This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 5
00 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.
The great interest in the field of knowledge discovery in relational databases has led to the development of mathematical algorithms that have proven effective in deriving knowledge of both descriptive and predictive types. However, the great develop
ment of information technology, and the wide spread of social networks and advanced web pages, increased the use of intent database systems.
In this work, we are proposing a new model for knowledge discovery in database (KDD) named "SCRUM-BI". It based on SCRUM agile methodology to enhance the way of building Business Intelligence and Data Mining applications. This model characterized as
more adaptive to the changing requirements, priorities and rapidly evolving business environments. SCRUM-BI Also improves and enhances the process of knowledge obtaining and sharing, which contributes to support strategic decision-making.
The model was validated using a case study on the telecommunications sector in Syria.
