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Discussion of XML focuses on indexing techniques for XML documents, grouping them into flat-file, semistructured, and structured indexing paradigms. Highlights include searching techniques, including full text search and multistage search; search result presentations; database and information retrieval system integration; XML query languages; and retrieval models. (Author/LRW)

Usability of software should measure both user preference and user performance. The notion of usability involves several dimensions. These dimensions include: system feedback, consistency, error prevention, performance/efficiency, user like/ dislike, and error recovery. Each of these dimensions are characterized by fuzzy aspects and linguistic terms. This paper develops a model for each of the dimensions using fuzzy-set theory. It then uses the Takagi-Sugeno fuzzy-inference approach for developing an overall measure of usability. Results are presented for several different user interfaces. Index Terms--Fuzzy systems, usability, user interfaces.

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, 'whether a total sales amount 1,000 items indicates a good or bad sales performance' is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses. Index Terms--Data warehouse, semantic model, algebraic operator, extended SQL, fuzzy set, membership function.

This paper presents an algebraic specification of net objects. By net objects we mean those that are defined in object-oriented paradigms and those defined with nested relations in complex database models. An algebraic specification is set up that involves structures of net objects, accesses of net objects, and some features of object-oriented programming, such as multiple inheritance and polymorphism. Objects and their relationships are then characterized formally in the modeling, which utilizes the hierarchical approach in the algebraic theory of abstract data types, and is further developed by adding mechanisms from existing object systems. Categories of net objects are presented with the properties of accesses among them.

In Digital Enterprises Structured Data and Semi/Unstructured Content are normally stored in two different repositories, with the first often being stored in relational Databases and the second in a content manager which is frequently at an external outsourcer. This storage of complementary information in two different silos has led to the information being processed and data mined separately which is undesirable. Effective knowledge and information use requires seamless access and intelligent analysis of information in its totality to allow enterprises to gain enhanced insights. In this paper, we develop techniques to carry out correlation of the information across different sources and then carryout out knowledge discovery across these complementary sources in a conjoint manner. The techniques developed in our research will then be used to address significant issues in four application areas namely Business, Logistics, Bioinformatics and Electric Power Systems but potential applications with significant impact are much more extensive. [ABSTRACT FROM AUTHOR]

Byline: R. Rajugan, Elizabeth Chang, Tharam S. Dillon The success of the Semantic Web (SW) relies heavily on formulation and interoperability of well-defined ontologies and their utilisation. Thus, in this context, it is interesting to investigate concepts like ontology view specification, construction and extraction. Here, in this paper, we address one such issue: ontology views. The proposed ontology view mechanism is a Layered View Model (LVM) and is specified and defined using conceptual and logical level semantics, as opposed to a query language. Our aim here is to provide a detailed theoretical foundation for LVM for ontologies. Also, our proposed concepts are illustrated using an industrial case-study example.

Byline: Robert Steele, William Gardner, Darius Chandra, Tharam S. Dillon Security of personal medical information has always been a challenge for the advancement of Electronic Health Records (EHRs) initiatives. eXtensible Markup Language (XML), is rapidly becoming the key standard for data representation and transportation. The widespread use of XML and the prospect of its use in the Electronic Health (e-health) domain highlights the need for flexible access control models for XML data and documents. This paper presents a declarative access control model for XML data repositories that utilises an expressive XML role control model. The operational semantics of this model are illustrated by Xplorer, a user interface generation engine which supports search-browse-navigate activities on XML repositories.

Trust determines the likelihood of detecting and preventing unfair practices. Trust in the virtual world also has a dynamic aspect and the management of the dynamic aspects of trust is examined.

