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In this paper we describe a novel intelligent web based system which employs human feedback and evaluation to assist an Internet user to interactively identify the clothing of a missing person. It is novel in terms of employing GA to interactively create different shirt designs. It also illustrates human-centeredness from two perspectives. Firstly, from a multimedia information system perspective, in order to capture the user semantics it employs user feedback. Secondly, from a computational intelligence perspective the user is involved in defining objective function of a GA agent for optimizing search. The system can be used by the law enforcement authorities to identify the type, color and design of the shirt worn by a missing person. It has been tested on a small number of users with encouraging results.

Emotions form an important component of human behavior and decision making. In this paper we employ image processing and soft computing techniques to design and implement emotional state profiling of a sales candidate while they provide psychological inputs related to their selling behavior to an intelligent sales recruitment and benchmarking system. This research is part of a larger effort aimed at giving computers and other pervasive devices the skills of emotional intelligence, such as the ability to recognize a person's emotions and to respond appropriately to those emotions. The work reported has implications in not only in recruitment and benchmarking but also web recommender and information personalization systems.

This paper proposes a human-centered multi-layered multi-agent architecture for performance optimization of data mining applications. Feedback from user is used for optimizing the data mining performance. This paper describes application of the architecture in banking and finance domain

In this paper we outline a task-centered agent-oriented approach to modelling (or software engineering) of intelligent real-time systems for critical events like environmental disasters (e.g., bush fires, earthquakes), alarm processing in critical and emergency situations in power systems, and in war situations involving detection and destruction of enemy planes by fighter pilots. We model the user (e.g., operator or fighter pilot) at two distinct levels. These include the problem solving or the task level and performance optimisation level. Alarm processing is used as an application to illustrate the modelling at the task and performance optimisation levels respectively

Soft computing agents today are being applied in a range of areas including image processing, engineering, process control, data mining, internet and others. In the process of applying soft computing agents to complex real world problems three phenomena have emerged. Firstly, the application of soft computing agents in distributed environments has resulted in merger of techniques from soft computing area with those in distributed artificial intelligence. Secondly, in order to facilitate better understanding and frequent use of the soft computing agent technology, humanization of these agents has become an important design issue (Takagi, 2001). Thirdly, given the approximate and imprecise nature of the solutions provided by soft computing agents, optimization has become an important design issue in an effort to improve the quality of solution provided by soft computing agents. Finally, from a user’s perspective, as the soft computing agent technologies have moved out of laboratories into the internet and the real world the need to model and express these technologies in the problem solving context of user. In this chapter we model these four phenomenons as part of multi-layered multi-agent architecture. The layered architec­ture is also motivated by the human-centered approach and criteria outlined in the 1997 NSF workshop on human-centered systems.

In-season N management of irrigated corn requires frequent acquisition of plant N estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech and QuickBird satellite multispectral data using the normalized GNDVI to produce N status maps of a study site on three days during the corn vegetative growth period. Scale factors to represent N sufficient and N deficient corn were determined for both systems from relationships between the normalized GNDVI and the NSI. A third classification was required for this study to classify areas that exhibited leaf chlorosis that was not caused by N deficiency, i.e., not N related. N status maps generated from normalized GNDVI values showed similar patterns between the two systems for the three corn growth stages (V10, V12, and V15) investigated. However, the extent of the pattern varied between systems. On 2 July (V10), six of six sample sites were correctly classified using leaf N content to indicate plant N status. On the other two days (7 July and 15 July), four of six sites were correctly classified. Two not N related areas were classified as N deficient when leaf N content was adequate.

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. Recently, unstained methods have been used for classification and analysis of cells in human and mammalian tissues. The classifications accuracies have been have been quite poor. We describe a novel application of genetic algorithms for significantly improving the segmentation and classification of cells in unstained Chinese hamster ovarian image samples. The multiagent soft computing model represents a symbiotic relationship between soft computing agents like genetic algorithms, neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.

E-recruitment area has emerged as an important e-business function in the past few years. We describe a novel e-recruitment multiagent application for recruitment and benchmarking of salespersons. Most existing approaches to recruitment rely on the interview process or on psychometric techniques. Both these approaches have had limited success. We describe a multiagent e-sales recruitment system (e-SRS), which integrates a selling behavioral model with expert systems and soft computing techniques like fuzzy-K-means for predicting the selling behavior profile of a sales candidate.

