Time and Space Optimization in Linear Time Sorting Using LSORT

The requirement for faster computation is increasing day by day, and researchers are working hard to improve processing speed in different aspects. In real time, the data elements are collected in an unsorted manner. Unsorted-data processing takes more processing time. Thus, one of the most important steps in reducing processing times when dealing with big data sets is sorting. For sorting elements, various standard sorting algorithms are applied. Each algorithm takes a different amount of processing time and storage space. Linear time sorting is the most preferable sorting technique for larger data sets. Due to intermediate storage and process, the conventional counting sort takes high processing time and storage space for sorting the elements. Likewise, the intermediate array size depends on the value of the key element. Hence, in the proposed work without intermediate storage the elements are going to be sorted by using Lsort algorithm. Through the removal of intermediate storage the processing time and storage space is reduced in the proposed work. When compared to algorithms such as selection sort, bubble sort and merge sort, the proposed technique reducing time or space complexity. Similarly, the conventional sorting works well for positive numbers only not for negative and fractional numbers. Hence, the proposed algorithm is developed for processing integers with positive, negative and non-integers numbers also. This process improves time and space complexity and to adapt integers with positive, negative and non-integers numbers are all combined within a single algorithm. The proposed Lsort is compared with popular sorting techniques like merge sort, quick sort, heap sort and conventional counting sort for prove its efficiency for large range of numbers. For performance analysis is done for different size of data with number of active operations (swap), elapsed time comparison with other sorting techniques.