Traffic flow forecasting is a useful technology applied to solve traffic congestion problems and to improve transportation mobility. Neural networks related approaches have been applied to develop traffic forecasting models for more than two decades. Since neural networks are sensitivity in parameters selection, selecting appropriate modeling configuration is essential to improve the accuracy and efficiency of traffic flow prediction. However, this is usually conducted by the trial-and-error method, which is very time consuming while involving too many design factors. Therefore, this paper utilizes a robust and systematic optimization approach, the Taguchi method, for obtaining the optimized configuration of the proposed exponential smoothing and extreme learning machine forecasting model. The developed model is applied to real-world data collected from freeways and highways in the United Kingdom and is compared with three existing forecasting models. The results indicate that the Taguchi method is efficient and capable for the forecasting model design and the proposed model with the optimized configuration has superior performance in traffic flow forecasting with approximate 91% and 88% accuracy rate in freeway and highway in both peak and nonpeak traffic periods. [ABSTRACT FROM AUTHOR]

Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account. [ABSTRACT FROM PUBLISHER]

Fuzzy regression methods have commonly been used to develop consumer preferences models, which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform, whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure, which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modeling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial, which includes only significant regressors. Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least-squares regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly used for new product development. In addition, smaller scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided. [ABSTRACT FROM PUBLISHER]

Smart grids have created an emerging entity of “prosumer” in the energy value network who not only consumes energy but also generates and shares the green energy with the utility grid. Hence, effective management of prosumers has become pivotal to ensure a long-term, sustainable energy-sharing process. Recently, the concept of a Prosumer Community Group (PCG) has emerged as one of the most promising and effective ways to manage prosumers. However, developing sustainable PCGs is challenging. One of the key challenges in this regard is to assess the contribution made by individual prosumers of a PCG, and find a subset of the most influential prosumers whose behavior would facilitate the long-term sustainability of the PCG. In this paper, we have focused on this challenge and proposed an innovative methodology to assess and rank the prosumers, in order to build an influential membership base. We have assessed the long-term and short-term energy behaviors of prosumers based on multiple evaluation criteria and accordingly decided the ranks of the prosumers, whereby the higher ranked prosumers are deemed to be more influential in enhancing the long-term sustenance of the PCG. Furthermore, we have presented simulation results to verify our proposed methodology. The current literature on smart-grid research field has no work investigating this challenge, making our contribution novel. [ABSTRACT FROM PUBLISHER]

On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: 1) the characteristics of current data captured by on-road sensors are assumed to be time invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and 2) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization (IPSO) algorithm is proposed to develop short-term traffic flow predictors by integrating the mechanisms of PSO, neural network and fuzzy inference system, to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems. [ABSTRACT FROM PUBLISHER]

On-road sensors provide proactive traffic control centers with current traffic flow conditions in order to forecast the future conditions. However, the number of on-road sensors is usually huge, and not all traffic flow conditions captured by these sensors are useful for predicting future traffic flow conditions. The inclusion of all captured traffic flow conditions is an ineffective means of predicting future traffic flow. Therefore, the selection of appropriate on-road sensors, which are significantly correlated to future traffic flow, is essential, although the trial and error method is generally used for the selection. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for determinations of appropriate on-road sensors, in order to capture useful traffic flow conditions for forecasting. The effectiveness of the Taguchi method is demonstrated by developing a traffic flow predictor based on the architecture of fuzzy neural networks which can perform well on traffic flow forecasting. The case study was conducted based on traffic flow data captured by on-road sensors located on a Western Australia freeway. The advantages of using the Taguchi method can be indicated: 1) Traffic flow predictors with high accuracy can be designed, and 2) development time of traffic flow predictors is reasonable. [ABSTRACT FROM PUBLISHER]

For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users' commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a significant improvement regarding speech recognition accuracy. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg–Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs. [ABSTRACT FROM PUBLISHER]

