Prof. P Krishna Reddy and his students Preetham Sathineni, A Srinivas Reddy and Anirban Mondal published a paper on An Efficient Distributed Coverage Pattern Mining Algorithm at the 9th International Conference on Big Data Analytics (BDA-2021), which was organized at Indian Institute Of Information Technology Allahabad (IIITA), Prayagraj  from 15 – 18 December. Research work as explained by the authors:

Mining of coverage patterns from transactional databases is one of the data mining tasks. It has applications in banner advertising, search engine advertising and visibility computation. In general, most real-world transactional databases are typically large. Mining of coverage patterns from large transactional databases such as query log transactions on a single computer is challenging and time-consuming. In this paper, we propose Distributed Coverage Pattern Mining (DCPM) approach. In this approach, we employ a notion of the summarized form of Inverse Transactional Database (ITD) and replicate it at every node. We also employ an efficient clustering-based method to distribute the computational load of extracting coverage patterns among the Worker nodes. We performed extensive experiments using two real-world datasets and one synthetic dataset. The results show that the proposed approach significantly improves the performance over the state-of-the-art approaches in terms of execution time and data shuffled.

Conference page: http://bda2021.org/