE Database subject headings: Information retrieval; Information technology; Information management; Internet; Knowledge-based systems.

Abstract Subliminal information theory proposes that information is not only processed without awareness, but that it is also acted upon without awareness. Some research suggests such information is even prioritized [...]

Knowledge-based innovations emerge and get embodied in organizational structure by combination of socialized agency, differentiated expertise, defensible turf and organizational support. Practitioner networks, markets for knowledge-based service and professional firm hierarchies are the pathways that combine these elements in the management consulting firms.

Objectives: To determine the accuracy of self-reported mammography use and generate a correction factor that adjusts for reporting errors among Mexican-American women. Design: Self-reported mammography use was compared with a medical chart review. Participants: A random sample of 199 Mexican-American women aged 50-74 in southeast Texas. Main Outcome Measures: Self-reported mammography use for the preceding 2 years and mammography use determined by medical chart review. Results: Positive and negative predictive values of mammography self-reports in the previous 2 years were 73.9 and 86.0%, respectively. The correction factor was 79.6%, or ~80% of the self-reported mammograms within the preceding 2 years could be verified through medical chart reviews. Conclusions: Mammography use rates among Mexican-American women were not as high as self-reports indicated. Estimates need to be adjusted downward by approximately 20%. doi: 10.1300/J013v45n03_04 KEYWORDS. Mammography, Mexican-Americans, validation

To promote sustainable construction, the construction industry needs to intensify its efforts to move to a knowledge intensive mode. Sustainability goals can only be achieved if new resources of knowledge and expertise inform construction activities. Some of this comes in the form of good practice and standards, but much will have to come from situated and contextual appreciations of sustainability goals and local practices developed across organizational, professional, and multicultural boundaries. The paper describes a knowledge management environment based on the web-services model aimed at promoting informed sustainability practices. This validation of the knowledge environment provided the basis for formulating recommendations for promoting knowledge-based sustainable construction. DOI: 10.1061/(ASCE)0887-3801(2007)21:2(78) CE Database subject headings: Sustainable development; Construction management; Economic factors.

Professional construction management (PCM) is a burgeoning industry for public construction in Taiwan. Most problems encountered in the execution of PCM projects are treated in an empirical manner. This paper develops a new fuzzy-neural approach to establish a knowledge base for dealing with the problems encountered in the execution of PCM projects. This knowledge base confirms the causal relationships of these problems. Further, the data are integrated into a database management system to facilitate search inquiries and to make the information available to applications by other users. In its use, a fuzzy semantic description of the problems in project execution occurring in the prior phases is utilized to deduce the corresponding influence of those problems upon the following phases. The influence is represented by the specific lagging percentage of estimated pricing progress. The case study of the planning and design phases is validated by experts, and the results of this approach are reasonable and acceptable in practice. This instrument is useful because it can yield fruitful results in public construction project management. DOI: 10.1061/(ASCE)0887-3801(2007)21:2(102) CE Database subject headings: Fuzzy sets; Knowledge-based systems; Construction management; Diagnosis; Taiwan.

Recent advances in information and networking technologies havecontributed significantly to global connectivity and greatly facilitated and fostered information creation, distribution, and access. The resultant ever-increasing volume of online textual documents creates an urgent need for new text mining techniques that can intelligently and automatically extract implicit and potentially useful knowledge from these documents for decision support. This research focuses on identifying and discovering event episodes together with their temporal relationships that occur frequently (referred to as evolution patterns (EPs) in this paper) in sequences of documents. The discovery of such EPs can be applied in domains such as knowledge management and used to facilitate existing document management and retrieval techniques [e.g., event tracking (ET)]. Specifically, we propose and design an EP discovery technique for mining EPs from sequences of documents. We experimentally evaluate our proposed EP technique in the context of facilitating ET. Measured by miss and false alarm rates, the EP-supported ET (EPET) technique exhibits better tracking effectiveness than a traditional ET technique. The encouraging performance of the EPET technique demonstrates the potential usefulness of EPs in supporting ET and suggests that the proposed EP technique could effectively discover event episodes and EPs in sequences of documents. Index Terms--Document clustering, event evolution, event tracking (ET), evolution patterns (EPs), knowledge management, temporal patterns, text mining.