Nowadays, recommendation systems in e-commerce are booming because of their potential and applicability for personalized services to customers. However, recommendation is not always what customers are expecting. Odd pitches and poor matches in the system have led to outpouring of anecdotes. It means that quality control doesn't apply to the recommendation here. To cope with this problem, this paper proposes a new way to improve both the quality of rating and recommendation itself for e-recommendation system. The concepts will integrate to the implementation of our on-going e-recommendation system and the second concept is illustrated in a financial domain.

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. In this paper we describe a multi-agent distributed control system model for image processing of unstained human (mammalian) cell images. The control system model develops a symbiotic relationship between soft computing agents like neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.

It is often known that an EEG has a personal characteristic. However, there are no researches to achieve the consideration of the personal characteristic. Then, the analyzed frequency components of the EEG have that the frequency components in which characteristics are contained significantly, and that not. Moreover, these combinations have the human equation. We think that these combinations are the personal characteristics frequency components of the EEG. In this paper, the EEG analysis method by using the GA, the FA and the NN is proposed. The GA is used for selecting the personal characteristics frequency components. The FA is used for extracting the characteristic data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, EEG pattern was classified using computer simulations. The EEG pattern has 4 conditions, which are listening to rock music, Schmaltzy Japanese ballad music, healing music and classical music. The result, in the case of not using the personal characteristics frequency components, gave over 95% accuracy. This result of our experiment shows the effectiveness of the proposed method.

Recently in the world, many researches of personal identification method using biometrics are widely done. Especially, personal identification using the face is used because of needless for physical contact. In this paper, we propose the new method to identify the personal using the SPCA and the NN. The SPCA has been proposed in order for the PCS to achieve high-speed processing. Moreover, the personal identification system needs to have not only ability to recognize registrant correctly but also ability to reject un-registrant certainly. Therefore, we perform simulation using un-registrant. Furthermore, we analyzed and examined about the individual feature in a face by using the GA and the SPCA. Then, in order to show the effectiveness of the proposed method, we show computer simulations by suing the real image. From these results, we show the effectiveness.

Intelligent agents (or software agents) today can be seen as providing support to practitioners and problem solvers at three levels, namely, clerical, tool and task levels. In particular, we outline computational level based agent definitions of fuzzy neural agents. We show the application of these agents in determining faulty components in a electrical power system network.

A generic neuro-expert system architecture which can overcome difficulties faced by stand-alone expert systems and artificial neural networks is proposed. It can be applied in various problem domains, such as engineering and fault diagnosis, which require problem decomposition. It is recommended for use in real-time systems. The neuro-expert system architecture can be used at different levels of a power system hierarchy for alarm interpretation and fault diagnosis. >

Electronic Commerce (EC) can be broadly seen as the application of information technology and telecommunications to create virtual trading networks where goods and services are sold and purchased.

The applications in this book employ a number of concepts and technologies associated with e-business. The e-business concepts include types of e-business systems, e-business strategies and e-business models. The technologies can be grouped under Internet and web technologies, intelligent technologies, web software engineering (Object-oriented and agents) technologies, and multimedia. E-business strategies and e-business models also provide the context in which e-business technologies are applied by organizations to gain competitive advantage. This chapter introduces the reader to these concepts and technologies.

In the past decade human-centeredness has become an important enabling concept in information system development. The fast growth of the Internet and WWW and partial failure of the dot corns has further accelerated development in this area. The need for human-centeredness has been felt in practically all areas of information systems and computer science. These include e-business, intelligent systems (traditional and web-based), software engineering, multimedia data modeling, data mining, enterprise modeling and human-computer interaction. In this chapter we discuss the pragmatic issues leading to human-centeredness in these areas and the enabling theories which are converging towards human-centered system development. These enabling theories include theories in philosophy, cognitive science, psychology and work-oriented design for human-centered e-business system development framework. We conclude the chapter with a discussion section that outlines the foundations of the human-centered system development framework described in the next chapter.

The Internet has become a major driving force behind the development of computer based human resource management systems. This chapter describes e-business analysis, design and implementation of e-Sales Recruitment System (e-SRS) for a recruitment company. It illustrates the application of activity-centered e-business analysis component, problem solving ontology component and transformation agent component of the human-centered e-business system development framework. We begin this chapter with a brief description of human resource management e-business systems. It is followed by a brief discussion of motivation for using information technology in the area of sales recruitment, then followed by a detailed e-business analysis of the sales recruitment activity using the activity-centered e-business analysis component of the human-centered e-business system development framework. Finally, the e-business design of the e-SRS is described based on two alternative approaches. The first approach involves integration of a psychology based selling behavior model of artificial intelligence techniques like rule based expert systems. The selling behavior profiling and benchmarking results are outlined based on the artificial intelligence approach. The alternative approach is an adaptive approach, which involves integration of the selling behavior model with soft computing methods like fuzzy k-means clustering. In this incremental learning approach, the behavioral patterns are mined into meaningful selling behavior category clusters.

