相关论文: Why Data Science Projects Fail

Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining…

The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution…

We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The…

Software development comprises complex tasks which are performed by humans. It involves problem solving, domain understanding and communication skills as well as knowledge of a broad variety of technologies, architectures, and solution…

Process science is a highly interdisciplinary field of research. Despite numerous proposals, process science lacks an adequate understanding of the core concepts of the field, including notions such as process, event, and system. A more…

Workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on…

This study describes a vision, how technology can help improving the efficiency in research. We propose a new clean-slate design, where more emphasis is given on the correctness and up-to-dateness of the scientific results, it is more open…

It is no secret that many projects fail, regardless of the business sector, software projects are notoriously disaster victims, not necessarily because of technological failure, but more often due to their uncertainties. The threats…

With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of various interrelated research communities -- have turned…

As our capacity to study ever-expanding domains of our science has increased (including the time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys in multiple wavebands with broad spatial coverage and…

Consider the situation where a data analyst wishes to carry out an analysis on a given dataset. It is widely recognized that most of the analyst's time will be taken up with \emph{data engineering} tasks such as acquiring, understanding,…

Increasingly common open data and open application programming interfaces (APIs) together with the progress of data science -- such as artificial intelligence (AI) and especially machine learning (ML) -- create opportunities to build novel…

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do…

Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives…

Scientific datasets play a crucial role in contemporary data-driven research, as they allow for the progress of science by facilitating the discovery of new patterns and phenomena. This mounting demand for empirical research raises…

Artificial intelligence (AI) governance is the body of standards and practices used to ensure that AI systems are deployed responsibly. Current AI governance approaches consist mainly of manual review and documentation processes. While such…

Science is essential to innovation and economic prosperity. Although studies have shown that national scientific development is affected by geographic, historic, and economic factors, it remains unclear whether there are universal…

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature…

Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft…

Small to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence…