Using natural user interfaces for collaborative process modelling in virtual environments

Modelling business processes for analysis or redesign usually requires the collaboration of many stakeholders. These stakeholders may be spread across locations or even companies, making co-located collaboration costly and difficult to organize. Modern process modelling technologies support remote collaboration but lack support for visual cues used in co-located collaboration. Previously we presented a prototype 3D virtual world process modelling tool that supports a number of visual cues to facilitate remote collaborative process model creation and validation. However, the added complexity of having to navigate a virtual environment and using an avatar for communication made the tool difficult to use for novice users. We now present an evolved version of the technology that addresses these issues by providing natural user interfaces for non-verbal communication, navigation and model manipulation.

Second international workshop on theory and applications of process visualization

The representation of business process models has been a continuing research topic for many years now. However, many process model representations have not developed beyond minimally interactive 2D icon-based representations of directed graphs and networks, with little or no annotation for information overlays. In addition, very few of these representations have undergone a thorough analysis or design process with reference to psychological theories on data and process visualization. This dearth of visualization research, we believe, has led to problems with BPM uptake in some organizations, as the representations can be difficult for stakeholders to understand, and thus remains an open research question for the BPM community. In addition, business analysts and process modeling experts themselves need visual representations that are able to assist with key BPM life cycle tasks in the process of generating optimal solutions. With the rise of desktop computers and commodity mobile devices capable of supporting rich interactive 3D environments, we believe that much of the research performed in computer human interaction, virtual reality, games and interactive entertainment have much potential in areas of BPM; to engage, provide insight, and to promote collaboration amongst analysts and stakeholders alike. We believe this is a timely topic, with research emerging in a number of places around the globe, relevant to this workshop. This is the second TAProViz workshop being run at BPM. The intention this year is to consolidate on the results of last year's successful workshop by further developing this important topic, identifying the key research topics of interest to the BPM visualization community.

A demand-responsive decision support system for coal transportation

In this paper, a demand-responsive decision support system is proposed by integrating the operations of coal shipment, coal stockpiles and coal railing within a whole system. A generic and flexible scheduling optimisation methodology is developed to identify, represent, model, solve and analyse the coal transport problem in a standard and convenient way. As a result, the integrated train-stockpile-ship timetable is created and optimised for improving overall efficiency of coal transport system. A comprehensive sensitivity analysis based on extensive computational experiments is conducted to validate the proposed methodology. The mathematical proposition and proof are concluded as technical and insightful advices for industry practice. The proposed methodology provides better decision making on how to assign rail rolling-stocks and upgrade infrastructure in order to significantly improve capacity utilisation with the best resource-effectiveness ratio. The proposed decision support system with train-stockpile-ship scheduling optimisation techniques is promising to be applied in railway or mining industry, especially as a useful quantitative decision making tool on how to use more current rolling-stocks or whether to buy additional rolling-stocks for mining transportation.

Development route of the wind power industry in China

Wind power is one of the world's major renewable energy sources, and its utilization provides an important contribution in helping solve the energy problems of many countries. After nearly 40 years of development, China's wind power industry now not only manufactures its own massive six MW turbines but also has the largest capacity in the world with a national output of 50 million MW•h in 2010 and set to rise by eight times of that amount by 2020. This paper investigates this development route by analyzing relevant academic literature, statistics, laws and regulations, policies and research and industry reports. The main drivers of the development in the industry are identified as technologies, turbines, wind farm construction, pricing mechanism and government support systems, each of which is also divided into different stages with distinctive features. A systematic review of these aspects provides academics and practitioners with a better understanding of the history of the wind power industry in China and reasons for its rapid development with a view to enhancing progress in wind power development both in China and the world generally.

End-of-life care in the intensive care unit : a systematic review of qualitative studies investigating the experiences and perceptions of the patient's family

Review question/objective The review objective is to synthesise the best available evidence on experiences and perceptions of family members of intensive care unit patients on the adequacy of end-of-life care, where life-support modalities have been withheld or withdrawn. Inclusion criteria Types of participants This review will consider studies that report on the experiences and perceptions of patients’ families on EOLC in the ICU, where life-support modalities have been withheld or withdrawn. The family is defined as “those who are closest to the patient... the family may include the biological family, family by acquisition, and the family of choice and friends”. Phenomena of interest The phenomena of interest for this review are the patients’ families experiences, perceptions or views on the adequacy of EOLC delivered in the ICU, where life-support modalities were withheld or withdrawn. These experiences may refer to the following views on domains of care considered important at the end-of-life in the ICU, which have been described already in the existing literature: timely, consistent, and compassionate communication, clinician availability, clinical decision making based on patients’ preferences, goals and values, physical care implemented to maintain patient comfort, holistic interdisciplinary care and bereavement care for families of patients who died.

