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Cytologic screening has been widely used for controlling the prevalence of cervical cancer. Errors from sampling, screening and interpretation, still concealed some unpleasant results. This study aims at designing a cellular image analysis system based on feasible and available software and hardware for a routine cytologic laboratory. Totally 1814 cellular images from the liquid-based cervical smears with Papanicolaou stain in 100x, 200x, and 400x magnification were captured by a digital camera. Cell images were reviewed by pathologic experts with peer agreement and only 503 images were selected for further study. The images were divided into 4 diagnostic categories. A PC-based cellular image analysis system (PCCIA) was developed for computing morphometric parameters. Then support vector machine (SVM) was used to classify signature patterns. The results show that the selected 13 morphometric parameters can be used to correctly differentiate the dysplastic cells from the normal cells (p<0.001). Additionally, SVM classifier has been demonstrated to be able to achieve a high accuracy for cellular classification. In conclusion, the proposed system provides a feasible and effective tool for the evaluation of gynecologic cytologic specimens. [ABSTRACT FROM AUTHOR]

In a health care context, the use of RFID (Radio Frequency Identi- fication) technology can be employed for not only bringing down health care costs but also to facilitate automatic streamlining patient identification processes in health centers and assist medical practitioners in quick and accurate diagnosis and treatments. In this paper, we outline a describe design and application of RFID-based Real-time Intelligent Clinical Diagnosis and Treatment Support System (ICDTS) in health care. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a common complication of diabetes that damages the eye's retina. Recognition DR as early as possible is very important to protect patients' vision. We propose a method for screening DR and distin-guishing Prolifetive Diabetic Retinopathy (PDR) from Non-Prolifetive Retino-pathy (NPDR) automatatically through color retinal images. This method evaluates the severity of DR by analyzing the appearnce of bright lesions and retinal vessel patterns. The bright lesions are extracted through morphlogical re-consturction. After that, the retinal vessels are automatically extracted using multiscale matched filters. Then the vessel patterns are analyzed by extracting the vessel net density. The experimental results domonstrate that it is a effective solution to screen DR and distinguish PDR from NPDR by only using color retinal images. [ABSTRACT FROM AUTHOR]

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use. [ABSTRACT FROM AUTHOR]

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experi-ence-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face Image Database, Image Preprocessing Module and Diagnosis Engine. Face Image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing techni-ques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%. [ABSTRACT FROM AUTHOR]

Objective tongue color analysis is an important research point for tongue diagnosis in Traditional Chinese Medicine. In this paper a research based on the clinical process of diagnosing tongue color is reported. The color data in RGB color space were first transformed into the data in CIELAB color space, and the color gamut of the displayed tongue was obtained. Then a numerical method of tongue color classification based on the Traditional Chinese Medicine (for example: light white tongue, light red tongue, red tongue) was developed. The conclusion is that this research can give the description and classification of the tongue color close to those given by human vision and may be carried out in clinical diagnosis. [ABSTRACT FROM AUTHOR]

The home remote monitoring systems aim to make a protective contribution to the well being of individuals (patients, elderly persons) requiring moderate amounts of support for independent living spaces, and improving their everyday life. Existing researches of these systems suffer from lack of experimental data and a standard medical database intended for their validation and improvement. This paper presents a multi-sensors environment for acquiring and recording a multimodal medical database, which includes physiological data (cardiac frequency, activity or agitation, posture, fall), environment sounds and localization data. It provides graphical interface functions to manage, process and index these data. The paper focuses on the system implementation, its usage and it points out possibilities for future work. [ABSTRACT FROM AUTHOR]

How to wean patients efficiently off mechanical ventilation continues to be a challenge for medical professionals. In this paper we have described a novel approach to the study of a ventilator weaning prediction system (VWPS). Firstly, we have developed and written three Artificial Neural Network (ANN) algorithms to predict a weaning successful rate based on the clinical data. Secondly, we have implemented two user-friendly weaning success rate prediction systems; the VWPS system and the BWAP system. Both systems could be used to help doctors objectively and effectively predict whether weaning is appropriate for patients based on the patients' clinical data. Our system utilizes the powerful processing abilities of MatLab. Thirdly, we have calculated the performance through measures such as sensitivity and accuracy for these three algorithms. The results show a very high sensitivity (around 80%) and accuracy (around 70%). To our knowledge, this is the first design approach of its kind to be used in the study of ventilator weaning success rate prediction. [ABSTRACT FROM AUTHOR]

