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Applying computational intelligence for product design is a fast-growing and promising research area in computer sciences and industrial engineering. However, there is currently a lack of books, which discuss this research area. This book discusses a wide range of computational intelligence techniques for implementation on product design. It covers common issues on product design from identificationof customer requirements in product design, determination of importance of customer requirements, determination of optimal design attributes, relating design attributes and customer satisfaction, integration of marketing aspects into product design, affective product design, to quality control of new products. Approaches for refinement of computational intelligence are discussed, in order to address different issues on product design. Cases studies of product design in terms of development of real-world new products are included, in order to illustrate the design procedures, as well as the effectiveness of the computational intelligence based approaches to product design. This book covers the state-of-art of computational intelligence methods for product design, which provides a clear picture to post-graduate students in industrial engineering and computer science. It is particularly suitable for researchers and professionals working on computational intelligence for product design. It provides concepts, techniques and methodologies, for product designers in applying computational intelligence to deal with product design.

One of the characteristics of Demand-Driven Production is that goods should be manufactured and delivered to customers within the specified pe-riod of time. Manufacturers achieve this by utilizing various efficient production planning, scheduling tools and techniques. But situations may arise where the manufacturer, despite such techniques, may not be able to meet the required de-mand. So strategies need to be developed by which situations like these are coun-tered and the financial loss from them alleviated. One such strategy is to hedge the production of goods from third party producers. But before doing so, the manufac-turer has to carry out a cost-benefit analysis that will determine the feasibility and viability of considering this option. In this chapter, we propose a methodology by which the manufacturer does the cost-benefit analysis and then makes an informed decision about whether to hedge with third party producers.

Recently mining frequent substructures from XML data has gained a considerable amount of interest. Different methods have been proposed and examined for mining frequent patterns from XML documents efficiently and effectively. While many frequent XML patterns generated are useful and interesting, it is common that a large portion of them is not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain whether the discovered XML patterns are significant and not just coincidental associations, and provide a precise statistical approach to support this framework. The proposed strategy combines data mining and statistical measurement techniques to discard the non significant patterns. In this paper we considered the “Prions” database that describes the protein instances stored for Human Prions Protein. The proposed unified framework is applied on this dataset to demonstrate its effectiveness in assessing interestingness of discovered XML patterns by statistical means. When the dataset is used for classification/prediction purposes, the proposed approach will discard non significant XML patterns, without the cost of a reduction in the accuracy of the pattern set as a whole.

Concept matching is important when heterogeneous data sources are to be merged for the purpose of knowledge sharing. It has many useful applications in areas such as schema matching, ontology matching, scientific knowledge management, e-commerce, enterprise application integration, etc. With the desire of knowledge sharing and reuse in these fields, merging commonly occurs among different organizations where the knowledge describing the same domain is to be matched. Due to the different naming conventions, granularity and the use of concepts in different contexts, a semantic approach to this problem is preferred in comparison to syntactic approach that performs matches based upon the labels only. We propose a concept matching method that initially does not consider labels when forming candidate matches, but rather utilizes structural information to take the context into account and detect complex matches. Real world knowledge representations (schemas) are used to evaluate the method.

In this paper we introduce an ontology specifically designed for product innovation. We discuss the specifics and advantages of domain ontology construction for product innovation. We then look at the application ofthe framework to the scuba diving equipment manufacturing industry domain.

While association rule mining is one of the most popular data mining techniques, it usually results in many rules, some of which are not considered as interesting or significant for the application at hand. In this paper, we conduct a systematic approach to ascertain the discovered rules and provide a rigorous statistical approach supporting this framework. The strategy proposed combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non significant rules. A real world dataset is used to demonstrate how the proposed unified framework can discard many of the redundant or non significant rules and still preserve high accuracy of the rule set as a whole.

Bioinformatics tools and systems perform a diverse range of functions including: data collection, data mining, data analysis, data management, and data integration. Computer-aided technology directly supporting medical applications is excluded from this definition and is referred to as medical informatics. This book is not an attempt at authoritatively describing the gamut of information contained in this field. Instead, it focuses on the areas of biomedical data integration, access, and interoperability as these areas form the cornerstone of the field. However, most of the approaches presented are generic integration systems that can be used in many similar contexts.