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. Index Terms--Knowledge-based systems (KBSs), systems, validation knowledge, validation of artificial intelligence (AI) systems.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.03.015 Byline: Atakan Azturk, Sinan Kayaligil, Nur E. Azdemirel Keywords: Production; Lead time estimation; Knowledge-based systems; Data mining; Regression trees Abstract: We explore use of data mining for lead time estimation in make-to-order manufacturing. The regression tree approach is chosen as the specific data mining method. Training and test data are generated from variations of a job shop simulation model. Starting with a large set of job and shop attributes, a reasonably small subset is selected based on their contribution to estimation performance. Data mining with the selected attributes is compared with linear regression and three other lead time estimation methods from the literature. Empirical results indicate that our data mining approach coupled with the attribute selection scheme outperforms these methods. Author Affiliation: Industrial Engineering Department, Middle East Technical University, 06531 Ankara, Turkey Article History: Received 14 April 2004; Accepted 4 March 2005

Recently, significant progress has been made in the development of physics-based, knowledge-aided (KA) signal processing strategies supported by improvements in real-time embedded computing architectures. These developments provide designers of advanced sensor systems an unprecedented degree of flexibility when implementing next generation adaptive sensor systems. In the case of radar, this has been manifested in the first ever, real-time, KA space-time adaptive processing (KA-STAP) system for advanced clutter/interference suppression. This paper provides exemplars of real-world effects giving rise to the need for 'intelligent' adaptation schemes and overviews the KA approach to sensor signal processing in some detail. Moreover, we survey a collection of papers describing recent KA sensor research that follow in this issue.

In this paper we discuss the combined use of a priori information and adaptive signal processing techniques for the design and the analysis of a knowledge-aided (KA) radar receiver for Doppler processing. To this end, resorting to the generalized likelihood function (GLF) criterion (both one-step and two-step), we design and assess data-adaptive procedures for the selection of training data. Then we introduce a KA radar detector composed of three elements: a geographic-map-based data selector, which exploits some a priori information concerning the topography of the observed scene, a data-adaptive training selector which removes dynamic outliers from the training data, and an adaptive radar detector which performs the final decision about the target presence. The performance of the KA algorithm is analyzed both on simulated as well as on real radar data collected by the McMaster University IPIX radar. The results show that the new KA system achieves a satisfactory performance level and can outperform some previously proposed adaptive detection schemes.

The paper addresses how to efficiently exploit the knowledge-base (KB), e.g. environmental maps and characteristics of the targets, in order to gain improved performance in the tracking of multiple targets via measurements provided by a ship-borne radar operating in a littoral environment. In this scenario, the nonhomogeneity of the surveillance region makes the conventional tracking systems (not using the KB) very sensitive to false alarms and/or missed detections. It is demonstrated that an effective use of the KB can be exploited at various levels of the tracking algorithms so as to significantly reduce the number of false alarms, missed detections, and false tracks and improve true target track life. The KB is exploited at two different levels. First, some key parameters of the tracking system are made dependent upon the track location, e.g., sea, land, coast, meteo zones (i.e., zones affected by meteorological phenomena) etc. Second, modifications are introduced to cope with a priori identified regions with high clutter density (e.g. littoral areas, roads, meteo zones etc.). To evaluate the behavior of the proposed knowledge-based tracking systems, extensive results are presented using both simulated and real radar data.

In the airborne space-time adaptive processing (STAP) setting, a priori information via knowledge-aided covariance estimation (KACE) is employed in order to reduce the required sample support for application to heterogeneous clutter scenarios. The enhanced FRACTA (FRACTA.E) algorithm with KACE as well as Doppler-sensitive adaptive coherence estimation (DS-ACE) is applied to the KASSPER I & II data sets where it is shown via simulation that near-clairvoyant detection performance is maintained with as little as 1/3 of the normally required number of training data samples. The KASSPER I & II data sets are simulated high-fidelity heterogeneous clutter scenarios which possess several groups of dense targets. KACE provides a priori information about the clutter covariance matrix by exploiting approximately known operating parameters about the radar platform such as pulse repetition frequency (PRF), crab angle, and platform velocity. In addition, the DS-ACE detector is presented which provides greater robustness for low sample support by mitigating false alarms from undernulled clutter near the clutter ridge while maintaining sufficient sensitivity away from the clutter ridge to enable effective target detection performance.

Many practical problems arise when implementing digital terrain data in airborne knowledge-aided (KA) space-time adaptive processing (STAP). This paper addresses these issues and presents solutions with numerical implementations. In particular, using digital land classification data and digital elevation data, techniques are developed for registering these data with radar return signals, correcting for Doppler and spatial misalignments, adjusting for antenna gain, characterizing clutter patches for secondary data selection, and ensuring independent secondary data samples. These techniques are applied to select secondary data for a single-bin post-Doppler STAP algorithm using multi-channel airborne radar measurement (MCARM) program data. Results with the KA approach are compared with those obtained using the standard sliding window method for choosing secondary data. These results illustrate the benefits of using terrain information, a priori data about the radar, and the importance of statistical independence when selecting secondary data for improving STAP performance.