In the past decade, perhaps, the most dramatic evolution, a new agenda, in organization is the dawn of the new economy associated with the value of intellectual assets. The main goal of this paper is to integrate Formal Concept Analysis (FCA) with Protégé to build up ontology-based Traditional Chinese Medicine, with Acu-point serving both as an example, and a knowledge-sharing platform. This may make the experts' knowledge visualization and understand with the general public, provide that a patient has the opportunity to participate in, and be totally aware of, the complete symptoms of his/her illness. Moreover, the platform could contribute towards eliminating the phenomenon of information disparity between patients and physicians. [ABSTRACT FROM AUTHOR]

Tongueprints are fissile texture on tongue and one of observational contents of tongue diagnosis, which is an important diagnostic method in Traditional Chinese Medicine (TCM). With deep researches on tongueprints, three basic problems emerge: (1) TCM has always held that healthy individuals do not exhibit tongueprints, but in the recent years some medical researchers have found some healthy individuals in their small sample (< 1000 cases) tongue image database do exhibit tongueprints. How about do tongueprints in a large amount of healthy individuals (> 2000 cases)? (2) If about a third of a large amount of healthy individuals have tongueprints, mainstream diagnosis by inspecting tongueprints should lead to an over-diagnosis problem and some healthy individuals are diagnosed mistakenly as unhealthy individuals because it holds tongueprints themselves declare publicly diseases. Thus it is necessary to diagnose definitely a tongueprint image belong to an unhealthy individual based on tongueprint features. This is a basic problem and is of theoretical and practical importance for diagnosis by inspecting tongueprints. And which features of tongueprints can be used to diagnose definitely a tongueprint image belong to an unhealthy individual? (3) Actually, the second problem is recognition and classification of tongueprints in healthy and unhealthy individuals. To further promote the modernization process of the traditional tongue diagnosis Chow are the researches done on automatic classification of tongueprints in healthy and unhealthy individuals? After further making sure there appear tongueprints in healthy individuals and finding which features of tongueprints can be used to diagnose definitely a tongueprint image belong to an unhealthy individual by statistic analysis on a large database of tongue images (more than 3000 cases), this paper do the researches about automatic classification of tongueprints in healthy and unhealthy individuals based on the large database. Firstly tongeprint regions are extracted by multi-direction synthetic tongueprint extraction. Then a SVM classifier is used to recognize a tongueprint image belong to a healthy or unhealthy individual based on tongueprint features. Recognition accurate rate of tongueprints in healthy and unhealthy individuals respectively are 75.81% and 74.61% based on the large database. [ABSTRACT FROM AUTHOR]

Over the past decade, the number of long-term care (LTC) residents has increased, and many have accepted treatments such as medication, rehabilitation and occupational therapy. This study discusses the effectiveness of occupational therapy when given to dementia patients of different contexts. The results of this study showed that patients of a good condition in the first stage present a more positive attitude towards participation in the occupational therapy designed by the institution; therefore, they have a greater chance of their condition improving or remaining the same. However, patients of an average condition have a more passive attitude towards taking part in any therapy; therefore, they have a greater chance of their condition deteriorating. In conclusion, occupational therapy has an effect on all kinds of patients. [ABSTRACT FROM AUTHOR]

This research develops a portable personal EKG signal monitoring system to help patients monitor their EKG signals instantly to avoid the occurrence of tragedies. This system is built with two main units: signal pro-cessing unit and monitoring and evaluation unit. The first unit consists of EKG signal sensor, signal amplifier, digitalization circuit, and related control circuits. The second unit is a software tool developed on an embedded Linux platform (called CSA). Experimental result indicates that the proposed system has the practical potential for users in health monitoring. It is demonstrated to be more convenient and with greater portability than the conventional PC-based EKG signal monitoring systems. Furthermore, all the application units embedded in the system are built with open source codes, no licensed fee is required for operating systems and authorized applications. Thus, the building cost is much lower than the traditional systems. [ABSTRACT FROM AUTHOR]

The Sublingual Vein Diagnosis, one part of Tongue Diagnosis, plays an important role in deciding the healthy condition of humans. This paper focuses on establishing a feature quantization framework for the inspection of sublingual veins of healthy humans, composed of two parts: the segmentation of sublingual veins of healthy humans and the feature quantization of them. Firstly, a novel technique of sublingual vein segmentation is proposed here. Sublingual Vein Color Model, which combines the Bayesian Decision with CIEYxy color space, is established based on a large number of labeled sublingual images. Experiments prove that the proposed method performs well on the segmentation of images from healthy humans with weak color contrast between sublingual vein and tongue proper. And then, a chromatic system in conformity with diagnostic standard of Traditional Chinese Medicine doctors is established to describe the chromatic feature of sublingual veins. Experimental results show that the geometrical and chromatic features quantized by the proposed framework are properly consistent with the diagnostic standard summarized by TCM doctors for healthy humans. [ABSTRACT FROM AUTHOR]

