P Shiridi Kumar working with Prof. P Krishna Reddy presented a paper on A Two-Level Indexing Scheme for Extracting Frequent Patterns with GPUs from Symbolic Sequence Data at the 13th International Conference on Big Data & AI (BDA 2025) held at  IIIT Bangalore from 17 to 20 July. Here is the summary of the paper as explained by the authors P Shiridi Kumar, Uday Kiran Rage and Prof.  P Krishna Reddy: 

Symbolic sequence data is generated from applications such as human activity recognition, financial modeling, intrusion detection systems, and drug discovery tasks. Extracting frequent patterns from symbolic data is an important research problem. In the literature, several CPU-based approaches have been proposed for pattern extraction from symbolic sequence data. However, due to their sequential execution, CPU-based approaches suffer from performance issues in extracting patterns from large sequence data. In modern computing infrastructures, GPUs are playing a central role in carrying out high-performance computing tasks due to their parallel design. Consequently, researchers have developed GPU-based data mining techniques for tasks such as pattern mining, clustering, and classification. In this paper, we propose a two-level indexing scheme that utilizes extra auxiliary memory to facilitate the efficient extraction of frequent patterns by exploiting the parallel processing capabilities of GPUs. Experimental results on three real-world datasets demonstrate that the proposed approach achieves substantial runtime improvements approximately 4× speedup over traditional CPU-based algorithms for smaller support values (e.g., 5), and more than 20× speedup for support thresholds greater than 10—when applied to large symbolic sequence data, outperforming both CPU-based and naive GPU-based methods.

As steering committee chair, Prof. Krishna Reddy participated in the steering committee meeting held during the 13th International Conference on Big Data & AI (BDA 2025).

July 2025