Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


July Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the July 2021 Edition of the journal. The last date of research paper submission is June 15, 2021.

Online Analytical Processing on Hadoop using Apache Kylin

Supriya Vitthal Ranawade, Shivani Navale, Akshay Dhamal, Kuldeep Deshpande, Chandrashekhar Ghuge. Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Supriya Vitthal Ranawade, Shivani Navale, Akshay Dhamal, Kuldeep Deshpande, Chandrashekhar Ghuge
Download full text
  1. Supriya Vitthal Ranawade, Shivani Navale, Akshay Dhamal, Kuldeep Deshpande and Chandrashekhar Ghuge. Online Analytical Processing on Hadoop using Apache Kylin. International Journal of Applied Information Systems 12(2):1-5, May 2017. URL, DOI BibTeX

    	author = "Supriya Vitthal Ranawade and Shivani Navale and Akshay Dhamal and Kuldeep Deshpande and Chandrashekhar Ghuge",
    	title = "Online Analytical Processing on Hadoop using Apache Kylin",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "May 2017",
    	volume = 12,
    	number = 2,
    	month = "May",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "1-5",
    	url = "",
    	doi = "10.5120/ijais2017451682",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


In the Big Data age, it is necessary to remodel the traditional data warehousing and Online Analytical Processing (OLAP) system. Many challenges are posed to the traditional platforms due to the ever increasing data. In this paper, we have proposed a system which overcomes the challenges and is also beneficial to the business organizations. The proposed system mainly focuses on OLAP on Hadoop platform using Apache Kylin. Kylin is an OLAP engine which builds OLAP cubes from the data present in hive and stores the cubes in HBase for further analysis. The cubes stored in HBase are analyzed by firing SQL-like analytics queries. Also, reports and dashboards are further generated on the underlying cubes that provides powerful insights of the company data. This helps the business users to take decisions that are profitable to the organization. The proposed system is a boon to small as well as large scale business organizations. The aim of the paper is to present a system which builds OLAP cubes on Hadoop and generate insightful reports for business users.


  1. Kuldeep Deshpande, and Dr. Bhimappa Desai “Limitations of datawarehouse platforms and Assessment of Hadoop as an alternative”, IJITMIS, Volume 5, Issue 2, pp. 51-58, 2014.
  2. Shruti Tekadpande, Leena Deshpande “Analysis and Design of ETL process using Hadoop”, IJEIT, Volume 4, Issue 12, pp. 171-174 2015.
  3. T.K.Das and Arati Mohapatro, “A Study on Big Data Integration with Data Warehouse”, International Journal of Computer Trends and Technology (IJCTT) – volume 9, number 4, Mar 2014.
  4. Merv Adrian and Colin White, “Analytic Platforms: Beyond the Traditional Data Warehouse”, BeyeNETWORK Custom Research Report, 2010.
  5. Clark Bradley, Ralph Hollingshead, Scott Kraus, Jason Lefler, Roshan Taheri, “Data Modeling Considerations in Hadoop and Hive”, Technical paper, SAS, 2013.
  6. Bo Wang, Hao Gui, Mark Roantree, Martin F. O’Connor, “Data Cube Computational Model with Hadoop MapReduce”, WEBIST, 2014.
  7. Marissa Rae Hollingsworth, Hadoop and Hive as Scalable Alternatives to RDBMS: A Case Study, Boise State University, 2012.
  8. Jie Song, Chaopeng Guo, Zhi Wang, Yichan Zhang, Ge Yu, et al.. “HaoLap: a Hadoop based OLAP system for big data”, Journal of Systems and Software, Elsevier, 2015, vol. 102, pp.167-181.
  9. Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J.M., and Welton, C. MAD Skills: New Analysis Practices for Big Data. PVLDB 2(2), 2009.
  10. Yongqiang He et all “RCFile: A Fast and Space-efficient Data Placement Structure in MapReduce-based Warehouse Systems”, ICDE 2011.
  11. Philip Russom, Next generation Datawarehouse platforms, The Datawarehousing Institute, 2009
  12. Ashish Thusoo, Joydeep Sen Sarma, Amit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu and Raghotham Murthy, Hive – A Petabyte Scale Data Warehouse Using Hadoop, IEEE, 2010.
  13. Alfredo Cuzzocrea, Il-Yeol Song, Ladjel Bellatreche, “Data Warehousing and OLAP over Big Data: Current challenges and future research directions”, DOLAP '13 Proceedings of the sixteenth international workshop on Data warehousing and OLAP, Pages 67-70.
  14. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., and Pirahesh, H. Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-Tab, and Sub Totals. Data Mining and Knowledge Discovery 1(1), 1997.
  15. Tian, X., 2008. Large-scale SMS messages mining based on map-reduce, IEEE International Symposium on Computational Intelligence and Design. Piscataway, NJ, USA, October 17–18, 2008, pp. 7–12.


Analytics, OLAP, Hadoop, Kylin, Hive, Datawarehouse