AceMap: A Novel Approach towards Displaying Relationship among Academic Literatures

A large number of papers are being published every year, which makes it difficult for researchers to grasp the relationship among the scientific literatures and the big picture of academic fields. The new challenges have thus been raised, such as analyzing the complicated citation and author network, mining valuable scientific knowledge, and visualizing big scholarly data. The existing academic systems, such as Google Scholar and DBLP have mainly adopted text-based methods, while some other systems make attempts to better navigate the literatures, for example, AMiner and Science Navigation Map. Although these systems show improvements, they fail to present the academic data in a holistic way, and also have limited functions. Therefore, we need to develop new tools which can realize more modules and further explore the academic literatures. In this paper, we conceptualize and design a novel academic system, AceMap, to analyze the big scholarly data and present the results through a "map" approach. AceMap integrates several algorithms in the field of network analysis and data mining, and then displays the information in a clear and intuitive way, aiming to help the researchers facilitate their work. After describing the big picture, we present achieved results and our work in progress. By far, AceMap has implemented the following functions: dynamic citation network display, paper clustering, academic genealogy, author and conference homepage, etc. We have also designed and performed distributed network analysis algorithms in a cutting-edge Spark system and utilized modern visualization tools to present the results. Finally, we conclude our paper by proposing the future outlooks.