ENERGY EFFICIENCY EVALUATION IN MANUFACTURING THROUGH AN ONTOLOGY‐REPRESENTED KNOWLEDGE BASE

Improving energy efficiency in a manufacturing company through an energy management system requires active participation of different stakeholders and involvement of different organizational entities and technical processes. Interoperability of stakeholders and entities is the key factor to achieve a successful implementation of an energy management system. Researchers have been developing approaches in applying ontologies to address interoperability issues among humans as well as machines. Ontologies have also been used for knowledge representation in different domains, such as energy management and manufacturing. In recent years, researchers have developed knowledge‐based intelligent energy management systems in buildings, especially households, which use ontologies for knowledge representation. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. This paper proposes an approach to apply ontology to allow knowledge‐based energy efficiency evaluation in manufacturing companies. The ontology provides a formal knowledge representation that addresses the interoperability issues due to different human stakeholders as well as machines involved in the energy management system of the company. This paper also describes the methods used to construct and to process the ontology. Copyright © 2013 John Wiley & Sons, Ltd.     

INCASE: SIMULATING EXPERIENCE TO ACCELERATE EXPERTISE DEVELOPMENT BY KNOWLEDGE WORKERS

INCASE, a web‐based electronic learning (e‐learning) environment, has been built to enable rapid expertise development in novice‐level professional knowledge workers. This paper describes the theoretical underpinnings (constructivist epistemology) behind INCASE and uses a design science methodology to describe its design, construction, instantiation, and validation. The INCASE system addresses a fundamental problem in knowledge worker domains where experience is a necessary component of expertise development and a barrier to the rapid development of domain experts. Copyright © 2013 John Wiley & Sons, Ltd.     

Machine‐independent audit trail analysis—a tool for continuous audit assurance

This paper reports the results of a research project which examines the feasibility of developing a machine‐independent audit trail analyser (MIATA). MIATA is a knowledge‐based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for auditors when assessing the risk of unauthorized user activity in multi‐user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert security auditor would be needed to look for two main types of events—user activity rejected by the system's security settings (failed actions) and users behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge‐based system is suited to applications that require expertise to perform well‐de?ned, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine‐independent intelligent audit trail analysis, an anomaly‐detection approach is adopted. Time series forecasting methods are used to develop and maintain the user pro?le database (knowledge base) that allows identi?cation of users with rejected behaviour as well as abnormal behaviour. The knowledge‐based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table I). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user pro?les and forecasts of behaviour on a daily basis. As such, it also ‘learns’ from changes in user behaviour. The feasibility of generating machine‐independent audit trail records, and the applicability of the anomaly‐detection approach and time series forecasting methods, are demonstrated using three case studies. These results support the proposal that developing a machine‐independent audit trail analyser is feasible. Such a system will be an invaluable aid to an auditor in detecting potential computer intrusions and monitoring user activity. Copyright © 2004 John Wiley & Sons, Ltd.     

Strategic diagnostics and management decision making: a hybrid knowledge‐based approach

A conceptual model is introduced whereby the focus is placed on environmental scanning, diagnostics and decision‐making on the basis of managerial judgement through the application of tools such as intelligent agents, hybrid intelligent systems, scenario analysis and knowledge‐based systems. The model has a critical stage as an antecedent to the strategic advice that encompasses the issues of strategic fit to purpose (theme, industry, company and strategic business unit‐driven). A number of applications dealing with these four layers are shown, as well as an illustration of hybrid intelligent systems for strategic marketing planning. Copyright © 2007 John Wiley & Sons, Ltd.     

An adaptive hierarchical fuzzy logic system for modelling of financial systems

In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates in Australia is developed. The proposed system uses a hierarchical fuzzy logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases that are used for prediction of interest rates in Australia. A hierarchical fuzzy logic system is developed to model and predict three‐month (quarterly) interest rate ?uctuations. The system is further trained to model and predict interest rates for six‐month and one‐year periods. The proposed system is developed with ?rst two, three, then four and ?nally ?ve hierarchical knowledge bases to model and predict interest rates. A novel architecture called a feed forward fuzzy logic system using fuzzy logic and genetic algorithms is also developed to predict interest rates. A back‐propagation hierarchical neural network system is also developed to predict interest rates for three‐month, six‐month and one‐year periods. The results obtained from these two systems are then compared with the hierarchical fuzzy logic system results and conclusions are drown on the accuracy of all systems for prediction of interest rates in Australia. Copyright © 2004 John Wiley & Sons, Ltd.     

