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Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered system research, and enhance the replicability of evaluation…
Pipeline parallelism is one of the key components for large-scale distributed training, yet its efficiency suffers from pipeline bubbles which were deemed inevitable. In this work, we introduce a scheduling strategy that, to our knowledge,…
Elasticity is a form of self-adaptivity in cloud-based software systems that is typically restricted to the infrastructure layer and realized through auto-scaling. However, both reactive and proactive forms of infrastructure auto-scaling…
Research in blockchain systems has mainly focused on improving security and bridging the performance gaps between blockchains and databases. Despite many promising results, we observe a worrying trend that the blockchain landscape is…
The real-time data collection and automation capabilities offered by the Internet of Things (IoT) are revolutionizing and transforming Business Processes (BPs) into IoT-enhanced BPs, showing high potential for improving sustainability.…
The rule technological landscape is becoming ever more complex, with an extended number of specifications and products. It is therefore becoming increasingly difficult to integrate rule-driven components and manage interoperability in…
Background, enhancing interoperability of bioinformatics knowledge bases is a high priority requirement to maximize data reusability, and thus increase their utility such as the return on investment for biomedical research. A knowledge base…
Elastic autoscaling is the fundamental mechanism that enables the cloud-based services to continually evolve themselves - through changing the related software configurations and hardware resource provisions - under time-varying workloads.…
Increasing reuse opportunities is a well-known problem for software designers as well as for hardware designers. Nonetheless, current software and hardware engineering practices have embraced different approaches to this problem. Software…
In the contemporary world of dynamic digital solutions and services, the significance of effective and stable cloud solutions cannot be overestimated. The cloud adaptation is becoming more popular due to mobile advantages, including…
Ubiquitous computing encapsulates the idea for technology to be interwoven into the fabric of everyday life. As computing blends into everyday physical artifacts, powerful opportunities open up for social connection. Prior connected media…
Machine Learning Operations (MLOps) has become increasingly critical as more organisations move ML models into production. However, the growing landscape of MLOps solutions has introduced complexity for practitioners trying to select…
The knowledge of the world is passed on through libraries. Accordingly, domain expertise and experiences should also be transferred within an enterprise by a knowledge base. Therefore, models are an established medium to describe good…
In large distributed systems, failures are a daily event occurring frequently, especially with growing numbers of computation tasks and locations on which they are deployed. The advantage of representing an application with a workflow is…
Workflow management systems allow the users to develop complex applications at a higher level, by orchestrating functional components without handling the implementation details. Although a wide range of workflow engines are developed in…
Cloud computing has been attracting the attention of several researchers both in the academia and the industry as it provides many opportunities for organizations by offering a range of computing services. For cloud computing to become…
Epidemiologists have a growing interest in employing computational approaches to solve analytic problems, with simulation being arguably the most accessible among all approaches. While previous literature discussed the utility of simulation…
Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by…
The combination between innovative topics and emerging technologies lets researchers define new processes and models. New needs regard the definition of modular and scalable approaches, with society and environment in mind. An important…
The most pressing problems in science are neither empirical nor theoretical, but infrastructural. Scientific practice is defined by coproductive, mutually reinforcing infrastructural deficits and incentive systems that everywhere constrain…