Examination of relative efficacy in computational search techniques

Abstract

In communication and transaction systems, searching is a crucial procedure, and there are several techniques available to maximize data retrieval. These include the well-known techniques for effective searching in sorted datasets: Hash Search, Ternary Search, Exponential Search, and Interpolation Search. By putting these four algorithms into practice in Python and evaluating their search effectiveness on artificial datasets made up of sorted arrays of distinct random integers with values ranging from 1 to , where n varies from 1,000 to 1,000,000 items, this study evaluates the performance of these algorithms.  calculated average search durations for existing elements across 100 queries using a pseudo-process technique with timed code execution. Because they are deterministic exact-match searches, all algorithms achieve  accuracy, precision, and recall, and performance data include average search time (in seconds). According to the results, Hash Search performs best with near-constant times of roughly  microseconds, followed by Interpolation Search at 1-2 microseconds, and Ternary and Exponential Searches, which increase with dataset size and vary from 1-4 microseconds. Though newer deep learning techniques, such as learned indexes, may predict data distributions and offer  speedups and 10–100x size reductions in specific database contexts, these classic strategies are still effective for broad use.