社会终身学习网络知识平台建设的关键技术应用探析

社会终身学习网络知识平台的建设要以人为主体,要有利于学习者在学习过程中的知识交互;要体现学习者之间的学习行为的社会化联结,要有利于平台学习者在进行正式学习和非正式学习过程中的隐性知识经验共享;要以建构主义思想为基础建立具有良好导航功能的智能导师系统,要有利于知识碎片的汇聚.本文从以上功能视角出发分别对构建理念和具体技术实现做了阐述.     

Design principles for long‐lived Internet agents

Prior research on intelligent Internet agents has failed to address the needs of long‐lived data‐collecting agents, focusing instead on short‐lived transaction agents. Transaction agents typically run for a few seconds and retrieve information for a single transaction. With the staggering growth of electronic commerce, researchers and practitioners will want to design long‐lived data‐collecting agents that intelligently search for, retrieve, interpret, categorize, and store vast amounts of related information each time that they run. Such agents can run over the course of days rather than seconds and can be used by practitioners for decision support applications or by researchers as part of an empirical research methodology. This paper proposes a framework for agent sophistication, and emphasizes a number of design concepts for long‐lived Internet agents, including intelligence, validation, concurrency, recovery, monitoring, and interactivity. These concepts are used in the development of an illustrative tool called Electronic Data Retrieval LexicaL Agent (eDRILL), an object‐oriented data‐collecting agent. eDRILL is designed using the Unified Modeling Language (UML) and is written in Java. It gathers research data from an online auction. © 2000 John Wiley & Sons, Ltd.     

Law and Executive Compensation: A Cross‐Country Study

Companies outside the U.S. use substantially less equity in their compensation mix than U.S. firms. But despite this consistent “cross‐sectional” difference, the pattern of changes in equity‐based pay of non‐U.S. companies over time appears to mirror changes in the pay of U.S. companies. The authors provide persuasive evidence that features of a nation's legal environment help explain major differences in compensation structure across countries. As a general rule, companies in countries that provide greater protection of shareholder rights use larger amounts of equity‐based compensation. And the equity mix also tends to be higher when a country's legal system ensures strict enforcement of the laws that are on the books. At the same time, since the equity mix varies considerably over time within the same legal environment, it is clear that factors other than the legal environment affect compensation structure. The authors report that, after controlling for legal factors, company‐specific variables that proxy for “agency” conflicts—not only between managers and shareholders, but between controlling and minority shareholders as well—also affect the compensation mix in fairly predictable ways. The bottom line of this study is that, although we may have a global market for talent, compensation structures across countries are not likely to converge unless and until the underlying legal protections afforded shareholders converge. At the same time, the effect of agency costs in compensation design for non‐U.S. firms appears to be partly conditioned by the nation's legal system and the entire set of regulatory and other institutions that are affected by it.     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

基于保险业务流程的知识管理问题——以团体健康保险新契约核保业务流程为例

保险行业是一个流程导向性的行业,也是一个高度知识密集型的行业。保险公司员工需要在某个专业领域多年的知识积累,包括流程知识、业务领域的技术、复杂问题的应急能力等经验知识,这些都是为了高效地完成工作。而这些类型的知识普遍存在于人们的大脑中,并且具有不稳定易变的特征,所以迫切地需要挖掘这类知识,并进行知识共享。保险公司的运行...     

THE USE OF SOCIAL MEDIA IN THE SUPPLY CHAIN: SURVEY AND EXTENSIONS

This paper investigates the capabilities of social media, such as Facebook, Twitter, Delicious, Digg and others, for their current and potential impact on the supply chain. In particular, this paper examines the use of social media to capture the impact on supply‐chain events and develop a context for those events. This paper also analyses the use of social media in the supply chain to build relationships among supply‐chain participants. Further, this paper investigates the of use of user‐supplied tags as a basis of evaluating and extending an ontology for supply chains. In addition, using knowledge discovery from social media, a number of concepts related to the supply chain are examined, including supply‐chain reputation and influence within the supply chain. Prediction markets are analysed for their potential use in supply chains. Finally, this paper investigates the integration of traditional knowledge management along with knowledge generated from social media. Copyright © 2011 John Wiley & Sons, Ltd.     

