Dynamic construction of lattice of cuboids in data warehouse

References

• W.H. Inmon, “Building the data warehouse,” Johnwiley & sons, 2005.
   Google Scholar
• M.P. Deshpande, S. Agarwal, R. Agarwal, A. Gupta, J. F. Naughton, R. Ramakrishnan and S. Sarawagi, “On the computation of multidimensional aggregates”, Proc. of 1996 International Conference on Very Large Data Bases VLDB’96.
   Google Scholar
• V. Harinarayan, Rajaraman, A., Ullman, “Implementing Data Cubes Efficiently”, In ACM SIGMOD International Conference on Management of Data, ACM Press, New York (1996) pp. 205-216.
   Google Scholar
• I. Antoaneta, R Boris, “Multidimensional models-constructing data cube” Int. conference on computer systems and technologies-Comp-SysTech’2004, Volume 5 pp. 1-7.
   Google Scholar
• L.Y. Wen, K.I. Chung, “A genetic algorithm for OLAP data cubes” Knowledge and information systems, January 2004, volume 6, Issue1, pp 83-102.
   Google Scholar
• K. Aouiche, P. Jouve, and J. Darmont. “Clustering-based materialized view selection in data warehouses”, ADBIS’06, volume 4152 of LNCS, pages 81–95, 2006.
   Google Scholar
• S. Mann, A. Gosain, and S. Sabaharwal. “OO Approach for Developing Conceptual Model for A Data Warehouse.” Journal of Technology and Engineering Science“, volume 1 (2009) pp. 79-82.
   Google Scholar
• Wang, Yuxiang & Song, Aibo & Luo, Junzhou. (2010). A MapReduce-Merge-based Data Cube Construction Method. Proceedings - 9th International Conference on Grid and Cloud Computing, GCC 2010, pp. 1-6.
   Google Scholar
• Chaudhari, Manoj S., and Chandrashekhar Dhote. “Dynamic materialized view selection algorithm: a clustering approach”, International Conference on Data Engineering and Management”, pp. 57-66. Springer, Berlin, Heidelberg, 2010.
   Google Scholar
• Kumar, TV Vijay, and Santosh Kumar. “Materialized view selection using simulated annealing”, In International Conference on Big Data Analytics, pp. 168-179. Springer, Berlin, Heidelberg, 2012.
   Google Scholar
• Gosain, A., & Mann, S. (2012). An object-oriented multidimensional model for data warehouse. In Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies (Vol. 8350, p. 83500I).
   Google Scholar
• Gosain, Anjana, and Suman Mann. “Space and Time Analysis on the Lattice of Cuboid for Data Warehouse.” International Journal of Computer Applications, volume 77-No.3 (2013). doi: 10.5120/13378-0993
   Google Scholar
• Sen, Soumya, Santanu Roy, Anirban Sarkar, Nabendu Chaki, and Narayan C. Debnath. “Dynamic discovery of query path on the lattice of cuboids using hierarchical data granularity and storage hierarchy” Journal of Computational Science 5, no. 4 (2014) pp. 675-683. doi: 10.1016/j.jocs.2014.02.006
   Google Scholar
• Gosain, A., Mann, S. Empirical validation of metrics for object oriented multidimensional model for data warehouse. International Journal of System Assurance Engineering and Management, volume 5, pages262–275(2014).
   Google Scholar
• Gosain, A., Khatri, S.K. and Mann, S., 2014, October. Multidimensional modeling for data warehouse using object oriented approach. In Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization (pp. 1-6). IEEE.
   Google Scholar
• Djiroun, Rahma, et al. “A data cube design and construction methodology based on OLAP queries.” 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016.
   Google Scholar
• Gosain, Anjana. “Materialized cube selection using particle swarm optimization algorithm.” Procedia Computer Science 79 (2016), pp. 2-7. doi: 10.1016/j.procs.2016.03.002
   Google Scholar
• Kumar, TV Vijay, and Biri Arun. “Materialized view selection using HBMO”, International Journal of System Assurance Engineering and Management, (2017), pp. 379-392. doi: 10.1007/s13198-015-0356-4
   Web of Science ®Google Scholar
• Hua Chen (2018) “Personalized recommendation system of e-commerce based on big data analysis”, Journal of Interdisciplinary Mathematics, 21:5, 1243-1247. doi: 10.1080/09720502.2018.1495599
   Web of Science ®Google Scholar
• J. Vimala, VS. Anusuya Ilamathi & S. Rajareega (2019) “Implicative and positive implicative filters of residuated lattices in multiset context, Journal of Discrete Mathematical Sciences and Cryptography“, 22:5, 869-882. doi: 10.1080/09720529.2019.1685238
   Web of Science ®Google Scholar
• Rajendra Kumar, Sunil Kumar Khatri & Mario José Diván (2020) “Efficiency measurement of data centers: An elucidative review”, Journal of Discrete Mathematical Sciences and Cryptography, 23:1, 221-236. doi: 10.1080/09720529.2020.1721886
   Web of Science ®Google Scholar
• Kumar Rahul, Rohitash Kumar Banyal & Puneet Goswami (2020) “Analysis and processing aspects of data in big data applications”, Journal of Discrete Mathematical Sciences and Cryptography, 23:2, 385–393. doi: 10.1080/09720529.2020.1721869
   Web of Science ®Google Scholar