Applying Johnson's Algorithm for Efficient Shortest Path Analysis in Big Data

In this paper, Johnson's Algorithm is employed for Big Data shortest route analysis. Its efficiency and efficacy in managing massive datasets to improve data processing and decision-making is studied. To solve big dataset's computing problems, Johnson's Algorithm, which efficiently finds shortest routes in networks with negative edge weights, is used. This method optimizes route analysis operations in Big Data settings to extract insights faster and more accurately. The study shows Johnson's Algorithm's viability and advantages in big data analytics, possibly enabling its use in network routing, transportation optimization, and social network analysis. Three cases are shown in Sample City Distance Matrix data. The initial Original Distance Matrix data was collected in a random sample of 5 cities and showed a 10–43 km range. In the second Reweighted Matrix with Potential Values instance, data from 5 random cities was collected to calculate the shortest route with a minimum and maximum km of 5–30 km. The data from the third Final Shortest Path Matrix in a random sample of 5 cities is relevant for transportation planning and network optimization with a minimum and maximum distance of -3 and -21 kilometers.