In the last chapter we outlined relevance feedback as one of the methods for developing user-centered multimedia applications. On the other hand, researchers in the computational intelligence or soft computing community have been recently trying to develop intelligent applications which humanize computational intelligence technologies (Takagi 2001, 2002). In this chapter we describe an intelligent web multimedia system which employs relevance feedback as a means of assisting an Internet user (relative or friend of a missing person) to interactively identify the clothing of a missing person. The system can be used by the law enforcement authorities, like the police to identify the type, color and design of the shirt worn by a missing person.

It is widely acknowledged that the main barrier to e-business lies in the need for applications to meaningfully share information. The negative impact on e-business of the inherent limitations of traditional approaches to knowledge sharing has been comparable to the Internet’s initial lack of reliability or security. In the past, knowledge sharing and organization efforts nearly always produced document-based Knowledge Management Systems (KMS), i.e. collections of documents internally maintained by organizations and focused on particular domains.

Businesses today are using the Internet as a genuine resource for gaining competitive advantage. On-line customization is one useful customer relationship management strategy adopted by e-businesses to add customer value and improve sales of their product and services using the Internet. People are inclined to believe those who have similar interests and living habits. In other words, determining the buying habit of customers on the Internet can benefit both customers and the e-business. From a customer’s point of view, identifying customers with similar e-banking product buying habits may help that customer make their decision about a new product. On the other hand, knowing the buying habit of customers can help e-business practitioners to better package their products in an e-banking (or Internet banking) environment and design personalized services oriented towards each individual customer.

This chapter builds on the foundations laid down in the previous chapter. It describes the human-centered e-business system development framework for multi-agent e-business systems based on human-centered criteria outlined in the first chapter and the pragmatic considerations and enabling theories discussed in chapter 3, which contribute towards realization of those criteria. The human-centered framework is described in terms of four components, namely, activity-centered e-business analysis, problem solving ontology, transformation agent, and multimedia interpretation, respectively. The three human-centered criteria are used as guidelines for development of the human-centered framework. The pragmatic considerations and contributing theories are used to develop the structure and content, or knowledge base, of the four components. The structure and content are described at the conceptual and computational (transformation agents) level. We start this chapter by describing the external and internal planes of human interaction which underpin the development of the human-centered framework. We follow it with the description of two components of the human-centered e-business system development framework, namely, activity-centered e-business analysis and problem solving ontology. In the next chapter we continue with the description of the problem solving ontology component and describe two other components, namely, the transformation agent and multimedia interpretation component. These four components have been used to define the external and internal planes of human interaction with the environment.

In the last few years the Internet has had an enormous impact on businesses and consumers. Figure1.1 shows a comparison of adoption time of Internet as compared with other technologies like personal computer, radio and television. It has taken only four years for the number of Internet users to grow to 50 million compared to sixteen years for personal computer users and thirty-eight years for the radio. The brick-and-mortar companies have had to adapt not only with the pace of the technological change but also the disruptive affect of the Internet enabled e-commerce and e-business technologies. E-commerce and e-business have changed the way people live their lives and the way businesses operate. Many brick-and-mortar companies are still coming to terms with the pace of technological change and recognizing the true competitive advantage of e-business. However, given the technology-enabled nature of e-business, the e-business applications run a similar or higher risk than traditional business applications of being driven by technology-centeredness rather than human-centeredness or customer-centeredness. The stakes are higher than in traditional business applications because organizations embarking on e-commerce and e-business have been forced to look at ways to model customer or user’s expectations from their businesses more explicitly as compared to the conventional business models in traditional commerce.

The objective of this chapter is to outline the computational framework of intelligent multimedia multi-agent systems based on the human-centered approach. The title human-centered virtual machine encapsulates the integration of conceptual components of the human-centered system development framework and the technology based artifacts used to realize the conceptual components at the computational level.

