• Sohan Bhattacharya
• Sukanta Bhattacharya

The exponential growth of digital data has intensified the need for efficient and scalable search algorithms capable of handling large-scale datasets. We propose the K-Segmented Search Algorithm (KSSA), a novel approach that partitions the search space into K logical segments, enabling parallel or distributed processing and reducing search latency. Experimental results show that KSSA outperforms traditional algorithms in both time complexity and memory usage for terabyte-scale datasets.

K-Segmented Search Algorithm, Data Retrieval, Search Optimization, Time Complexity, Ordered Data, Multi-Pointer Search

"K-Segmented Search: A Novel Multi-Pointer Algorithm for Large-Scale Ordered Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 10, page no.b387-b396, October-2025, Available :http://www.jetir.org/papers/JETIR2510154.pdf

"K-Segmented Search: A Novel Multi-Pointer Algorithm for Large-Scale Ordered Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 10, page no. ppb387-b396, October-2025, Available at : http://www.jetir.org/papers/JETIR2510154.pdf