Big data pipelines are becoming increasingly vital in a wide range of data intensive application domains such as digital healthcare, telecommunication, and manufacturing for efficiently processing data. Data pipelines in such domains are complex and dynamic and involve a number of data processing steps that are deployed on heterogeneous computing resources under the realm of the Edge-Cloud paradigm. The processes of testing and simulating big data pipelines on heterogeneous resources need to be able to accurately represent this complexity. However, since big data processing is heavily resource-intensive, it makes testing and simulation based on historical execution data impractical. In this paper, we introduce the SIM - PIPE Dry Runner approach - a dry run approach that deploys a big data pipeline step by step in an isolated environment and executes it with sample data; this approach could be used for testing big data pipelines and realising practical simulations using existing simulators.

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.

Project and task management tools aim to support remote or face-to-face collaboration. Despite the growing needs for these tools, little is known about how they are utilized in practice. This paper presents the results of an exploratory study using UpWave, a task management tool, and the ways that it enables team collaboration. The group interviewees utilize UpWave for their collaborations and report on its features in terms of use, best practices, motivations and rewards for users to encourage their collaboration. This paper concludes that project and task management tools offer new possibilities for collaborations; it also makes suggestions for using such tools in teams. This study's future work will include a mixed-methods approach to gain a greater understanding of the tools' effects in various collaboration settings.

The new software engineering initiative, Semat, is in the process of developing a kernel for software engineering that stands on a solid theoretical basis. So far, it has suggested a set of kernel elements for software engineering and basic language constructs for defining the elements and their usage. This paper describes a session during which Semat results and status will be presented. The presentation will be followed by a discussion panel.

One of the key challenges of the model-driven paradigm is to define, manage, and maintain traces and relationships between different models and model views, including the code of the system. In the COMBINE, DAIM and CAFE projects, we aim to define a framework supporting the model-driven paradigm. A main part of the framework is a metamodel-based transformation profile, which is aimed at supporting the definition of transformation schemes and being a baseline for transformation execution. Our primary objective is to assist a complete development lifecycle, from business models, via a requirements model, to architecture models, platform models and code, with a framework that helps us in a simple way to define transformations, mappings, and refinements. The framework should provide a usable notation that can be modelled with standard UML tools. The contribution of this paper is a baseline for such a framework.