In this paper we propose the novel technique, On-line Contin-uous Ontological Evolution (OCOE) approach, which applies text mining to automate TCM (Traditional Chinese Medicine) telemedicine ontology evolution. The first step of the automation process is opening up a closed skeletal TCM ontology core (TCM onto-core) for continuous evolution and absorption of new scientific knowledge. The test-bed for the OCOE verification was the production TCM telemedicine system of the Nong's Company Limited; Nong's is a subsidiary of the PuraPharm Group in the Hong Kong SAR, which is dedicated to TCM telemedicine system development. At Nong's the skeletal TCM onto-core for clinical practice is closed (does not automatically evolve). When the OCOE is combined with the Nong's enterprise TCM onto-core it: i) invokes its text miner by default to search for new scientific findings over the open web incessantly; and ii) selectively prunes and stores useful new findings in special OCOE data structures. These data structures can be appended to the skeletal TCM onto-core logically to catalyze the evolution of the overall system TCM ontology, which is the logical combination: 'original skeletal TCM onto-core plus contents of the special OCOE data structures' The evolutionary process works on the contents of the OCOE data structures only and does not alter any skeletal TCM onto-core knowledge. This onto-core evolution approach is called the 'logical-knowledge-add-on' technique. OCOE deactivation will nullify the pointers and cut the association between the OCOE data structures and skeletal TCM onto-core, thus immediately reverting the clinical practice back to the original skeletal TCM onto-core basis.

The assessment of content quality (CQ) in social media adds a layer of complexity over traditional information quality assessment frameworks. Challenges arise in accurately evaluating the quality of content that has been created by users from different backgrounds, for different domains and consumed by users with different requirements. This paper presents a comprehensive review of 19 existing CQ assessment related frameworks for social media in addition to proposing directions for framework improvements.

In the last few years the web has evolved from just being a source of information to a platform for business applications. In parallel to this, wireless sensor network technologies have evolved to a stage where they are capable of connecting to the web. This amalgamation of technologies referred to as 'Web of Things', has created new opportunities for intelligent application development. However, the "Web of Things" has brought interesting challenges like - efficiently utilizing online sensors, sensor composition for just in time application development and others that require urgent attention. In this paper, we propose a conceptual framework and reference architecture for embedding the notions of Ubiquitous Intelligence and Computing within the 'Web of Things' and empowering their use in a broader context.

The World Health Organization predicted that depression would be the world's leading cause of disability by 2020. This is calling for urgent interventions. As most mental illnesses are caused by a number of genetic and environmental factors and many different types of mental illness exist, the identification of a precise combination of genetic and environmental causes for each mental illness type is crucial in the prevention and effective treatment of mental illness. Sophisticated data analysis tools, such as data mining, can greatly contribute in the identification of precise patterns of genetic and environmental factors and greatly help the prevention and intervention strategies. One of the factors that complicates data mining in this area is that much of the information is not in strictly structured form. In this paper, we demonstrate the application of tree mining algorithms on semi-structured mental health information. The extracted data patterns can provide useful information to help in the prevention of mental illness, and assist in the delivery of effective and efficient mental health services.

Frequent subtree mining is an important problem in the area of association rule mining from semi-structured or tree structured documents, often found in many commercial, web and scientific domains. This paper presents the u3Razor algorithm, for mining unordered embedded subtrees where the distance of nodes relative to the root of the subtree needs to be considered. Mining distance-constrained unordered embedded subtrees will have important applications in web information systems, conceptual model analysis and more sophisticated knowledge matching. An encoding strategy is presented to efficiently enumerate candidate unordered embedded subtrees taking the distance of nodes relative to the root of the subtree into account. Both synthetic and real-world datasets were used for experimental evaluation and discussion.

In this paper, we address the issues and solutions of using OWL to model the knowledge captured in relational databases. Some types of knowledge in databases cannot be modeled directly using OWL constructs. Two alternative approaches are proposed with examples of two types of knowledge. Firstly the data value range constraint and secondly the calculation knowledge representation. The first approach to the problem is to conceptualize the data range as a new class and the second solution to the problem is proposed, based on utilizing software agent technology. Examples with OWL code and implementation code are given to demonstrate the problems and solutions.

