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1 – 10 of over 41000Michael Fellmann, Agnes Koschmider, Ralf Laue, Andreas Schoknecht and Arthur Vetter
Patterns have proven to be useful for documenting general reusable solutions to a commonly occurring problem. In recent years, several different business process management…
Abstract
Purpose
Patterns have proven to be useful for documenting general reusable solutions to a commonly occurring problem. In recent years, several different business process management (BPM)-related patterns have been published. Despite the large number of publications on this subject, there is no work that provides a comprehensive overview and categorization of the published business process model patterns. The purpose of this paper is to close this gap by providing a taxonomy of patterns as well as a classification of 89 research works.
Design/methodology/approach
The authors analyzed 280 research articles following a structured iterative procedure inspired by the method for taxonomy development from Nickerson et al. (2013). Using deductive and inductive reasoning processes embedded in concurrent as well as joint research activities, the authors created a taxonomy of patterns as well as a classification of 89 research works.
Findings
In general, the findings extend the current understanding of BPM patterns. The authors identify pattern categories that are highly populated with research works as well as categories that have received far less attention such as risk and security, the ecological perspective and process architecture. Further, the analysis shows that there is not yet an overarching pattern language for business process model patterns. The insights can be used as starting point for developing such a pattern language.
Originality/value
Up to now, no comprehensive pattern taxonomy and research classification exists. The taxonomy and classification are useful for searching pattern works which is also supported by an accompanying website complementing the work. In regard to future research and publications on patterns, the authors derive recommendations regarding the content and structure of pattern publications.
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This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus (FRC) and genetic algorithm (GA).
Abstract
Purpose
This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus (FRC) and genetic algorithm (GA).
Design/methodology/approach
The paper introduces a new interpretation of multidimensional fuzzy implication (MFI) to represent the author's knowledge about the training data set. It also considers the notion of a fuzzy pattern vector (FPV) to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one‐dimensional fuzzy implication. For the estimation of Ri floating point representation of GA is used. Thus, a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non‐fuzzy features of a test pattern can be classified.
Findings
The performance of the proposed scheme is tested on synthetic data. Subsequently, the paper uses the proposed scheme for the vowel classification problem of an Indian language. In all these case studies the recognition score of the proposed method is very good. Finally, a benchmark of performance is established by considering Multilayer Perceptron (MLP), Support Vector Machine (SVM) and the proposed method. The Abalone, Hosse colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. The benchmark study also establishes the superiority of the proposed method.
Originality/value
This new soft computing approach to pattern classification is based on a new interpretation of MFI and a novel notion of FPV. A set of fuzzy relations which is the core of the pattern classifier, is estimated using floating point GA and very effective classification of patterns under vague and imprecise environment is performed. This new approach to pattern classification avoids the curse of high dimensionality of feature vector. It can provide multiple classifications under overlapped classes.
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– The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.
Abstract
Purpose
The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.
Design/methodology/approach
The authors use grey relational analysis to implement a relationship-based similarity measure for tolerance rough sets.
Findings
The proposed classification method has been tested on several real-world data sets. Its classification performance is comparable to that of other rough-set-based methods.
Originality/value
The authors design a variant of a similarity measure which can be used to estimate the relationship between any two patterns, such that the closer the relationship, the greater the similarity will be.
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Sezgin Kaya and Keith Alexander
To present the development and results of the classification system based on the preceding paper (Journal of Facilities Management; Vol. 4, No. 2), which has highlighted ten…
Abstract
Purpose
To present the development and results of the classification system based on the preceding paper (Journal of Facilities Management; Vol. 4, No. 2), which has highlighted ten patterns for the identification of similarity and dissimilarity of FM organisations (FMOs).
Design/methodology/approach
This paper reports on the application of these ten patterns onto 22 in‐house FMOs in the UK, and tests the applicability and the validity of the classification system. Pattern recognition's unsupervised clustering is used for measuring the similarities between the sample population. Two particular statistical methods have been used in hybrid: principal component analysis and k‐means.
Findings
As a result of the analysis, out of 22 samples, three classes of FMOs are found. The two of these (w1 and w3) involve mixed market sectors and the other involves only healthcare FMOs.
Research limitations/implications
The classification system enables us to group FMOs according to their similarities for identification and description. Ore specifically there are three implications of the system: networking for best practice sharing and learning, development of demand side market intelligence, and comparison of performance in respective groups.
Originality/value
The classification system introduces a systematic approach to understand and classify FMO, and hence describe their characteristic features. It has been demonstrated in this paper that results of such as system lead to knowledge and practice exchange between two FMOs.
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Ovidiu Ghita, Dana Ilea, Antonio Fernandez and Paul Whelan
The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary…
Abstract
Purpose
The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters.
Design/methodology/approach
The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases.
Findings
The most important finding resulting from this study is that although the LBP/C and the multi‐channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture.
Practical implications
Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding.
Originality/value
This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi‐channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.
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Shervan Fekriershad and Farshad Tajeripour
The purpose of this paper is to propose a color-texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise…
Abstract
Purpose
The purpose of this paper is to propose a color-texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for this proposed approach.
Design/methodology/approach
One of the efficient texture analysis operations is local binary patterns (LBP). The proposed approach includes two steps. First, a noise resistant version of color LBP is proposed to decrease its sensitivity to noise. This step is evaluated based on combination of color sensor information using AND operation. In a second step, a significant points selection algorithm is proposed to select significant LBPs. This phase decreases final computational complexity along with increasing accuracy rate.
Findings
The proposed approach is evaluated using Vistex, Outex and KTH-TIPS-2a data sets. This approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy. In two other experiments, results show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi-resolution and general usability are other advantages of our proposed approach.
Originality/value
In the present paper, a new version of LBP is proposed originally, which is called hybrid color local binary patterns (HCLBP). HCLBP can be used in many image processing applications to extract color/texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images.
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Blesson Varghese and Gerard McKee
The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need…
Abstract
Purpose
The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems.
Design/methodology/approach
The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation.
Findings
A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment.
Originality/value
This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.
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The presented principal objective is information retrieval from tepetitiously generated data sets. In contrast to conventional, formally statistical analysis, the genesis of data…
Abstract
The presented principal objective is information retrieval from tepetitiously generated data sets. In contrast to conventional, formally statistical analysis, the genesis of data is postulated as the principal feature of analysis for obtaining the optimum of information from any data set. Consequently, arbitrary randomization of naturally sequential data, formal averaging, and “amplitude‐classification” of formal statistics are rejected because of suppression of information. Preservation of information is accomplished by pattern classification of coherent data sets into characteristic pattern prototypes. Examples are given.
These proceedings cover a study conference of the Federation Internationale de Documentation, held at Beatrice Webb House, Dorking, Surrey, from the 13th to the 17th May 1957…
Abstract
These proceedings cover a study conference of the Federation Internationale de Documentation, held at Beatrice Webb House, Dorking, Surrey, from the 13th to the 17th May 1957, following a decision taken at the Brussels Conference of the F.I.D. in September 1955.
Sergei O. Kuznetsov, Alexey Masyutin and Aleksandr Ageev
The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.
Abstract
Purpose
The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.
Design/methodology/approach
Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm.
Findings
The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability.
Originality/value
This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.
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