In the last four chapters we have described applications of HCVM in e-sales recruitment, e-banking, e-business data organization and knowledge management. In chapter 5 we also described the multimedia component of the HCVM. In all these chapters multimedia has been looked at in terms of how it can be used for improving the representational efficiency, effectiveness and interpretation of computer-based artifacts and also to some extent how it can be used for perceptual problem solving. In fact, multimedia data (e.g., text, image, video and audio) today is an inherent part of Internet and web-based applications. In that respect, there are interesting research issues and problems associated with management and retrieval of multimedia data from multimedia databases. Queries and operations based on classical approaches (e.g., relational database structures) just won’t do for multimedia data, where browsing is an important paradigm. The importance of this paradigm is illustrated by the fact that multimedia databases are sometimes referred to as hypermedia databases. Standard indexing approaches won’t work for annotation independent, content-based queries over multimedia data. The problem is further compounded by the fact that metadata of different media artifacts cannot be effectively used for modeling user queries involving text, image, video and audio data. Incorporating user semantics is an effective way of dealing with multimedia data indexing and retrieval.

An integrated model for real time alarm processing in a real world terminal power station is applied. The integrated model is a combination of a generic neuro-expert system model, object model, and UNIX operating system process (UOSP) model. It is shown how the massive parallelism and fast execution features of ANNs help to cope with real-time system constraints like data variability and fast response time. For further enhancing reliability, a practical use of competing expert system-artificial neural networks (ES-ANN) objects is proposed. >

We outline the features of a cognitively compatible symbolic-connectionist architecture for complex data intensive time-critical domains especially in the diagnostic area. We examine how a symbolic-connectionist architecture copes with the time-critical and size complexity aspects of a real time alarm processing system developed for a regional power system control centre. >

In this paper we describe the problem-solving constructs, namely information processing constructs, learning constructs, knowledge representation constructs and computational constructs for building complex symbolic-connectionist systems. These constructs are applicable in particular to complex diagnostic domains and in general to complex data intensive domains. >

This paper outlines a human-centered virtual machine of problem solving agents, intelligent agents, software agents and objects. It deals with issues related to high-assurance (e.g. reliability, availability, real-time and others) through design of human-centered system architecture in which technology is a primitive. The human-centered virtual machine is based on a number of human-centered perspectives including the distributed cognition approach. The human-centered virtual machine has been applied in complex data intensive time critical problems like real-time alarm processing and fault diagnosis, air combat simulation and business (decision support).

We explain the different approaches to fusing neural networks and symbolic processing aspects. Four categories are distinguished namely fusion, transformation, combination and associative systems. A brief description of each is given. We also briefly describe the GEMSES architecture as an example of an associative system which has been built from philosophical, cognitive, practical viewpoints.

Intelligent Multi-Agent Hybrid Distributed Architecture (IMAHDA) for complex systems has been defined at the task structure level and the computational level. IHMADA has been built around five information processing phases. The five phases represent a deliberate as well as automated or reflexive reasoning structure. This paper outlines some aspects of the computational level of IMAHDA. The computational level is derived from four layers, namely, object layer, software agent layer, intelligent agent layer, and problem solving agent layer. Agent description in terms of percepts, goals, actions, and environment for some of the problem solving agents is also outlined. IMAHDA has been applied in engineering (real time systems) and management areas.

In this paper we outline an intelligent hybrid multi-agent architecture for engineering complex systems. The hybrid multi-agent architecture is described at the task structure level. The architecture has been successfully applied in a real time alarm processing and fault diagnosis system in a power system control centre with good performance results.

In this paper, we show the relevance of fuzzy systems in an integrated symbolic and subsymbolic architecture for information processing. We also show how fuzzy logic can be used to represent knowledge in the integrated architecture. We end up with an integrated architecture of knowledge based, connectionist and fuzzy systems for information processing and knowledge modelling. The integrated information processing architecture assists the problem solver in determining the relevance of the three computational paradigms, and their strategy for integrating them in problem solving.

Knowledge based systems, fuzzy systems, and artificial neural networks (ANN) are the three most widely used computational paradigms for emulating different aspects of human cognition like information processing, knowledge representation, and learning. A proper integration of these three paradigms can help to realize powerful problem solving strategies especially for large data intensive domains. In this direction we have developed a generic architecture for integration of symbolic (knowledge based and fuzzy) and connectionist (ANN) systems for large, data intensive domains at the task structure level, computational (symbol) level, and the program level. In this paper we outline the knowledge modeling aspects of the integrated symbolic (knowledge based and fuzzy)-connectionist architecture. The knowledge content of the architecture can facilitate a problem solver in modeling the knowledge required for using as well as integrating the three intelligent paradigms.