The increasing need for representing information through more complex structures where semantics and relationships among data objects can be more easily expressed has resulted in many semi-structured data sources. Structure comparison among semi-structured data objects can often reveal valuable information, and hence tree mining has gained a considerable amount of interest in areas such as XML mining, Bioinformatics, Web mining etc. We are primarily concerned with the task of mining frequent ordered induced and embedded subtrees from a database of rooted ordered labeled trees. Our previous contributions consist of the efficient Tree Model Guided (TMG) candidate enumeration approach for which we developed a mathematical model that provides an estimate of the worst case complexity for embedded subtree mining. This potentially reveals computationally impractical situations where one would be forced to constrain the mining process in some way so that at least some patterns can be discovered. This motivated our strategy of tackling the complexity of mining embedded subtrees by introducing the Level of Embedding constraint. Thus, when it is too costly to mine all frequent embedded subtrees, one can decrease the level of embedding constraint gradually down to 1, from which all the obtained frequent subtrees are induced subtrees. In this paper we develop alternative implementations and propose two algorithms MB3-R and iMB3-R, which achieve better efficiency in terms of time and space. Furthermore, we develop a mathematical model for estimating the worst case complexity for induced subtree mining. It is accompanied with a theoretical analysis of induced-embedded subtree relationships in terms of complexity for frequent subtree mining. Using synthetic and real world data we practically demonstrate the space and time efficiency of our new approach and provide some comparisons to the two well know algorithms for mining induced and embedded subtrees. [ABSTRACT FROM AUTHOR]

The recent development of Web-of-Things (WoT) and Cyber-Physical Systems (CPS) raises a new requirement of connecting abstract computational artifacts with the physical world. This requires both new theories and engineering practices that model cyber and physical resources in a unified framework, a challenge that few current approaches are able to tackle. The solution must break the boundary between the cyber world and the physical world by providing a unified infrastructure that permits integrated models addressing issues from both worlds simultaneously. This paper proposes a framework to integrate WoT and CPS. A case study is presented to demonstrate the advantage of the framework. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Modeling of manufacturing processes is important because it enables manufacturers to understand the process behavior and determine the optimum operating conditions of the process for a high yield, low cost and robust operation. However, existing techniques in modeling manufacturing processes cannot address the whole common issues in developing models for manufacturing processes: a) manufacturing processes are usually nonlinear in nature; b) a small amount of experimental data is only available for developing manufacturing process models; c) outliers often exist in experimental data; d) explicit models in a polynomial form are often preferred by manufacturing process engineers; and e) models with satisfactory prediction accuracy are required. In this paper, a modeling algorithm, namely, the particle swarm optimization-based fuzzy regression (PSO-FR) approach, is proposed to generate fuzzy nonlinear regression models, which seek to address all of the common issues in developing models for manufacturing processes. The PSO-FR first employs the operations of particle swarm optimization to generate the structures of the process models in nonlinear polynomial form, and then it employs a fuzzy coefficient generator to identify outliers in the original experimental data. Fuzzy coefficients of the process models are determined by the fuzzy coefficient generator in which the experimental data excluding the outliers is used. The effectiveness of the PSO-FR approach is evaluated by modeling the manufacturing process liquid epoxy molding process which is a commonly used technology for microchip encapsulation in electronic packaging. Results were compared with those based on the commonly used modeling methods. It was found that PSO-FR can achieve better goodness-of-fitness than other methods. Also, the prediction accuracy of the model developed based on the PSO-FR is better than the other methods. [ABSTRACT FROM PUBLISHER]

Large amount of online information is or can be represented using semi-structured documents, such as XML. The information contained in an XML document can be effectively represented using a rooted ordered labeled tree. This has made the frequent pattern mining problem recast as the frequent subtree mining problem, which is a pre-requisite for association rule mining form tree-structured documents. Driven by different application needs a number of algorithms have been developed for mining of different subtree types under different support definitions. In this paper we present an algorithm for mining unordered embedded subtrees. It is an extension of our general tree model guided (TMG) candidate generation framework and the proposed U3 algorithm considers all support definitions, namely, transaction-based, occurrence-match and hybrid support. A number of experiments are presented on synthetic and real world data sets. The results demonstrate the flexibility of our general TMG framework as well as its efficiency when compared to the existing state-of-the-art approach. [ABSTRACT FROM AUTHOR]