In this paper we explore the development of systems for software engineering ontology instantiations management in the methodology for multi-site distributed software development. Ultimately the systems facilitate collaboration of teams in multi-site distributed software development. In multi-site distributed environment, team members in the software engineering projects have naturally an interaction with each other and share lots of project data/agreement amongst themselves. Since theyare not always residing at the same place and face-to-face meetings hardly happen, there is a need for methodology and tools that facilitate effective communication for efficient collaboration. Whist multi-site distributed teams collaborate, there are a lot of shared project data updated or created. In a large volume of project data, systematic management is of importance. Software engineering knowledge is represented in the software engineering ontology whose instantiations, which are undergoing evolution, need a good management system. Software engineering ontology instantiations signify project information which is shared and has evolved to reflect project development, changes in the software requirements or in the design process, to incorporate additional functionality to systems or to allow incremental improvement, etc.

The use of UML 2.1 to model a broad range of systems is evident from the variety of UML diagrams in academia and in the marketplace. One class of systems currently gaining popularity are Multi-Agent systems. There are efforts underway to use UML to model these systems and these efforts are both productive and form the basis for both a methodology and a notation for systems of this type.

Before initiating a financial e-commerce interaction over the World Wide Web, the initiating agent would like to analyze the possible Risk in interacting with an agent, to ascertain the level to which it will not achieve its desired outcomes in the interaction. By analyzing the possible risk, the initiating agent can make an informed decision of its future course of action with that agent. To determine the possible risk in an interaction, the initiating agent has to determine the probability of failure and the possible consequences of failure to its resources involved in the interaction. In this chapter as a step towards riskanalysis, we propose a methodology by which the initiating agent can determine beforehand the probability of failure in interacting with an agent, to achieve its desired outcomes.

The success of the semantic web relies heavily on ontologies. However, using ontologies for this specific area poses a number of new problems. One of these problems, extracting a high quality ontology from a given base ontology, is currently receiving increasing attention. Areas such as versioning, distribution and maintenance of ontologies often involve this problem. Here, a formalism is presented that enables grouping ontology extraction requirements into different categories, called optimization schemes. These optimization schemes provide a way to introduce quality in the extraction process. An overview of the formalism is discussed, as well as a demonstration of several example optimization schemes. Each of these optimization schemes meets a certain requirement, and consists of rules and algorithms. Examples of how the formalism is deployed to reach a high-quality result, called a materialized ontology view, are covered. The presented methodology provides a foundation for further developments, and shows the possibility of obtaining usable ontologies in a highly automated way.

When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough so that the misclassification rate is kept to a minimum. In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level allows for optimizing processes such as rule splitting, merging and deleting, while the information stored at the lower level allows for determining the attribute relevance for a particular rule. The attributes detected as irrelevant can be removed and the ones previously detected as irrelevant can be reintroduced if necessary. The method is evaluated on the rules extracted from publicly available real world datasets using different classifiers, and the results demonstrate the effectiveness of the presented rule optimizing technique.

Biomedical Knowledge of Proteomics Domain is represented in the Protein Ontology, whose instantiations, which are undergoing evolution, need a good management and maintenance system. Protein Ontology instantiations signify data and information about proteins that is shared and has evolved to reflect development in Protein Ontology Project and Proteomics Domain itself. Protein Ontology needs to be populated with data and information from data and information sources proteomics domain. In this chapter we explore the implementation methodology for Data and Knowledge Management in Proteomics Domain by mapping various protein data and knowledge sources to the concepts defined in the protein ontology and developing a Protein Instance Store

RFID, as an emerging technology, has very huge potential in today's social and business developments. Security and Privacy are one of the important issues in the design of practical RFID protocols. In this paper, we focus on RFID authentication protocol. RFID mutual authentication is used to ensure that only an authorized RFID reader can access to the data of RFID tag while the RFID tag is confirmed that it releases data to the authenticated RFID reader. This paper will propose an anonymous mutual authentication protocol for RFID tag and reader. RFID tag is anonymous to RFID reader so that privacy can be preserved. In addition, mutual authentication does not need to rely on a back-end database.

