An Ontological Approach for Organizing a Knowledge Base to Share and Reuse Business Workflow Templates

International audienceBusiness process models have been used in a lot of enterprise applications. Along with their popularity, the problem of how to create them correctly in terms of semantics and syntax while effectively promoting the reuse of suitable parts of existing models is increasingly interest. This paper describes how to organize a knowledge base to facilitate the shareability and reusability of business workflow templates. We first introduce a repository consisting of business workflow templates which are well-checked at the syntactic and semantic level. An organizational mechanism for control flow-based business workflow templates is therefore provided to ensure an effective search of templates. We then propose a process for developing workflow templates. Thereby for each use case, users can select and modify suitable workflow templates from the knowledge base

Reflections on the Use of Social Networking Sites as an Interactive Tool for Data Dissemination in Digital Archaeology

Based on a case study, the paper analyses the possibilities of social media as a tool for science communication in the context of information and communication technology (ICT) usage in archaeology. Aside from discussing the characteristics of digital archaeology, the social networking sites (SNS) Twitter, Sketchfab, and ResearchGate are integrated into a digital research data dissemination tool. As a result, above-average engagement rates with few impressions were observed. Compared with that, status updates focusing on actual fieldwork and other research activities gain high numbers of impressions with below-average engagement rates. It is believed that most of the interactions are restricted to a core audience and that a clearly defined social media strategy is obligatory for successful research data dissemination in archaeology, combined with regular posts in the SNS. Additionally, active followers are of highest importance

Vision-based Detection of Mobile Device Use While Driving

The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance

The Gamification of Crowdsourcing Systems: Empirical Investigations and Design

Recent developments in modern information and communication technologies have spawned two rising phenomena, gamification and crowdsourcing, which are increasingly being combined into gamified crowdsourcing systems. While a growing number of organizations employ crowdsourcing as a way to outsource tasks related to the inventing, producing, funding, or distributing of their products and services to the crowd – a large group of people reachable via the internet – crowdsourcing initiatives become enriched with design features from games to motivate the crowd to participate in these efforts. From a practical perspective, this combination seems intuitively appealing, since using gamification in crowdsourcing systems promises to increase motivations, participation and output quality, as well as to replace traditionally used financial incentives. However, people in large groups all have individual interests and motivations, which makes it complex to design gamification approaches for crowds. Further, crowdsourcing systems exist in various forms and are used for various tasks and problems, thus requiring different incentive mechanisms for different crowdsourcing types. The lack of a coherent understanding of the different facets of gamified crowdsourcing systems and the lack of knowledge about the motivational and behavioral effects of applying various types of gamification features in different crowdsourcing systems inhibit us from designing solutions that harness gamification’s full potential. Further, previous research canonically uses competitive gamification, although crowdsourcing systems often strive to produce cooperative outcomes. However, the potentially relevant field of cooperative gamification has to date barely been explored. With a specific focus on these shortcomings, this dissertation presents several studies to advance the understanding of using gamification in crowdsourcing systems

The terminator : an AI-based framework to handle dependability threats in large-scale distributed systems

With the advent of resource-hungry applications such as scientific simulations and artificial intelligence (AI), the need for high-performance computing (HPC) infrastructure is becoming more pressing. HPC systems are typically characterised by the scale of the resources they possess, containing a large number of sophisticated HW components that are tightly integrated. This scale and design complexity inherently contribute to sources of uncertainties, i.e., there are dependability threats that perturb the system during application execution. During system execution, these HPC systems generate a massive amount of log messages that capture the health status of the various components. Several previous works have leveraged those systems’ logs for dependability purposes, such as failure prediction, with varying results. In this work, three novel AI-based techniques are proposed to address two major dependability problems, those of (i) error detection and (ii) failure prediction. The proposed error detection technique leverages the sentiments embedded in log messages in a novel way, making the approach HPC system-independent, i.e., the technique can be used to detect errors in any HPC system. On the other hand, two novel self-supervised transformer neural networks are developed for failure prediction, thereby obviating the need for labels, which are notoriously difficult to obtain in HPC systems. The first transformer technique, called Clairvoyant, accurately predicts the location of the failure, while the second technique, called Time Machine, extends Clairvoyant by also accurately predicting the lead time to failure (LTTF). Time Machine addresses the typical regression problem of LTTF as a novel multi-class classification problem, using a novel oversampling method for online time-based task training. Results from six real-world HPC clusters’ datasets show that our approaches significantly outperform the state-of-the-art methods on various metrics