Neighbor knowledge construction is the foundation for the development of cooperative query answering systems capable of searching for close match or approximate answers when exact match answers are not available. This paper presents a technique for developing neighbor hierarchies at the attribute level. The proposed technique is called the evolved Pattern-based Knowledge Induction (ePKI) technique and allows construction of neighbor hierarchies for nonunique attributes based upon confidences, popularities, and clustering correlations of inferential relationships among attribute values. The technique is applicable for both categorical and numerical (discrete and continuous) attribute values. Attribute value neighbor hierarchies generated by the ePKI technique allow a cooperative query answering system to search for approximate answers by relaxing each individual query condition separately. Consequently, users can search for approximate answers even when the exact match answers do not exist in the database (i.e., searching for existing similar parts as part of the implementation of the concepts of rapid prototyping). Several experiments were conducted to assess the performance of the ePKI in constructing attribute-level neighbor hierarchies. Results indicate that the ePKI technique produces accurate neighbor hierarchies when strong inferential relationships appear among data. Index Terms--Approximate query answering, clustering, knowledge discovery, query-answering systems, similarity measures.

This paper proposes a linear belief function (LBF) approach to evaluate portfolio performance. By drawing on the notion of LBFs, an elementary approach to knowledge representation in expert systems is proposed. It is shown how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset-pricing models. The authors then appeal to Dempster's rule of combination to integrate the knowledge for assessing the overall belief of portfolio performance and updating the belief by incorporating additional evidence. An example of three gold stocks is used to illustrate the approach. Index Terms--Dempster-Shafer belief functions, knowledge-based systems, linear belief functions, multivariate normal distribution, portfolio evaluation.

As public programs become more complex and interdependent, their effectiveness increasingly relies on interorganizational networks in which knowledge and information am the main currencies of interaction. This paper reports the results of a study of seven knowledge-networking projects in New York State government. Each project was followed for 3 years as a case study. In addition, the participants took part in two surveys that gathered expectations at the beginning of the study and perceptions of actual experience near the end. The surveys focus on the benefits, barriers, and extent of success associated with knowledge and information sharing. The results show that prior expectations changed as a result of experience. After engaging in the projects for more than 2 years, participants perceived that achieved benefits of knowledge networking were not as great as they expected and the barriers were not as formidable. In addition, the survey shows that organizational and individual networking successes are separable from project success. Perceived technological barriers appear to limit individual networking success. Perceived policy and legal barriers appear to constrain the achievement of substantive project goals. Expected and perceived organizational barriers were negatively associated with all three kinds of success. KEYWORDS: information sharing, interorganizational networks, interorganizational relations, knowledge networks, knowledge sharing

This paper reports on the use of multi-agent system technology to automate the management and analysis of SCADA and digital fault recorder (DFR) data. The multi-agent system, entitled Protection Engineering Diagnostic Agents (PEDA), integrates legacy intelligent systems that analyze SCADA and DFR data to provide data management and online diagnostic information to protection engineers. Since November 2004, PEDA agents have been intelligently interpreting and managing data online at a transmission system operator in the U.K. As the results presented in this paper demonstrate, PEDA supports protection engineers by providing access to interpreted power systems data via the corporate intranet within minutes of the data being received. In this paper, the authors discuss their experience of developing a multi-agent system that is robust enough for continual online use within the power industry. The use of existing agent development toolsets and standards is also discussed. Index Terms--Cooperative systems, decision support systems, fault diagnosis, intelligent systems, knowledge-based systems, power transmission protection.

A theory of trust for a given system consists of a set of rules that describe trust of agents in the system. In a certain logical framework, the theory is generally established based on the initial trust of agents in the security mechanisms of the system. Such a theory provides a foundation for reasoning about agent beliefs as well as security properties that the system may satisfy. However, trust changes dynamically. When agents lose their trust or gain new trust in a dynamic environment, the theory established based on the initial trust of agents in the system must be revised, otherwise it can no longer be used for any security purpose. This paper investigates the factors influencing trust of agents and discusses how to revise theories of trust in dynamic environments. A methodology for revising and managing theories of trust for multiagent systems is proposed. This methodology includes a method for modeling trust changes, a method for expressing theory changes, and a technique for obtaining a new theory based on a given trust change. The proposed approach is very general and can be applied to obtain an evolving theory of trust for agent-based systems. Index Terms--Belief logic, multiagent systems, secure systems, theory revision, trust management.