We propose a novel On-line Continuous Ontology Evolution and Clinical Intelligence Discovery (OCOE&CID) approach to pry open an intrinsically closed skeletal ontology core (onto-core) for real-time evolution. This approach was verified successfully with the TCM (Traditional Chinese Medicine) telemedicine system operated by the Nong's Company Limited of the Hong Kong SAR. An open evolvable onto-core is conceptually the combination of "a closed skeletal TCM onto-core plus the master aliases table (MAT) contents". The OCOE&CID mechanism automatically lets its text miner WEKA plough through the open web looking for new scientific findings. It then prunes the findings and adds the result to enrich the MAT contents. This achieves on-line knowledge acquisition, which is essential to the real-time incessant TCM onto-core evolutionary process. The MAT contents are stored in four related special data structures, which can be appended/disconnected to/from the skeletal TCM onto-core. As a result, the knowledge updates in the open evolvable onto- core affect only the MAT contents and do not alter any of the original skeletal onto-core information that was installed by consensus certification. The MAT contents are necessary for supporting the unique OCOE&CID semantic aliasing operation, which standardizes TCM knowledge with respect to the established enterprise vocabulary and discovers clinical intelligence. [ABSTRACT FROM AUTHOR]

Service-oriented computing (SOC), Grid computing, and Web2.0 computing have increasingly received momentum in both research and industry. In this paper, we propose an abstract Web-inclusive distributed computing model (WIDCOM) in order to analyse these three important distributed computing paradigms. We then advance the notion of software in existing Grid services as discussed in open Grid services architecture, which provides software middleware or wrappers for accessing hardware resources towards the notion that the resources provided can be hardware, software, or hybrid hardware/software. It also proposes an approach using the integration of Grid service, semantic Grid, and Web2.0 to overcome some of the limitations of the existing Web services architecture, which relies on having the Web version of the RPC mechanism and thus has difficulty in dealing with massive scale of user participation and communication across the Internet. The new approach produces a novel Grid architecture—GRIDSpace. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents the current state-of-the-art of the existing work in the area of tree mining with association rule mining as the case in point and XML mining as one applicable example. We discuss the increasing adoption of XML as a data exchange and storage medium and the challenges it poses in the area of frequent pattern mining. The main focus is on the elements of association mining, problems of tree enumeration, candidate generation and frequency counting. [ABSTRACT FROM AUTHOR]

Due to the inherent flexibilities in both structure and semantics, XML association rules mining faces few challenges, such as: a more complicated hierarchical data structure and ordered data context. Mining frequent patterns from XML documents can be recast as mining frequent tree structures from a database of XML documents. In this study, we model a database of XML documents as a database of rooted labeled ordered subtrees. In particular, we are mainly concerned with mining frequent induced and embedded ordered subtrees. Our main contributions are as follows. We describe our unique embedding list representation of the tree structure, which enables efficient implementation of our Tree Model Guided (TMG) candidate generation. TMG is an optimal, nonredundant enumeration strategy that enumerates all the valid candidates that conform to the structural aspects of the data. We show through a mathematical model and experiments that TMG has better complexity compared to the commonly used join approach. In this article, we propose two algorithms, MB3-Miner and iMB3-Miner. MB3-Miner mines embedded subtrees. iMB3-Miner mines induced and/or embedded subtrees by using the maximum level of embedding constraint. Our experiments with both synthetic and real datasets against two well-known algorithms for mining induced and embedded subtrees, demonstrate the effectiveness and the efficiency of the proposed techniques. [ABSTRACT FROM AUTHOR]

Privacy is becoming increasingly important due to the advent of e-commerce. E-commerce applications frequently require customers to divulge many personal details about themselves that must be protected carefully in accordance with privacy principles and regulations. Here, the authors define a privacy ontology to support the provision of privacy and help derive the level of privacy associated with e-commerce transactions and applications. The privacy ontology provides a framework against which e-commerce sites can benchmark their privacy policies and implementations. [ABSTRACT FROM AUTHOR]

The article presents a mobile agent-based system named MAGICS which is used to support business-to-consumer electronic commerce or mobile commerce applications. The authors analyze the design of mobile agent-based shopping applications and its usage. MAGICS differs from other agent-based systems due to the fact that it is a mobile agent-based system designed to address the general consumer buying process. A distributed approach is employed through which merchants can set up special servers to serve mobile agents at their own Web sites.