Can the use of quality assurance tools reduce the impact of surgical complications on the well-being of obstetricians and gynaecologists in Australia and New Zealand?

Background While adverse events primarily affect the patient, surgeons involved can also experience considerable distress. Aims The aim of the survey was to assess the impact of complications on the day-to-day life, work and health of Australian and New Zealand obstetricians and gynaecologists and to evaluate existing support systems and coping strategies. Methods A 43-question survey on self-assessment, quality assurance (QA) tools, impact of complications on individuals’ health and relationships, and support available was emailed to fellows, trainees, subspecialists and subspecialty trainees of the Royal Australian and New Zealand College of Obstetrics and Gynaecology (RANZCOG). We collected 606 responses from a target population of 2296 (response rate 26.3%). Results When complications occur, sleep was affected of 80%, family and social relationships of 55% and physical health of 48% of respondents. The major sources of support were from colleagues (83%), family (82%) and medical defence organisations (73%), with professional bodies perceived as providing least support. Nearly 80% of respondents felt the need to talk to someone they trust during times of complications. Overall, 100% of respondents used at least one QA tool (62% used two, 26% three and 9% four QA strategies). There were significant differences between respondent groups in use of the QA tools. Conclusions Surgical complications have a significant impact on the well-being of Australian and New Zealand obstetricians and gynaecologists. Existing support comes from colleagues and family, but structured, unbiased support for surgeons from a professional source is urgently warranted.

Diode-clamped three-level inverter-based battery/supercapacitor direct integration scheme for renewable energy systems

This paper describes a diode-clamped three-level inverter-based battery/supercapacitor direct integration scheme for renewable energy systems. The study is carried out for three different cases. In the first case, one of the two dc-link capacitors of the inverter is replaced by a battery bank and the other by a supercapacitor bank. In the second case, dc-link capacitors are replaced by two battery banks. In the third case, ordinary dc-link capacitors are replaced by two supercapacitor banks. The first system is supposed to mitigate both long-term and short-term power fluctuations while the last two systems are intended for smoothening long-term and short-term power fluctuations, respectively. These topologies eliminate the need for interfacing dc-dc converters and thus considerably improve the overall system efficiency. The major issue in aforementioned systems is the unavoidable imbalance in dc-link voltages. An analysis on the effects of unbalance and a space vector modulation method, which can produce undistorted current even in the presence of such unbalances, are presented in this paper. Furthermore, small vector selection-based power sharing and state of charge balancing techniques are proposed. Experimental results, obtained from a laboratory prototype, are presented to verify the efficacy of the proposed modulation and control techniques.

Utilizing voting systems for ranking user tweets

Twitter is a very popular social network website that allows users to publish short posts called tweets. Users in Twitter can follow other users, called followees. A user can see the posts of his followees on his Twitter profile home page. An information overload problem arose, with the increase of the number of followees, related to the number of tweets available in the user page. Twitter, similar to other social network websites, attempts to elevate the tweets the user is expected to be interested in to increase overall user engagement. However, Twitter still uses the chronological order to rank the tweets. The tweets ranking problem was addressed in many current researches. A sub-problem of this problem is to rank the tweets for a single followee. In this paper we represent the tweets using several features and then we propose to use a weighted version of the famous voting system Borda-Count (BC) to combine several ranked lists into one. A gradient descent method and collaborative filtering method are employed to learn the optimal weights. We also employ the Baldwin voting system for blending features (or predictors). Finally we use the greedy feature selection algorithm to select the best combination of features to ensure the best results.

Item reputation-aware recommender systems

Recommender systems provide personalized advice for customers online based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems use users’ ratings to generate their output. In this paper, we propose to combine reputation models with recommender systems to enhance the accuracy of recommendations. The main contributions include two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively, and a personalized reputation method to generate item reputations based on users’ interests. The proposed merging methods can be applicable to any recommendation methods and reputation methods, i.e., they are independent from generating recommendation scores and reputation scores. The experiments we conducted showed that the proposed methods could enhance the accuracy of existing recommender systems.