The explosion of knowledge management systems (KMS) and the need for their wide accessibility and availability has created an urgent need for reassessing the security practices anti policies in organizations. Security of these assets is a day-to-day job placing a tremendous cognitive load on information-technology (IT) professionals, which can make it almost impossible to manage the security aspects of KMS. Autonomic-computing systems are well suited to manage KMS, as they use high-level system objectives provided by administrators as the basis for managing the security of KMS. The authors model the self-protection and self-healing configuration attributes in autonomic systems through game-theoretic models. The proposed modeling approach progressively moves from a manual intervention-oriented security setup to an autonomic security setup. This allows the authors to compare and contrast the different approaches and provide insights on their applicability to different security environments. The authors find that moving to a partial autonomic system with self-healing mechanisms can provide a stable environment for securing enterprise knowledge assets and can reduce hacking. It is beneficial to implement an autonomic system when manual investigation costs are higher and/or when the volume of malicious traffic is very low. An autonomic approach is especially attractive when it is difficult to impose penalties on malicious users. Autonomic systems can be effective in securing organizational knowledge assets and in reducing the potential damage from malicious users. Index Terms--Autonomic computing, game theory, intrusion detection, secure knowledge management.

Secure knowledge management for eBusiness processes that span multiple organizations requires intraorganizational and interorganizational perspectives on security and access control issues. There is paucity in research on information assurance of distributed interorganizational eBusiness processes from a business process perspective. This paper presents a framework for secure semantic eBusiness processes integrating three streams of research, namely: 1) eBusiness processes; 2) information assurance; and 3) semantic technology. This paper presents the conceptualization and analysis of a secure semantic eBusiness process framework and architecture, and provides a holistic view of a secure interorganizational semantic eBusiness process. This paper fills a gap in the existing literature by extending role-based access control models for eBusiness processes that are done by using ontological analysis and semantic Web technologies to develop a framework for computationally feasible secure eBusiness process knowledge representations. An integrated secure eBusiness process approach is needed to provide a unifying conceptual framework to understand the issues surrounding access control over distributed information and knowledge resources. Index Terms--Description logics, information assurance and systems security, role-based access control, semantic eBusiness, semantic Web technology.

Knowledge management enhances the value of a corporation by identifying the assets and expertise as well as efficiently managing the resources. Security for knowledge management is critical as organizations have to protect their intellectual assets. Therefore, only authorized individuals must be permitted to execute various operations and functions in an organization. In this paper, secure knowledge management will be discussed, focusing on confidentiality, trust, and privacy. In particular, certain access-control techniques will be investigated, and trust management as well as privacy control for knowledge management will be explored. Index Terms--Data mining, privacy, role-based access control (RBAC), secure knowledge management, security policy, semantic web, trust negotiation (TN), usage control (UCON).

Knowledge management exploits the new opportunities of sharing knowledge among members of virtual communities in distributed computer networks, and knowledge-management systems are therefore modeled and designed as multiagent systems. In this paper, normative multiagent systems for secure knowledge management based on access-control policies are studied. It is shown how distributed access control is realized by means of local policies of access-control systems for documents of knowledge providers, and by means of global community policies regulating these local policies. Moreover, it is shown how such a virtual community of multiple knowledge providers respects the autonomy of the knowledge providers. Index Terms--Knowledge management, multiagent systems, normative systems, policies, virtual communities.

Data from knowledge workers are used to test two models of flow and to explore the relationship of flow elements with job and task types. The findings support a model of flow as the merging of awareness and application with the remaining flow elements acting as antecedents and outcomes of the flow experience.

With the advancement of the semantic web, the construction industry is at a stage where intelligent knowledge management systems can be used. Such systems support more effective collaboration, where virtual teams of skilled users, not software, exchange ideas, decisions, and best practice. To achieve that, there is a need to create consistent semantic representation of construction knowledge. Existing representations, in the form of classification systems and product data models, lack effective modeling of concept semantics--a fundamental requirement for human-based exchange of knowledge. Toward this objective, this paper presents a domain taxonomy that was developed as part of the e-COGNOS project. The taxonomy was developed as a first step in the establishment of domain ontology for construction. The taxonomy was developed to be process-centered and to allow for utilization of already existing classification systems (BS6100, Master Format, and UniClass, for example). The taxonomy uses seven major domains to classify construction concepts: Process, Product, Project, Actor, Resource, Technical Topics, and Systems. The taxonomy was developed and validated through extensive interaction with domain experts. The taxonomy was used to develop a prototype ontology for the construction domain including semantic relationships and axioms. The ontology was used to support several applications in semantic knowledge management as part of the e-COGNOS portal, including semantic indexing and retrieval of information and ontology-based collaborative project development. CE Database subject headings: Internet; Information management; Construction industry; Knowledge-based systems.

