Researchers around the world always request a research article relevant to their topic that satisfies their information need. The academic research environment generates an excessive amount of data called big scholarly data. Scholarly data usually includes millions of raw data represented in authors, papers, citations, and publication venues as well as author's information and affiliation. The enormous amount of valuable data generated by academic research has attracted researchers to explore this problem domain using different methodologies. Finding the most important articles in the field is considered a critical issue for researchers and journals as well as academic institutions. Ranking systems have become a very popular topic in the academic environment due to their importance in hiring, promotions, grants and award procedures. An accurate ranking system leads to an efficient recommendation system. This paper describes the background for big scholarly data and technologies. It also reviews the most important ranking systems and their algorithms. Recommendation systems approaches are presented for academic research, and the characteristics of highly cited papers are highlighted to help researchers improve their paper citations. Finally, this paper introduces an overview of big scholarly data visualization techniques and existing tools.