Big scholarly data bring about new opportunities and challenges with respect to knowledge discovery, data mining, science of science, and education. It is imperative and vital for scholars to drive their knowledge towards the innovative generation of values from big scholarly data. New knowledge can be extracted by analyzing and mining big scholarly data to, e.g., better understand research dynamics, scientific collaboration and success, identify new directions of research, assess the quality of science, and enable personalized teaching and learning. To achieve these goals, however, a lot of challenges facing big scholarly data acquisition, storage, management, processing and usage must be addressed.
The BigScholar 2019 workshop aims at bringing together academics and practitioners from diverse fields to share ideas and experience with management, analysis, mining, and applications of big scholarly data. The goal is to contribute to the birth of a community having a shared interest around big scholarly data and exploring it using knowledge discovery, data science and analytics, network science, and other appropriate technologies.
This Article Collection will mainly feature as the proceedings of BigScholar 2019 (CIKM workshop), though it can also accept some previously-solicited submissions.
Big scholarly data bring about new opportunities and challenges with respect to knowledge discovery, data mining, science of science, and education. It is imperative and vital for scholars to drive their knowledge towards the innovative generation of values from big scholarly data. New knowledge can be extracted by analyzing and mining big scholarly data to, e.g., better understand research dynamics, scientific collaboration and success, identify new directions of research, assess the quality of science, and enable personalized teaching and learning. To achieve these goals, however, a lot of challenges facing big scholarly data acquisition, storage, management, processing and usage must be addressed.
The BigScholar 2019 workshop aims at bringing together academics and practitioners from diverse fields to share ideas and experience with management, analysis, mining, and applications of big scholarly data. The goal is to contribute to the birth of a community having a shared interest around big scholarly data and exploring it using knowledge discovery, data science and analytics, network science, and other appropriate technologies.
This Article Collection will mainly feature as the proceedings of BigScholar 2019 (CIKM workshop), though it can also accept some previously-solicited submissions.