Matching of knowledge structures is generally important for scientific knowledge management, e-commerce, enterprise application integration, etc. With the desire of knowledge sharing and reuse in these fields, matching commonly occurs among different organizations on the knowledge describing the same domain. In this paper we propose a knowledge matching method which makes use of our previously developed tree mining algorithms for extracting frequent subtrees from a tree structured database. Example decision trees obtained from real world domains are used for experimentation purposes whereby some important issues that arise when extracting shared knowledge through tree mining are discussed. The potential of applying tree mining algorithms for automatic discovery of common knowledge structures is demonstrated.

Architecting service-oriented systems is a complex design activity. It involves making trade-offs among a number of interdependent design decisions, which are drawn from a range of concerns by various software stakeholders. In order to achieve effective and efficient SOC design we believe a careful study of architectural styles that can form the reference architecture is important. Hence, this paper provides a study of architectural styles for the reference architecture of SOC-based software systems. We propose a classification scheme for the architecture styles. These architectural styles are extracted from existing research projects and industry practices based on our classification scheme. For all those identified styles, we present an evolution trend driven by engineering principles for Internet-scale systems. As a result, this paper moves the first step towards creating a Reference Architecture that can be utilised to provide sensible guidance on the design of Web services application architecture.

Tackling the disadvantages associated with remote communication is a challenge in multi-site software development. The challenge includes all these factors: awareness of the work that is being done according to the project plan, the work that is being done to co-operate between teams, the issues that have been currently raised, the issues that have been clarified, and a means of discussing issues and arriving at a solution within a multi-site distributed environment. If everyone working on a certain project is located in the same area, then situational awareness is relatively straightforward, but the overheads in communication can be great when team members need to meet in order to discuss the problems, to raise issues, to make decisions and to find answers in a multi-site distributed environment. In this paper, we present issues, solutions, and challenges in Multi-site Software Development. The solution main idea is to have knowledge and agreement explicitly interpreted by software tools rather than just being implicitly interpreted by human developers. The representation of software engineering concepts, software development tasks, software development models, software development processes, software development issues and development solutions, as well as software development documentation in the digital form, provides intuitive, clear, precise concepts and ideas, knowledge. Sharing knowledge facilitates a common understanding of the knowledge. This enables effective ways of reaching a consensus of understanding which is of benefit to remote team members in a distributed environment. The common knowledge is semantically shared not only among remote team members but also among software systems.

The purpose of the Trustworthiness Measure is to (a) to determine the quality of the Trusted Agents and (b) once the trusting agent has determined and recorded the trustworthiness of the trusted agent or the quality of the trusted agent, the trusting agent can use this determined and recorded quality of the trusted agent when some other agent queries it about the quality of the trusted agent. As can be clearly seen, if the trusting agent has not determined the trustworthiness of the quality of the trusted agent and subsequently recorded it, then it will not be in a position to communicate recommendations about the trusted agent. Unfortunately, in the existing literature there is no methodology for quantifying and expressing the trustworthiness of the trusted agent. In this paper we propose a methodology by that the trusting agent needs to following in order to determine the trustworthiness of the trusted agent. This methodology helps trusted business transactions, virtual collaboration and keeps the service-oriented environment trustworthy as well as helping to provide a transparent and harmonious nature to the distributed, heterogeneous, anonymous, pseudo-anonymous, and non-anonymous e-service networks.

There has been a rising need to handle and process streaming kind of data. It is continuous, unpredictable, time-varying in nature and could arrive in multiple rapid streams. Sensor data, web clickstreams, etc. are the examples of streaming data. One of the important issues about streaming data management systems is that it needs to be processed in real-time. That is, active rules can be defined over data streams for making the system reactive. These rules are triggered based on the events detected on the data stream, or events detected while summarizing the data or combination of both. In this paper, we study the challenges involved in monitoring events in a Data Stream Management System (DSMS) and how they differ from the same in active databases. We propose an architecture for event composition and detection in a DSMS, and then discuss an algorithm for detecting composite events defined on both the summarized data streams and the streaming data.