Achievement of organizational goals primarily calls for an effective utilization of human resources besides other resources. Pertinently, organizational goals can be achieved by taking into consideration the nature, abilities, and limitations of the manpower, enabling them to actualize their full potential, utilizing them fully for the growth of the organization, maintaining their quality, and integrating them with other resources. The performance appraisal system is a system of eliciting feedback, benchmarking it, communicating the same to the employees to bring holistic development in them besides evaluating the potential of employees. The present study aimed to analyze the performance appraisal system being practiced in industries of Punjab besides identifying the factors affecting the same.

IC design is upstream and innovation-oriented in semiconductor industry. This industry is emerging rapidly due to severe competition and shorter product life cycle. In this context, maintaining the leading edge among global competitors as well as overcoming operational challenges is critical to pursue sustainability. In order to enhance the achievements and improve service quality, IC design firms often endeavor to comprehend, if the inputs/investments have led to effective outputs/achievements. A holistic discussion on how successful IC design companies prevail can facilitate how different IC design firms utilize their particular attributes associated with organizational goals. The purpose of this research is to conduct a cluster analysis for global top 30 IC design firms through 15 selected variables to understand their characteristics. These 15 variables include inputs/investments and outputs/achievements collected from financial indicators primarily to examine their characteristics in each cluster. The factor analysis, cluster analysis, and discriminant analysis are adopted to identify the attributes of these companies. The findings first show that the preliminary 15 variables can be refined into four significant factors by their factor loadings using factor analysis. Second, five groups among 30 companies have been categorized in terms of cluster analysis. Third, the accuracy is 94.7% by the validation of discrimination analysis. The strengths and weaknesses of each single cluster are also discussed. The results can help IC design companies to pursue a more suitable business model and tackle unique improvements, respectively.

Intelligent agents are being increasingly used on the Internet to provide various kinds of support to the Internet users. Task level support is aimed at modeling the user's tasks and problem solving models. In this work, we focus on design and application of intelligent agents for providing task level support in Internet-based applications via an Electronic Broker. Our Broker is used to locate the desired information or product for its user and acts as a mediator between the information providers or on-line suppliers and user/customer. The operation of the intelligent electronic broker is demonstrated through a sample application, namely, the purchase of hardware adapters on the Internet.

The purpose of this chapter is to describe the application of the problem solving ontology component of HCVM in the area of face detection and annotation. computer-based aspects of an embedded feature-based multi-agent face detection and annotation system are described in this chapter. Unlike similar feature-based systems that start from searching for facial organs in the images and group them to find faces, the system solves the problem using the problem solving agents of the HCVM in a top-down fashion. The embedded mtilti-agent face detection system makes hypotheses of face locations and seeks evidences to verify them. The five problem solving agents follow a coarse grain to fine grain methodology and work on various features of color images. The coarse to fine top-down methodology is more akin to human perceptions than a bottom-up approach. The higher level problem solving agents like decomposition work as background knowledge for the control and decision agents resulting in improvement of the accuracy and speed of detection. It is also distinct from many other top-down systems in that no re-scaling of the images is needed in the searching process. To improve the detection rate, a unique iterative region-partitioning algorithm is developed in this multi-agent system. The problem solving agents model aspects related to skin-tone region segmentation, noise reduction, candidate face regions location and facial feature extraction/face detection, and annotation. The detection algorithm is invariant to scale and rotation to some degree. The performance of the multi-agent system has been tested with a large number of images and the results obtained thus far are very encouraging.

This chapter builds on the foundations laid down in the previous chapter. It describes the human-centered system development framework for intelligent multimedia multiagent systems based on human-centered criteria outlined in the first chapter and the pragmatic considerations and theories discussed in chapter 3, which contribute towards realization of those criteria. The human-centered framework is described in terms of four components, namely, activity-centered analysis, problem solving ontology, transformation agents, and multimedia interpretation, respectively. The three human-centered criteria are used as guidelines for development of the human-centered framework. The pragmatic considerations and contributing theories are used to develop the structure and content, or knowledge base, of the four components. The structure and content are described at the conceptual and computational (transformation agents) level. We start this chapter by describing the external and internal planes of action which underpin the development of the human-centered framework. We follow it with the description of two components human-centered system development framework, namely, activity-centered analysis and problem solving ontology. In the next chapter we continue with the description of the problem solving ontology component and describe the transformation agent and multimedia interpretation component. These four components have been used to define the external and internal planes of human interaction with the environment.