The primary prevention of disease related to the lifestyle is an essential theme in medical research. Preventing before it arises is the important concept in traditional Chinese medicine (TCM). Since TCM, which emphasizes individual physical condition in medical treatment, has recently attracted considerable attention globally, objective diagnostic methods in TCM have been investigated in this work. Firstly, the fuzzy theory was applied to develop a tongue diagnosis supporting system based on the tongue diagnosis in TCM. Secondly, the usefulness of TCM health questionnaire was examined to identify individual physical condition. Our results suggest that the TCM health questionnaire is useful in the construction of a health supporting system based on TCM. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive feature extraction method for pattern recognition of hand gesture action sEMG to enhance the reusability of myoelectric control. The feature extractor is based on wavelet packet transform and Local Discriminant Basis (LDB) algorithms to select several optimized decomposition subspaces of origin SEMG waveforms caused by hand gesture motions. Then the square roots of mean energy of signal in those subspaces are calculated to form the feature vector. In data acquisition experiments, five healthy subjects implement six kinds of hand motions every day for a week. The recognition results of hand gesture on the basis of the measured SEMG signals from different use sessions demonstrate that the feature extractor is effective. Our work is valuable for the realization of myoelectric control system in rehabilitation and other medical applications. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) of the brain is preferred study on neurological emergencies. Physicians use CT to diagnose various types of intracranial hematomas, including epidural, subdural and intracerebral hematomas according to their locations and shapes. We propose a novel method that can automatically diagnose intracranial hematomas by combining machine vision and knowledge discovery techniques. The skull on the CT slice is located and the depth of each intracranial pixel is labeled. After normalization of the pixel intensities by their depth, the hyperdense area of intracranial hematoma is segmented with multi-resolution thresholding and region-growing. We then apply C4.5 algorithm to construct a decision tree using the features of the segmented hematoma and the diagnoses made by physicians. The algorithm was evaluated on 48 pathological images treated in a single institute. The two discovered rules closely resemble those used by human experts, and are able to make correct diagnoses in all cases. [ABSTRACT FROM AUTHOR]

In the protein universe, many proteins are composed of two or more polypeptide chains, generally referred to as subunits, which associate through noncovalent interactions and, occasionally, disulfide bonds to form protein quaternary structures. It has been known for long that the functions of proteins are closely related to their quaternary structure. With the number of protein sequences entering into data banks rapidly increasing, it is highly desirable to predict protein quaternary structures automatically from their primary sequences. Here, multi-scale energy of factor solution scores and feature combination were employed to form various input feature vectors, and the multi-class support vector machine (SVM) classifier modules were adopted for predicting protein quaternary structures. The rates of correct identification suggest that the individual primary sequence of an oligomeric protein do contain the information of its quaternary structure. The results of multi-scale energy of Factor 1 solution scores indirectly prove that biologically relevant complex formation is driven predominantly by the hydrophobic effect. The current approach is quite promising and may at least play a complimentary role to the existing methods. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading cancer among females, the key technology of preventing breast cancers is early detection. Based on the advantage of support vector machine (SVM), finding global solution and possessing higher generalization capability on dealing with the small sample, a new method of diagnosing breast cancer by CAD is proposed in this paper. Firstly, a principal component analysis is used to represent the information of ROI image, which account for most of the variance of the original data set while significantly reducing the data dimension. After the extraction of principal components, only those data of which account for most part of variance were retained as the feature vector and input into a SVM classification and BP neural network classification to classify. Finally, the results of experiment show that the accuracy and specificity for the diagnosis of breast cancer using SVM classification is good. [ABSTRACT FROM AUTHOR]

This paper attempts to extract tongue cracks, one of pathological features in tongue diagnosis. A detection scheme is proposed to extract tongue cracks. This scheme is based on the wide line detector which extracts the whole of the line by employing an isotropic nonlinear filter. The wide line detector is improved based on the properties of tongue cracks. The proposed scheme has been tested on a total of 286 cracked tongue images and our experimental results demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