The impact of manager philosophy on knowledge management systems

This paper analyses how information about managers and technology can be used to provide those managers with a system that is congruent with their needs. In particular, using McGregor's Theories X and Y philosophies, managerial needs are elicited and then contemporary knowledge management technologies, including intelligent agents, and the way they are implemented, are analysed to determine how they meet those manager needs. Different knowledge management technologies are found to be important to manifesting the requirements of particular management philosophies. For example, ‘Theory X’ appears consistent with use of intelligent agents to ‘monitor’ behaviour. This leads to the concept of ‘technology congruence’, where the choice of the technology ultimately is tied to which view of the world the manager employs. Copyright © 2010 John Wiley & Sons, Ltd.     

Self‐Fulfilling Prophecies of Failure: The Endogenous Balance Sheets of Distressed Companies

This article analyses a problem at the intersection of accounting, law, and economics: the economically efficient operation of legal arrangements for company failure is undermined because valuations of assets and liabilities become unstable once a firm is distressed. The paper draws on the three disciplines to show the pivotal role of asset and liability valuations in answering the legal question, whether the firm is insolvent, and the economic question, whether the firm should fail and its assets be redeployed to an alternative use. U.S. and U.K. evidence reveals a disconcerting indeterminacy in these processes: the probability that a firm will fail affects significantly the valuations assigned to assets and liabilities; but at the same time the valuation of assets and liabilities itself determines the probability of failure. This balance sheet endogeneity is then shown to delay economically efficient management changes under debtor‐oriented U.S. Chapter 11, and to induce unnecessary costly bankruptcy with creditor‐oriented U.K. receivership/administration. Recent cases trace this endogeneity in failures involving often controversial countermanding of huge financial claims.     

Decision analytics mobilized with digital coaching

The context to be addressed is the digitalization of industry and industrial processes. Digitalization brings enhanced customer relationships and value‐chain integration, which are effective instruments to meet increasing competition and slimmer margins for productivity and profitability. Digitalization also brings more pronounced requirements for effective planning, problem solving and decision making in an increasingly complex and fast‐changing environment. Decision analytics will meet the challenges from the growing global competition that major industrial corporations face and will help solve the problems of big data/fast data that digitalization is generating as a by‐product. A mantra is appearing in business magazines – that powerful, intelligent systems will be effective tools for the digitalization of industrial processes – but much less attention appears to be paid to the fact that users need advanced knowledge and skills to benefit from the intelligent systems. First, an effective transfer of knowledge from developers, experts and researchers to users (including management) will be needed; second, the daily use and operations of the systems need to be supported, as automated, intelligent industrial systems are complex to operate. We look at this transfer as knowledge mobilization and will work out how the mobilization can be supported with coaching; this coaching needs to be digital, as human coaches are both scarce and too expensive to employ in large numbers.     

Driving growth: Economic Value Added versus Intellectual Capital

This paper compares and contrasts Economic Value Added (EVA^TM) and Intellectual Capital (IC) as two technologies of managing oriented towards encouraging growth. The analysis suggests that EVA^TMand IC contrasts greatly. EVA^TMis a financial management system based on radical delegation and ‘empowerment’ and which therefore directs attention to the results created by managers. Based on financial micro-theory, EVA^TMis a performance measure that attempts to account more properly for the cost of capital, but more than that, it is also a management control system which seeks to create radically independent business units and minimize corporate staff. IC is a different control system concerned to encourage endogenous growth implemented via loosely coupled sets of non-financial measurements that become strong via stories and metaphors about the post-modern firm in the post-modern world. Here, based on theories of organizational knowledge and competence development, emphasis is put on mobilizing white collar productivity and creativity based on some form of evolutionary economics or resource-based theory. While EVA^TMlooks to managers as the movers of change, IC seems more systematically to promote the creativity possessed by employees.     