In the last two chapters we have looked into the application of four components of the HCVM. In this chapter we wish to demonstrate two other facets of the human-centered virtual machine, which have not been specifically looked at until now. These are the role of computer-based intermediaries (for back up support in problem solving), distributed communication in human-centered multi-agent systems and using computer-based artifacts to model breakdowns in human decision making. The role of intermediaries and distributed communication is illustrated by way of an intelligent distributed multi-agent multimedia euchre application on the net. In addition, aspects of a sales recruitment and benchmarking application are described to illustrate how computer-based artifact is used to overcome breakdowns in the sales, customer service and telesales recruitment decision making. In fact the modeling of the breakdowns is the prime motivator for the sales managers and sales personnel to use the computerized system for management of recruitment and benchmarking. The sales recruitment system described in this chapter (and developed by the first author) has been used in the industry for approximately the past three years.

In the last three chapters we have described applications of HCVM in medical informatics, image processing, internet games and sales recruitment. In these applications we have looked at multimedia artifacts in terms of how they can be used for improving the representational efficiency, effectiveness and interpretation of computer-based artifacts and also to some extent how they can be used for perceptual problem solving. However, there are interesting research issues and problems associated with management and retrieval of text, image, video, audio artifacts from single and multimedia databases. The area of multimedia information management and retrieval has in recent times become important due to the advent of the internet and World Wide Web (WWW). The solution to a number of issues in this area today lies with traditional solutions used for conventional databases. Recently, intelligent agents and human-centered information retrieval models are being proposed by researchers to address the problems in this area. In order to gain a proper understanding of the various problems and issues involved, we start this chapter by outlining the three evolution stages of this time honored area. We then describe these evolution stages in some detail. We include in this description applications and solutions proposed for multimedia information management and retrieval. We conclude the chapter by outlining open research problems in this area.

Human-centered system development is not a revolutionary concept in computer science and information systems but an evolutionary and enabling one. In this chapter we look at how some areas in computer science and information systems are evolving or moving towards human-centeredness. These areas include intelligent systems, electronic commerce, software engineering, multimedia databases, data mining, enterprise modeling and human-computer interaction. This evolution is based on the need for addressing pragmatic issues in these areas. We follow these pragmatic issues with enabling theories in philosophy, cognitive science, psychology and work-oriented design for human-centered system development framework. These theories are described and discussed in terms of their contributions toward human-centered system development framework. We conclude the chapter with a discussion section that outlines the foundations of the human-centered system development framework described in the chapter.

Electronic commerce (EC) can be broadly seen as the application of information technology and telecommunications to create trading networks where goods and services are sold and purchased, thus increasing the efficiency and the effectiveness of traditional commerce.

The objective of this chapter is to show the application of the conceptual and computational facets of the HCVM in a clinical drug prescription monitoring activity. The application is in the area of infectious diseases (Gorbach et al. 1998; Barrows et al. 1991) and addresses problems related to the gathering of patient symptomatic data, providing diagnostic assistance, and finding inconsistencies in practitioner prescribed treatments compared to those recommended by therapeutic guidelines (TG 1998).

In the last two decades digitial revolution has invaded business enterprises. Computers have enabled organizations to store gigabytes of data related to stock markets, electricity consumption profiles, troubleshooting and diagnostic data, etc.. As outlined by Fayyad and Uthuruswamy (1996a), in scientific endeavors, data represents observations carefully collected about some phenomenon under study. In business, data captures information about critical markets, competitors, and customers. On the other hand, in manufacturing, data captures performance and optimization opportunities, as well as the keys to improving process and troubleshooting problems. The reason organizations store or collect all this data is to enable them to extract some useful knowledge (at a later date!!) which can make them more productive, efficient, and competitive. The terms, Knowledge Discovery in Databases (KDD), and Data Mining have emerged in the last five years from this need of extracting useful knowledge. KDD is a nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable knowledge from data (Fayyad et al. 1996b). Data mining is a step in the KDD process that consists of applying data analysis and discovery (learning) algorithms that produce a particular enumeration of patterns (or models) over the data. In fact, the data mining process can fit into the framework of intelligent transformation systems described in chapter 3. It will be noticed in this chapter that the transformation process is largely a bottom up knowledge engineering strategy.