There has been a rising need to handle and process streaming kind of data. It is continuous, unpredictable, time-varying in nature and could arrive in multiple rapid streams. Sensor data, web clickstreams, etc. are the examples of streaming data. One of the important issues about streaming data management systems is that it needs to be processed in real-time. That is, active rules can be defined over data streams for making the system reactive. These rules are triggered based on the events detected on the data stream, or events detected while summarizing the data or combination of both. In this paper, we study the challenges involved in monitoring events in a Data Stream Management System (DSMS) and how they differ from the same in active databases. We propose an architecture for event composition and detection in a DSMS, and then discuss an algorithm for detecting composite events defined on both the summarized data streams and the streaming data.

The emergence of Semantic Web (SW) and the related technologies promise to make the web a meaningful experience. However, high level modelling, design and querying techniques proves to be a challenging task for organizations that are hoping to utilize the SW paradigm for their industrial applications. To address one such issue, in this paper, we propose an abstract view model with conceptual extensions for the SW. First we outline the view model, its properties and some modelling issues with the help of an industrial case study example. Then, we provide some discussions on constructing such views (at the conceptual level) using a set of operators. Later we provide a brief discussion on how such this view model can utilized in the MOVE [1] system, to design and construct materialized Ontology views to support Ontology extraction.

XML, a self-describing and semi-structured data format, is becoming a standard to represent and exchange data between applications across the Web. XML repositories are also starting to be used either to store data or as an interoperability layer for legacy applications and data sources. The widespread use of XML highlights the need for flexible access control models for XML documents to protect sensitive and valuable information from unauthorised access. This paper presents a novel declarative access control model and elaborates how this model allows the expression of access control rules in XML. The paper further introduces the operational semantics of the model by describing the Xplorer engine which supports search-browse-navigate activities on XML repositories. Xplorer takes as inputs XML-based data schema, instance data and access control rules to auto-generate an access control-enabled Web application in accordance with these rules.

EXtensible Markup Language (XML) has emerged as the dominant standard in describing and exchanging data among heterogeneous data sources. The ever increasing presence of XML web contents in large volumes creates the need to investigate Web Document Warehouses (WDW) and Web Document Marts, as a means of archiving and analysing large web contents for contextaware web/business intelligence. To address such an issue, in this paper, we focus on intuitively adopting our pervious work on XML-view based XML Document Warehouse design for building a Web Document Warehouse (WDW). To demonstrate this, here, we carryout a systematic approach to conceptual modelling and transformation of the warehouse conceptual model into a logical/schema (XML Schema) model and an in-depth analysis of deriving and querying context-aware WDW dimensions.

The proposed Fuzzy Logic Controller (FLC) is for dynamic buffer tuning at the user/server level. It eliminates buffer overflow on-line by ensuring that the buffer length always cover the queue length adaptively. The FLC contributes to prevent the AQM (active queue management) resources dished out at the system level from being wasted and to shorten the service roundtrip time (RTT) by reducing retransmission caused by user-level buffer overflow at the reception side. Combining fuzzy logic and the conventional PIDC model creates the FLC that operates with the {0,?}2 objective function. The fuzzy logic maintains the given safety margin about the reference point, which is symbolically represented by “0” in {0,?}2 . The short execution time of the FLC makes it suitable for supporting time-critical applications over the Internet.

Online transactions using mobile agents need secure protocols to help these mobile agents to accomplish the transactions initiated by a client in electronic commerce. However, the mobile agent could encounter a hostile environment. For example, a server may compromise the mobile agent and try to obtain private information from the client. A solution to tackle this issue has been proposed. The existing solution is implemented using RSA signatures that result in long signatures and heavy workloads for the mobile agent. Mobile agents will migrate from the client to a server and from one server to another in order to accomplish the client's transaction plan. Therefore, it will be interesting to re-approach this issue. We present a new scheme for secure transactions using mobile agents in potentially hostile environments. This transaction scheme is implemented by using a new undetachable signature scheme. The new undetachable signature protocol utilizes short signatures, which is desirable for low-bandwidth and efficient mobile communications.