A knowledge‐based framework for incorporating investor's preference into portfolio decision‐making

This paper describes a framework for the integration of a rule‐based system capable of identifying an investor's risk preference into a quantitative portfolio model based on risk and expected return. By inferring rules consisting of an investor's objective and subjective risk preferences, the integrated methodology provides the assets suitable for the preferences. Through investment in the portfolio composed of the assets, the investor is able to obtain the following bene?ts: reduction of costs and time spent to determine target assets, and alleviation of anxiety from ‘out‐of‐favor’ assets. The framework is applied to the development of a knowledge‐based portfolio system for constructing an investor's preference‐oriented portfolio. In the procedure of the system for ?nding an optimal portfolio, the system uses an arti?cial intelligence method of a case‐based reasoning to obtain preference thresholds for an investor when the investor's past investment records are available. Experimental results show that the framework contributes signi?cantly to the construction of a better portfolio from the perspective of an investor's bene?t/cost ratio than that produced by the existing portfolio models. Copyright © 2004 John Wiley & Sons, Ltd.     

The effect of investor sophistication on the influence of nonfinancial performance indicators on investors’ judgments

This paper presents an experiment that examines how enhanced disclosure of nonfinancial performance indicators affects the stock‐price estimates of nonprofessional and professional investors. Participants were provided with a case study containing excerpts from a hypothetical company’s annual report. The experiment was a 2 (nonprofessional and professional) × 3 (positive nonfinancial performance indicators, negative nonfinancial performance indicators, and financial information only) between‐subjects design. Consistent with conservatism, the nonprofessional investors underreacted in their stock‐price estimates to the positive nonfinancial disclosures, compared with professional investors with task‐specific knowledge. The results from this study suggest that the value of enhanced disclosure of this type may not flow equally to all users of financial reports, if conservatism, and lack of task‐specific knowledge, adversely affect their decision‐making.     

Ultimate Ownership, Income Management, and Legal and Extra-Legal Institutions

This study provides evidence of the role of both legal and extra‐legal institutions in limiting the income management induced by the detachment of control rights from the cash flow rights of ultimate owners. The tests use a unique, comprehensive data set for firm‐level control and ownership structures from 9 East Asian and 13 Western European countries. Univariate regressions show that income management that is induced by the wedge between control rights and cash flow rights is significantly limited in countries with high statutory protection of minority rights (proxied by legal tradition, minority rights protection, the efficiency of the judicial system, or disclosure standards) and effective extra‐legal institutions (proxied by the effectiveness of competition laws, diffusion of the press, and tax compliance). Furthermore, multiple regression results show that a common law tradition and an efficient judicial system subsume the effects of the other legal institutions, and that a high rate of tax compliance subsumes the effects of the other extra‐legal institutions in curbing insider income management. It is surprising that a high rate of tax compliance ultimately has a greater effect than legal tradition and the efficiency of the judicial system. Although this finding is unexpected, given prior evidence on the dominant roles of legal institutions in macroeconomic issues and corporate policies, it is consistent with the recent argument that effective tax enforcement is like a public good in that it can reduce insiders' private control benefits. An implication of this finding is that closer attention to extra‐legal institutions has the potential to enhance our understanding of the institutional reforms needed to limit insider private control benefits.     

Can knowledge based systems be designed to counteract deskilling effects?

Recent calls in the information systems research community argue that we know intelligent systems deskill users, and future research should focus on how to design systems that do not deskill, rather than continue to examine whether the phenomenon occurs. This should be a wakeup call for public accounting firms focused on implementing restrictive audit support systems, which leads to de-skilling of novice accounting professionals. Our research focuses on redesigning knowledge-based systems to facilitate expertise development and counteract the de-skilling effects that result from use of such systems. Specifically, we manipulate the design of the system interface by providing information cues in a screen format consistent with expert knowledge representations and manipulate automatic provision versus voluntary use of explanations for users during task completion. Results show that after using the knowledge-based system to complete a series of reenacted client engagements over a three-day period, both the interface design manipulation and automatic provision of explanations had a positive effect on novice accounting professionals’ development of expert-like knowledge structures. The results of the study have important implications for the development of knowledge-based systems intended to support accounting professionals’ (and other knowledge workers’) expertise development processes.