In the recent years, the Agile Kanban has emerged as an appropriate method used for
managing projects in numerous fields and various settings. Despite getting an increased …

There is an increasing number of big data science projects aiming to create value for
organizations by improving decision making, streamlining costs or enhancing business …

In an era of digital transformation, where in the last decades has increased the need of
embrace change rapidly, it's important that organizations make their transformation in the …

This paper first explores the concept of a lean project and defines four principles team
should follow to achieve lean data science. It then describes a new team process framework …

This paper presents an organizational framework for measuring institutional stakeholder
engagement for big data science teams toward cyberinfrastructure (CI) diffusion. CI projects …

Image quality is important, and can affect overall performance in image processing and
computer vision as well as for numerous other reasons. Image quality assessment (IQA) is …

The emerging discipline of Data Science poses several challenges for teams conducting
projects in the field as notably, the majority of Data Science teams fail to deliver the expected …

The effective implementation of data science projects (DSP) relies heavily on risk
management, where understanding and managing potential risks are important factors …

During the last decade, agile software development methodologies have been widely
adopted in various project contexts. Big data projects are different from software engineering …

The effective implementation of data science projects (DSP) relies heavily on risk
management, where understanding and managing potential risks are important factors …