Technical knowledge is a valuable asset for construction companies. The diversity and accumulation of such knowledge on an organizational level contribute to company profitability and growth. This paper discusses a new approach for extracting, consolidating, and then retrieving technical construction knowledge that builds on the contradiction resolution concepts of the theory of inventive problem solving. The approach was used to extract knowledge from a number of lessons learned describing technical construction problems encountered by a major construction company. The approach depends on finding the similarities between technical solutions of problems that belong to different technological domains. These similarities represent the essence of these solutions and are represented using domain-independent terms so that they can be applied to new problems. The outcomes of the knowledge extraction and accumulation process are discussed in the paper to address the feasibility of the proposed approach and its potential benefits and limitations. DOI: 10.1061/(ASCE)0733-9364(2005)131:9(993) CE Database subject headings: Algorithms; Construction management; Elevators; Knowledge-based systems; Silos.

The main aim of this paper is to present a knowledge-based framework for automatically selecting the best image enhancement algorithm from several available on a per image basis in the context of X-ray images of airport luggage. The approach detailed involves a system that learns to map image features that represent its viewability to one or more chosen enhancement algorithms. Viewability measures have been developed to provide an automatic check on the quality of the enhanced image, i.e., is it really enhanced? The choice is based on ground-truth information generated by human X-ray screening experts. Such a system, for a new image, predicts the best-suited enhancement algorithm. Our research details the various characteristics of the knowledge-based system and shows extensive results on real images. Index Terms--Aviation security, image analysis, image viewability, knowledge-based framework, neural networks, X-ray.

Byline: Cevdet Gologlu (1) Keywords: Knowledge-based systems; operation sequencing; feature-based design; CAPP Abstract: Process planning is a decision-making process. Decisions on machining operations for a particular feature have to be made on various independent conditions such as which operation should be performed with which tools and under what cutting parameters. An integrated knowledge-based CAPP system called ProPlanner has been developed. The system has five modules namely information acquisition, feature recognition, machining operation planning and tool selection, set-up planning, and operation sequencing. Most process-planning systems do not produce alternative process plans. Usually, a fixed sequence created by a process plan is not necessarily the best possible sequence. Therefore, the aim should be to generate all possible operation sequences and use some optimality criteria to obtain the best sequence for the given operating environment. This paper presents an efficient heuristic algorithm, belongs to the system's operation sequencing module, for finding near-optimal operation sequences from all available process plans in a machining set-up. The costs of the various machining schemes are calculated and the machining scheme with the lowest cost is chosen. All feasible cutting tools are identified for each particular feature and the corresponding machining operations. This process is repeated for all the features in the machining set-up. All possible feature sequence combinations allowed by the current feature constraints are then generated. Appropriate cutting tools are identified and assigned to different operations. The feature sequence with the smallest number of tool changes is adopted. Author Affiliation: (1) Karabuk Technical Education Faculty, Zonguldak Karaelmas University, 78050, Karabuk, Turkey Article History: Registration Date: 30/09/2004

Byline: Ercan Oztemel (1), Hatice Kolay (2), Cemalettin Kubat (1) Keywords: Knowledge-based scheduling; complex systems Abstract: Scheduling problems are becoming more and more complex everyday. This makes the current rules and algorithms difficult to comply with the requirements. New machines with the capabilities of processing more than one jobs is being developed. Sometimes one job is divided into parts and processed by more than one machine at the same time. These make the current algorithms insufficient. Artificial intelligence technologies, especially expert systems are proven to deal with such dynamic complex problems in several domains. In this study, an example of such a complex problem is introduced and knowledge-based scheduling for these kind of problems is elaborated with a real life industrial example. Author Affiliation: (1) Muh. Fak. End. Muh. Bol, Sakarya Universitesi, Esentepe Kampus, Sakarya (2) UNSA, Ambalaj San, ve Tic. A.Sc, Fabrika Sokak, No 8, Samandira, Kartal, Istanbul Article History: Registration Date: 30/09/2004

Pattern languages (PLs) are devised for use in architecture and urban design as ways of providing useful access to design knowledge both for the expert designer and for the lay user. They have subsequently found use in the software community for technical and organizational issues.