The goal of this study is to explore the effects of electrode place-ment on the hand gesture pattern recognition performance. We have conducted experiments with surface EMG sensors using two detecting electrode channels. In total 25 different hand gestures and 10 different electrode positions for measuring muscle activities have been evaluated. Based on the experimental results, dependencies between surface EMG signal detection positions and hand gesture recognition performance have been analyzed and summarized as suggestions how to define hand gestures and select suitable electrode positions for a myoelectric control system. This work provides useful insight for the development of a medical rehabilitation system based on EMG technique. [ABSTRACT FROM AUTHOR]

Brain computed tomography (CT) image based computer-aided diagnosis (CAD) system is helpful for clinical diagnosis and treatment. However it is challenging to extract significant features for analysis because CT images come from different people and CT operator. In this study, we apply nonnegative matrix factorization to extract both appearance and histogram based semantic features of images for clustering analysis as test. Our experimental results on normal and tumor CT images demonstrate that NMF can discover local features for both visual content and histogram based semantics, and the clustering results show that the semantic image features are superior to low level visual features. [ABSTRACT FROM AUTHOR]

In this paper, QRS morphological features and the artificial neural network method was used for Electrocardiogram (ECG) pattern classification. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. Authors propose a set of six ECG morphological features to reduce the feature vector size considerably and make the training process fast in addition to a simple but effective ECG heartbeat extraction scheme. Three types of artificial neural network models, MLP, RBF neural networks and SOFM were separately trained and tested for ECG pattern recognition and the experimental results of the different models have been compared. The MLP network exhibited the best performance and reached an overall test accuracy of 99.65%, and RBF and SOFM network both reached 99.1%. The performance of these classifiers was also evaluated in presence of additive Gaussian noise. MLP network was found to be more robust in this respect. [ABSTRACT FROM AUTHOR]

This paper proposes a novel feature selection technique for SELDI-TOF spectrum data. The new technique, called RISC (Relevance Index by Sample Counting) , measures the relevance of features based on each sample's discriminating power to partition the samples in the opposite class. We also proposes a heuristic searching method to obtain the optimal feature set, which makes use of the relevance parameters. Our technique is fast even for extremely high-dimensional datasets such as SELDI spectrum, since it has low computational complexity and consists of simple counting operations. The new technique also shows good performance comparable to the conventional feature selection techniques from the experiment on three clinical datasets from NCI/CCR and FDA/CBER Clinical Proteomics Program Databank: Ovarian 4-3-02, Ovarian 7-8-02, Prostate. [ABSTRACT FROM AUTHOR]

Screening for oral cancer in its early stage is of utmost importance for improving the survival rate of oral cancer patients. The current method of visual examination followed by biopsy of suspected cases is subjective with inter and intra personal variations. With a low ratio of oral-cancer experts to patients compounded by the reluctance of patients to undergo biopsy in rural India. The situation cries out for automatic screening device for oral cancer. In this context, optical spectroscopy based on Laser Induced Fluorescence (LIF) has been shown to be a promising technique in distinguishing between cancerous and benign lesions in the mouth. However, it has been observed that it is very difficult to distinguish pre-malignant spectra from malignant and normal spectra recorded in-vivo. Hence, obtaining the most discriminating features from the spectra becomes important. In this article a new method of feature selection is proposed using mean-shift and Recursive Feature Elimination (RFE) techniques to increase discrimination ability of the feature vectors. Performance of the algorithm is evaluated on a in-vivo recorded LIF data set consisting of spectra from normal, malignant and pre-malignant patients. Sensitivity of above 95% and specificity of above 99% towards malignancy are obtained using the proposed method. [ABSTRACT FROM AUTHOR]

Tongueprint images of healthy populations can be differentiated from those of unhealthy populations by features of tongueprints (tongueprint is fissile texture on the tongue) according to observation by naked eyes, classification, and statistical analysis on tongueprints for large tongue images of healthy and unhealthy populations. Tongueprint binary image is gotten by an existed method, and then tongue images are classified as no-tongueprint and tongueprint images by our approaches. In terms of obtained statistical results on tongueprints, a series of computerized methods, for example computing length ratio of long to short axis for optimal fitting ellipse of tongueprint binary regions, getting location and amount of extremity and cross point of tongueprint skeletons by computing pixel connective numeral to determine pixel type, straight line segment approach, support vector machine (SVM) classifier and so on, are employed to recognize tongueprint images of healthy and unhealthy populations. Applying our method to the large database of tongue images, we achieve promising experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative approach for composite ontology mapping. We first present an extended classification of automated ontology matching and propose an automatic composite solution for the matching problem based on cooperation. In our proposal, agents apply individual mapping algorithms and cooperate in order to change their individual results. We assume that the approaches are complementary to each other and their combination produces better results than the individual ones. Next, we compare our model with three state of the art matching systems. The results are promising specially for what concerns precision and recall. Finally, we propose an argumentation formalism as an extension of our initial model. We compare our argumentation model with the matching systems, showing improvements on the results. [ABSTRACT FROM AUTHOR]

