A survey on scholarly data: From big data perspective. Issue 4 (July 2017)

• Record Type: Journal Article
• Authors: Khan, Samiya; Liu, Xiufeng; Shakil, Kashish A.; Alam, Mansaf
• Is Part Of: Information processing & management. Volume 53:Issue 4(2017:Jul.)
• Page Start: 923
• Page End: 944
• Publication Date: 2017-07
• Subjects: Scholarly data -- Big data -- Cloud-based big data analytics -- Big scholarly data -- Big data platform -- Scholarly applications; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2017.03.006
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Survey of big scholarly data with respect to the different phases of the big data lifecycle. Identifies the different big data tools and technologies that can be used for development of scholarly applications. Investigates research challenges and limitations specific to big scholarly data and its applications. Provides research directions and paves way towards the development of a generic and comprehensive big scholarly data platform. Abstract: Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.