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One of the hot issues in many organization systems is how to transform large amounts of daily collected operational data into the useful knowledge from the perspective of declared company goals and expected business values. The main concerns of this invited session are Data Science (DS) and Digital Transformation (DT) paradigms, as a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information, knowledge, and value. Various interdisciplinary oriented DS and DT approaches may provide organizations the ability to use their data to improve quality of business, increase financial efficiency and operational effectiveness, conduct innovative research and satisfy regulatory requirements. Applications of appropriate DS and DS implementation methodologies together with outcomes related to collaborative and interdisciplinary approaches are inevitable when applying DT approaches to large and complex organization systems. For many years, such interdisciplinary approaches were used in analyzing big data gathered from not only business sectors, but also public, non-profit, and government sectors.
The main goal of the session is to attract researchers from all over the world who will present their contributions, interdisciplinary approaches or case studies in the area of DS and DT. The focus in Data Science may be set to various aspects, such as: data warehousing, reporting, online analytical processing, data analytics, data mining, process mining, text mining, predictive analytics and prescriptive analytics, as well as various aspects of machine learning, big data and time series analysis. We express an interest in gathering scientists and practitioners interested in applying DS and DT approaches in public and government sectors, such as healthcare, education, or security services. However, experts from all sectors are welcomed. |