Information integration and retrieval are useful tasks in many information systems. In these systems, it is far from an easy task to directly integrate information from natural language (NL) sources, because precisely capturing NL semantics is not a trivial issue in the first place. In this paper, we choose the botanical domain to investigate this issue. While most existing systems in this domain support only keyword-based search, this paper introduces an ontology-based approach to process parallel colour descriptions from botanical documents. Based on a semantic model, it takes advantage of ontologies so as to represent the semantics of colour descriptions precisely, to integrate parallel descriptions according to their semantic distances, and to answer colour-related species identification queries. To evaluate this approach, we implement a colour reasoner based on the FaCT-DG Description Logic reasoner, and present some results of our experiments on integrating parallel descriptions and species identification queries. From this highly specialised domain, we learn a set of more general methodological rules. [ABSTRACT FROM AUTHOR]

Many organizations nowadays face the problem of accessing existing data sources by means of flexible mechanisms that are both powerful and efficient. Ontologies are widely considered as a suitable formal tool for sophisticated data access. The ontology expresses the domain of interest of the information system at a high level of abstraction, and the relationship between data at the sources and instances of concepts and roles in the ontology is expressed by means of mappings. In this paper we present a solution to the problem of designing effective systems for ontology-based data access. Our solution is based on three main ingredients. First, we present a new ontology language, based on Description Logics, that is particularly suited to reason with large amounts of instances. The second ingredient is a novel mapping language that is able to deal with the so-called impedance mismatch problem, i.e., the problem arising from the difference between the basic elements managed by the sources, namely data, and the elements managed by the ontology, namely objects. The third ingredient is the query answering method, that combines reasoning at the level of the ontology with specific mechanisms for both taking into account the mappings and efficiently accessing the data at the sources. [ABSTRACT FROM AUTHOR]

The goal of this paper is to identify various aspects of context-awareness needed to facilitate semantics integration of data, and to discuss how this knowledge may be represented within ontologies. We first present a taxonomy of ontologies and we show how various kinds of ontologies may cooperate. Then, we compare ontologies and conceptual models. We claim that their main difference is the consensual nature of ontologies when conceptual models are specifically designed for one particular target system. Reaching consensus, in turn, needs specific models of which context dependency has been represented and minimized. We identify five principles for making ontologies less contextual than models and suitable for data integration and we show, as an example, how these principles have been implemented in the PLIB ontology model developed for industrial data integration. Finally, we suggest a road map for switching from conventional databases to ontology-based databases without waiting until standard ontologies are available in every domains. [ABSTRACT FROM AUTHOR]

Joining up services in e-Government usually implies governmental agencies acting in concert without a central control regime. This requires to the sharing scattered and heterogeneous data. Semantic Web Service (SWS) technology can help to integrate, mediate and reason between these datasets. However, since a few real-world applications have been developed, it is still unclear which are the actual benefits and issues of adopting such a technology in the e-Government domain. In this paper, we contribute to raising awareness of the potential benefits in the e-Government communityby analyzing motivations, requirements and expected results, before proposing a reusable SWS-based framework. We demonstrate the application of this framework by showing how integration and interoperability emerge from this model through a cooperative and multi-viewpoint methodology. Finally, we illustrate added values and lessons learned by two compelling case studies: a change of circumstances notification system and a GIS-based emergency planning system, and describe key challenges which remain to be addressed. [ABSTRACT FROM AUTHOR]

The ability to deal with inconsistencies and to evaluate the impact of possible solutions for resolving inconsistencies are of the utmost importance in real world ontology applications. The common approaches either identify the minimally unsatisfiable sub-ontologies or the maximally satisfiable sub-ontologies. However there is little work which addresses the issue of rewriting the ontology; it is not clear which axioms or which parts of axioms should be repaired, nor how to repair those axioms. In this paper, we address these limitations by proposing an approach to resolving unsatisfiable ontologies which is fine-grained in the sense that it allows parts of axioms to be changed. We revise the axiom tracing technique first proposed by Baader and Hollunder, so as to track which parts of the problematic axioms cause the unsatisfiability. Moreover, we have developed a tool to support the ontology user in rewriting problematic axioms. In order to minimise the impact of changes and prevent unintended entailment loss, both harmful and helpful changes are identified and reported to the user. Finally we present an evaluation of our interactive debugging tool and demonstrate its applicability in practice. [ABSTRACT FROM AUTHOR]

We explore flexible querying of RDF data, with the aim of making it possible to return data satisfying query conditions with varying degrees of exactness, and also to rank the results of a query depending on how "closely" they satisfy the query conditions. We make queries more flexible by logical relaxation of their conditions based on RDFS entailment and RDFS ontologies. We develop a notion of ranking of query answers, and present a query processing algorithm for incrementally computing the relaxed answer of a query. Our approach has application in scenarios where there is a lack of understanding of the ontology underlying the data, or where the data objects have heterogeneous sets of properties or irregular structures. [ABSTRACT FROM AUTHOR]

In this paper we propose an asymmetric semantic similarity among instances within an ontology. We aim to define a measurement of semantic similarity that exploit as much as possible the knowledge stored in the ontology taking into account different hints hidden in the ontology definition. The proposed similarity measurement considers different existing similarities, which we have combined and extended. Moreover, the similarity assessment is explicitly parameterised according to the criteria induced by the context. The parameterisation aims to assist the user in the decision making pertaining to similarity evaluation, as the criteria can be refined according to user needs. Experiments and an evaluation of the similarity assessment are presented showing the efficiency of the method. [ABSTRACT FROM AUTHOR]

The longstanding problem of automatic table interpretation still illudes us. Its solution would not only be an aid to table processing applications such as large volume table conversion, but would also be an aid in solving related problems such as information extraction and semi-structured data management. In this paper, we offer a conceptual modeling solution for the common special case in which so-called sibling pages are available. The sibling pages we consider are pages on the hidden web, commonly generated from underlying databases. We compare them to identify and connect nonvarying components (category labels) and varying components (data values). We tested our solution using more than 2,000 tables in source pages from three different domains—car advertisements, molecular biology, and geopolitical information. Experimental results show that the system can successfully identify sibling tables, generate structure patterns, interpret tables using the generated patterns, and automatically adjust the structure patterns, if necessary, as it processes a sequence of hidden-web pages. For these activities, the system was able to achieve an overall F-measure of 94.5%. [ABSTRACT FROM AUTHOR]

We deal with the problem of automated analysis of web service protocol compatibility and replaceability in presence of timing abstractions. We first present a timed protocol model for services and identify different levels of compatibility and replaceability that are useful to support service development and evolution. Next, we present operators that can perform such analysis. Finally, we present operators properties by showing that timed protocols form a new class of timed automata, and we briefly present our implementation. [ABSTRACT FROM AUTHOR]

As the Web continues to grow and evolve, more and more information is being placed in structurally rich documents, XML documents in particular, so as to improve the efficiency of similarity clustering, information retrieval and data management applications. Various algorithms for comparing hierarchically structured data, e.g., XML documents, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being modeled as ordered labeled trees. Nevertheless, a thorough investigation of current approaches led us to identify several structural similarity aspects, i.e. sub-tree related similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an improved comparison method to deal with fine-grained sub-trees and leaf node repetitions, without increasing overall complexity with respect to current XML comparison methods. Our approach consists of two main algorithms for discovering the structural commonality between sub-trees and computing tree-based edit operations costs. A prototype has been developed to evaluate the optimality and performance of our method. Experimental results, on both real and synthetic XML data, demonstrate better performance with respect to alternative XML comparison methods. [ABSTRACT FROM AUTHOR]

Data warehousing and OLAP are mainly used for the analysis of transactional data. Nowadays, with the evolution of Internet, and the development of semi-structured data exchange format (such as XML), it is possible to consider entire fragments of data such as documents as analysis sources. As a consequence, an adapted multidimensional analysis framework needs to be provided. In this paper, we introduce an OLAP multidimensional conceptual model without facts. This model is based on the unique concept of dimensions and is adapted for multidimensional document analysis. We also provide a set of manipulation operations. [ABSTRACT FROM AUTHOR]

