Third international workshop on empirical research in business process management (ER-BPM 2012)

Empirical research in business process management(BPM) is coming of age. In 2009, when the inaugural ER-BPM workshop was held, the field of BPM research was characterized by a strong emphasis on solution development, but also by an increasing demand for insights or evaluations of BPM technology based on dedicated empirical research strategies. The ER-BPM workshop series was created to provide an international forum for researchers to discuss and present such research.

The process and patterns of combining the use of traditional Chinese medicine and Western medicine in Taiwanese people with cancer

Objective: The nature of contemporary cancer therapy means that patients are faced with difficult treatment decisions about surgery, chemotherapy and radiotherapy. For some, this process may also involve consideration of therapies that sit outside the biomedical approach to cancer treatment, in our research, traditional Chinese medicine (TCM). Thus, it is important to explore how cancer patients in Taiwan incorporate TCM into their cancer treatment journey. This paper aims to explore of the patterns of combining the use of TCM and Western medicine into cancer treatment journey in Taiwanese people with cancer. Methods: The sampling was purposive and the data collected through in-depth interviews. Data collection occurred over an eleven month. The research was grounded in the premises of symbolic interactionism and adopted the methods of grounded theory. Twenty four participants who were patients receiving cancer treatment were recruited from two health care settings in Taiwan. Results: The study findings suggest that perceptions of health and illness are mediated through ongoing interactions with different forms of therapy. The participants in this study had a clear focus on “process and patterns of using TCM and Western medicine”. Further, ‘different importance in Western medicine and TCM’, ‘taken for granted to use TCM’, ‘each has specialized skills in Western medicine and TCM’ and ‘different symptoms use different approaches (Western medicine or TCM)’ may explicit how the participants in this study see CAM and Western medicine. Conclusions/Implications for practice: The descriptive frame of the study suggests that TCM and Western medicine occupy quite distinct domains in terms of decision making over their use. People used TCM based on interpretations of the present and against a background of an enduring cultural legacy grounded in Chinese philosophical beliefs about health and healthcare. The increasingly popular term of 'integrative medicine' obscures the complex contexts of the patterns of use of both therapeutic modalities. It is this latter point that is worthy of further exploration.

Seed-based biclustering of gene expression data

Background Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner. Methods In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns: (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty. Conclusions This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Retrieving information from microblog using pattern mining and relevance feedback

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

Maximal structuring of acyclic process models

This article addresses the transformation of a process model with an arbitrary topology into an equivalent structured process model. In particular, this article studies the subclass of process models that have no equivalent well-structured representation but which, nevertheless, can be partially structured into their maximally-structured representation. The transformations are performed under a behavioral equivalence notion that preserves the observed concurrency of tasks in equivalent process models. The article gives a full characterization of the subclass of acyclic process models that have no equivalent well-structured representation, but do have an equivalent maximally-structured one, as well as proposes a complete structuring method. Together with our previous results, this article completes the solution of the process model structuring problem for the class of acyclic process models.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Re-melting of Bi-2212/Ag laminated tapes by partial melting process

Bi-2212 tapes are prepared by a combination of dip-coating and partial melt processing. We investigate the effect of re-melting of those tapes by partial melting followed by slow cooling on the structure and superconducting properties. Microstructural studies of re-melted samples show that they have the same overall composition as partially melted tapes. However, the fractional volumes of the secondary phases differ and the amounts and distribution of the secondary phases have a significant effect on the critical current. Critical current of Bi-2212/Ag tapes strongly depends on the maximum processing temperature. Initial J(c)'s of the tapes, which are partially melted, then slowly solidified at optimum conditions and finally post-annealed in an inert atmosphere, are up to 10.4 x 10(3) A/cm(2). It is found that the maximum processing temperature at initial partial melting has an influence on the optimum re-heat treatment conditions for the tapes. Re-melted tapes processed at optimum conditions recover superconducting properties after post-annealing in an inert atmosphere: the J(c) values of the tapes are about 80-110% of initial J(c)'s of those tapes.

The process of remembering with The Forgotten Australians : digital storytelling and marginalized groups

Digital storytelling projects have proliferated in Australia since the early 2000s, and have been theorized as a means to disseminate the stories and voices of “ordinary” people. In this paper I examine through the case study of a 2009 digital storytelling project between the Australasian Centre for Interactive Design and a group identifying as Forgotten Australian whether digital storytelling in its predominant workshop-based format is able to meet the needs of profoundly marginalized and traumatized individuals and groups. For digital storytelling to be of use to marginalized groups as a means of communication or reflection a significant re-examination of the current approaches to its format, and its function needs to undertaken. This paper posits new ways of utilizing digital storytelling when dealing with trauma narratives.

Editorial : Learning, teaching and disseminating knowledge in business process management

Process-oriented thinking has become the major paradigm for managing companies and other organizations. The push for better processes has been even more intense due to rapidly evolving client needs, borderless global markets and innovations swiftly penetrating the market. Thus, education is decisive for successfully introducing and implementing Business Process Management (BPM) initiatives. However, BPM education has been an area of challenge. This special issue aims to provide current research on various aspects of BPM education. It is an initial effort for consolidating better practices, experiences and pedagogical outcomes founded with empirical evidence to contribute towards the three pillars of education: learning, teaching, and disseminating knowledge in BPM.

Chemical vapor deposited boron carbide : growth by a vapor-liquid-solid process

Detailed analytical electron microscope (AEM) studies of yellow whiskers produced by chemical vapor deposition (CVD)1 show that two basic types of whiskers are produced at low temperatures (between 1200°C and 1400°C) and low boron to carbon gas ratios. Both whisker types show planar microstructures such as twin planes and stacking faults oriented parallel to, or at a rhombohedral angle to, the growth direction. For both whisker types, the presence of droplet-like terminations containing both Si and Ni indicate that the growth process during CVD is via a vapor-liquid-solid (VLS) mechanism.

Collaborative 3D BPMN process modeller

A video detailing our new virtual world BPMN process modelling tool developed by Erik Poppe. Enables better situational awareness via use of remotely connected avatars and a shared 3D process diagram.

3D process visualisation configurations

Video detailing three process model visualisation configurations integrated into an agent driven virtual world simulation.

The Citizen’s Round Table process : canvassing public opinion on energy technologies to mitigate climate change

This study draws on communication accommodation theory, social identity theory and cognitive dissonance theory to drive a ‘Citizen’s Round Table’ process that engages community audiences on energy technologies and strategies that potentially mitigate climate change. The study examines the effectiveness of the process in determining the strategies that engage people in discussion. The process is designed to canvas participants’ perspectives and potential reactions to the array of renewable and non-renewable energy sources, in particular, underground storage of CO2. Ninety-five people (12 groups) participated in the process. Questionnaires were administered three times to identify changes in attitudes over time, and analysis of video, audio-transcripts and observer notes enabled an evaluation of level of engagement and communication among participants. The key findings of this study indicate that the public can be meaningfully engaged in discussion on the politically sensitive issue of CO2 capture and storage (CCS) and other low emission technologies. The round table process was critical to participants’ engagement and led to attitude change towards some methods of energy production. This study identifies a process that can be used successfully to explore community attitudes on politically-sensitive topics and encourages an examination of attitudes and potential attitude change.