An investigation of problem-solving support and relaxation motivations on e-WOM

As a consequence of greater computer-mediated consumer-to-consumer communication within the firm's marketing communications, there has been a growing need to understand these digital interactions more explicitly. That is, we still know little about the exact extrinsic and intrinsic motivations that drive electronic word-of-mouth. The purpose of the paper is to better understand why members within community-based websites develop a need to exchange and/or develop a social bond within the community. Questionnaire data were gathered from 147 members of an online beauty forum in Australia. The findings highlight that those members seeking problem-solving support in combination with elements of relaxation will be more inclined to exchange with other community members and develop a social bond within that community. Marketing managers can capitalise these findings by strengthening problem-solving support systems and creating environments where community members can also relax and unwind to increase the exchange between members and also increase the social bonds within the community.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Competitive Smart Cities through Healthy Decision-making

This chapter examines the challenges and opportunities associated with planning for competitive, smart and healthy cities. The chapter is based on the assumptions that a healthy city is an important prerequisite for a competitive city and a fundamental outcome of smart cities. Thus, it is preeminent to understand the planning decision support system based on local determinants of health, economic and social factors. One of the major decision support systems is e-health and this chapter will focus on the role of e-health planning, by utilising web-based geographic decision support systems. The proposed novel decision support system would provide a powerful and effective platform for stakeholders to access online information for a better decision-making while empowering community participation. The chapter also highlights the need for a comprehensive conceptual framework to guide the decision process of planning for healthy cities in association with opportunities and limitations. In summary, this chapter provides the critical insights of using information science-based framework and suggest online decision support methods, as part of a broader e-health approach for creating a healthy, competitive and smart city.

The YOU CALL-WE CALL randomized clinical trial: Impact of a multimodal support intervention after a mild stroke

Background Comparison of a multimodal intervention WE CALL (study initiated phone support/information provision) versus a passive intervention YOU CALL (participant can contact a resource person) in individuals with first mild stroke. Methods and Results This study is a single-blinded randomized clinical trial. Primary outcome includes unplanned use of health services (participant diaries) for adverse events and quality of life (Euroquol-5D, Quality of Life Index). Secondary outcomes include planned use of health services (diaries), mood (Beck Depression Inventory II), and participation (Assessment of Life Habits [LIFE-H]). Blind assessments were done at baseline, 6, and 12 months. A mixed model approach for statistical analysis on an intention-to-treat basis was used where the group factor was intervention type and occasion factor time, with a significance level of 0.01. We enrolled 186 patients (WE=92; YOU=94) with a mean age of 62.5±12.5 years, and 42.5% were women. No significant differences were seen between groups at 6 months for any outcomes with both groups improving from baseline on all measures (effect sizes ranged from 0.25 to 0.7). The only significant change for both groups from 6 months to 1 year (n=139) was in the social domains of the LIFE-H (increment in score, 0.4/9±1.3 [95% confidence interval, 0.1–0.7]; effect size, 0.3). Qualitatively, the WE CALL intervention was perceived as reassuring, increased insight, and problem solving while decreasing anxiety. Only 6 of 94 (6.4%) YOU CALL participants availed themselves of the intervention. Conclusions Although the 2 groups improved equally over time, WE CALL intervention was perceived as helpful, whereas YOU CALL intervention was not used.

Revising history for cost-informed process improvement

Organisations are constantly seeking new ways to improve operational efficiencies. This study investigates a novel way to identify potential efficiency gains in business operations by observing how they were carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how these trade-offs can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A number of optimisation techniques are proposed to explore and assess alternative execution scenarios. The objective function is represented by a cost structure that captures different process dimensions. An experimental evaluation is conducted to analyse the performance and scalability of the optimisation techniques: integer linear programming (ILP), hill climbing, tabu search, and our earlier proposed hybrid genetic algorithm approach. The findings demonstrate that the hybrid genetic algorithm is scalable and performs better compared to other techniques. Moreover, we argue that the use of ILP is unrealistic in this setup and cannot handle complex cost functions such as the ones we propose. Finally, we show how cost-related insights can be gained from improved execution scenarios and how these can be utilised to put forward recommendations for reducing process-related cost and overhead within organisations.

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.