Processing a twig pattern query in XML document includes structural search and content search. Most existing algorithms only focus on structural search. They treat content nodes the same as element nodes during query processing with structural joins. Due to the high variety of contents, to mix content search and structural search suffers from management problem of contents and low performance. Another disadvantage is to find the actual values asked by a query, they have to rely on the original document. In this paper, we propose a novel algorithm ValueExtractionwithRelationalTable (VERT) to overcome these limitations. The main technique of VERT is introducing relational tables to store document contents instead of treating them as nodes and labeling them. Tables in our algorithm are created based on semantic information of documents. As more semantics is captured, we can further optimize tables and queries to significantly enhance efficiency. Last, we show by experiments that besides solving different content problems, VERT also has superiority in performance of twig pattern query processing compared with existing algorithms. [ABSTRACT FROM AUTHOR]

One of the challenges of the Semantic Web is to integrate the huge amount of information already available on the standard Web, usually stored in relational databases. In this paper, we propose a formalization of a logic model of relational databases, and a transformation of that model into OWL, a Semantic Web language. This transformation is implemented in Cross, as an open-source prototype. We prove a relation between the notion of legal database state and the consistency of the corresponding OWL knowledge base. We then show how that transformation can prove useful to enhance databases, and integrate them in the Semantic Web. [ABSTRACT FROM AUTHOR]

Current graphical notations for XML Schema do not raise the level of abstraction for XML schemata in the same way traditional conceptual models raise the level of abstraction for data schemata. Traditional conceptual models, on the other hand, do not accommodate several XML Schema structures. Thus, there is a need to enrich traditional conceptual models with new XML Schema features. After establishing criteria for XML conceptual modeling, we propose an enrichment to represent the XML features missing in traditional models. We argue that our solution can be adapted generally for traditional conceptual models and show how it can be adapted for two popular conceptual models. [ABSTRACT FROM AUTHOR]

A continuous evolution of business process parameters, constraints and needs, hardly foreseeable initially, requires from the business process management systems a continuous design and a reliable process model. In this paper, we are interested in developing a reactive design through a process log analysis ensuring process re-engineering and execution reliability. We propose to analyse workflow logs to discover workflow transactional behavior and to improve and correct related recovery mechanisms subsequently. Our approach starts by collecting workflow logs. Then, we build, by statistical analysis techniques, an intermediate representation specifying elementary dependencies between activities. These dependencies are refined to mine the transactional workflow model. The analysis of the discrepancies between the discovered model and the initially designed model enables us to detect design gaps, concerning particularly the recovery mechanisms. Thus, based on this mining step, we apply a set of rules on the initially designed workflow to improve workflow reliability. [ABSTRACT FROM AUTHOR]

Neglecting traceability—i.e., the ability to describe and follow the life of a requirement—is known to entail misunderstanding and miscommunication, leading to the engineering of poor quality systems. Following the simple principles that (a) changes to UML model instances ought be justified to the stakeholders, (b) justification should proceed in a structured manner to ensure rigor in discussions, critique, and revisions of model instances, and (c) the concept of argument instantiated in a justification process ought to be well defined and understood, the present paper introduces the UML Traceability through Argumentation Method (UML-TAM) to enable the traceability of design rationale in UML while allowing the appropriateness of model changes to be checked by analysis of the structure of the arguments provided to justify such changes. [ABSTRACT FROM AUTHOR]

Enterprise integration refers to solutions that facilitate meaningful interactions among heterogeneous legacy applications. The scale, complexity and specificity of most enterprise integration efforts mean that design knowledge for enterprise integration has resisted codification. Important exceptions to this include: use of Business Process Models (BPM) to understand integration requirements; and Enterprise Integration Patterns (EIP), which present designers with abstract descriptions of recurring design tactics for integrating applications. The two, however, can be at odds. BPM encourages the control flow perspective; whereas EIP codifies an operational perspective. Mapping between the two to develop coherent solutions, therefore, tends to be problematic. To bridge the gap, we suggest an approach that builds on the theory of speech acts. We develop essential components of such an approach, including a re-representation of EIP as structures of speech acts, a characterization of tasks in BPM with action types, and a mapping between speech acts and action types. These components are accompanied by inference rules that produce a mapping between sets of tasks in a business process and structures of speech acts to allow reasoning on identification of appropriate EIPs for given set of tasks. We demonstrate usefulness of the proposed approach by application to industry cases. [ABSTRACT FROM AUTHOR]

Modeling is an important and time consuming part of the Business Process Management life-cycle. An analyst reviews existing documentation and queries relevant domain experts to construct both mental and concrete models of the domain. To aid this exercise, we propose the Rapid Business Process Discovery (R-BPD) framework and prototype tool that can query heterogeneous information resources (e.g. corporate documentation, web-content, code e.t.c.) and rapidly construct proto-models to be incrementally adjusted to correctness by an analyst. This constitutes a departure from building and constructing models toward just editing them. We believe this rapid mixed-initiative modeling will increase analyst productivity by significant orders of magnitude over traditional approaches. Furthermore, the possibility of using the approach in distributed and real-time settings seems appealing and may help in significantly improving the quality of the models being developed w.r.t. being consistent, complete, and concise. [ABSTRACT FROM AUTHOR]

This paper discusses extending the Unified Modelling Language by means of a profile for modelling knowledge-based system in the context of Model Driven Architecture (MDA) framework. The profile is implemented using the eXecutable Modelling Framework (XMF) Mosaic tool. A case study from the health care domain demonstrates the practical use of this profile; with the prototype implemented in Java Expert System Shell (Jess). The paper also discusses the possible mapping of the profile elements to the platform specific model (PSM) of Jess and provides some discussion on the Production Rule Representation (PRR) standardisation work. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for conceptual modeling which is based on a new modeling primitive, the niche, and associated constructs granularity and reconciliation. A niche is an environment where entities interact for a specific purpose, playing specific roles, and according to the norms and constraints of that environment. Granularity refers to the relative level of power or influence of an entity within a niche. Reconciliation is a relationship from N entities onto one reconciled entity, and represents explicitly a situation where two or more different perspectives of the same entity have been reconciled, by negotiation, into a single consensus view. The methodology we propose provides a systematic method of designing conceptual models along with a process for normalising inappropriate relationships. Normalising is a prescriptive process for identifying and remedying inconsistencies within a model based on granularities. Drawing on a number of case studies, we show how niches and granularity make complexity easier to manage, highlight inaccuracies in a model, identify opportunities for achieving project goals, and reduce semantic heterogeneity. [ABSTRACT FROM AUTHOR]

Engineering has a long tradition in analyzing the safety of mechanical, electrical and electronic systems. Important methods like HazOp and FMEA have also been adopted by the software engineering community. The misuse case method, on the other hand, has been developed by the software community as an alternative to FMEA and preliminary HazOp for software development. To compare the two methods misuse case and FMEA we have run a small experiment involving 42 third year software engineering students. In the experiment, the students should identify and analyze failure modes from one of the use cases for a commercial electronic patient journals system. The results of the experiment show that on the average, the group that used misuse cases identified and analyzed more user related failure modes than the persons using FMEA. In addition, the persons who used the misuse cases scored better on perceived ease of use and intention to use. [ABSTRACT FROM AUTHOR]

We investigate the computational complexity of reasoning over various fragments of the Extended Entity-Relationship (EER) language, which includes a number of constructs: isa between entities and relationships, disjointness and covering of entities and relationships, cardinality constraints for entities in relationships and their refinements as well as multiplicity constraints for attributes. We extend the known ExpTime-completeness result for UML class diagrams [5] and show that reasoning over EER diagrams with isa between relationships is ExpTime-complete even without relationship covering. Surprisingly, reasoning becomes NP-complete when we drop isa between relationships (while still allowing all types of constraints on entities). If we further omit disjointness and covering over entities, reasoning becomes polynomial. Our lower complexity bound results are proved by direct reductions, while the upper bounds follow from the correspondences with expressive variants of the description logic DL-Lite, which we establish in this paper. These correspondences also show the usefulness of DL-Lite as a language for reasoning over conceptual models and ontologies. [ABSTRACT FROM AUTHOR]

We consider the problem of adding both equality and order generating dependencies to Web ontology languages such as OWL DL that are based on description logics. Such dependencies underlie a number of problems that relate, for example, to web service composition, to document ordering, and to lower level algorithmic issues in service plan generation and evaluation. [ABSTRACT FROM AUTHOR]

Business modelling research is increasingly interested in exploring how domain ontologies can be used as reference models for business models. The Resource Event Agent (REA) ontology is a primary candidate for ontology-driven modelling of business processes because the REA point of view on business reality is close to the conceptual modelling perspective on business models. In this paper Ontology Engineering principles are employed to reengineer REA in order to make it more suitable for ontology-driven business modelling. The new conceptual representation of REA that we propose uses a single representation formalism, includes a more complete domain axiomatizat-ion (containing definitions of concepts, concept relations and ontological axioms), and is proposed as a generic model that can be instantiated to create valid business models. The effects of these proposed improvements on REA-driven business modelling are demonstrated using a business modelling example. [ABSTRACT FROM AUTHOR]