Parvathaneni Revanth Rathan received his MS  Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Prof. Krishna Reddy. Here’s a summary of Parvathaneni Revanth Rathan’s thesis Discovering Popular Paths and Management of Billboard Ads by Mining User Trajectory Data as explained by him: 

The field of pattern mining has emerged to discover potentially useful hidden knowledge (information or patterns) from the voluminous data. The rapid development of data acquisition technologies like sensors contributes to the generation of massive spatial trajectory data from the users using a global positioning system in their mobile phones, cars, watches, and others. The trajectory of the user is defined as the sequence of locations of the user during the transit. Mining patterns from the user trajectory data is an active research area. Research efforts are being made to propose approaches to process user trajectory data and propose approaches for better travel path identification, transportation management, user-activity recognition. The Google Maps system is one of the useful application that exploits user trajectory data. In this thesis, we proposed a pattern mining based framework for computing the popular paths by analyzing the user trajectory data. Furthermore, we proposed a pattern mining framework to allocate the advertisements in billboards with the goal of improving the revenue of the billboard operator. First, we investigated the problem of finding the paths based on user preferences, which we designate as popular paths. Such user preferences may include roads with relatively high thoroughfare (i.e., better for safety), smoother roads with better infrastructure (e.g., fewer potholes), roads with more facilities or points of interest nearby, lighted roads as opposed to dark and unsafe streets, roads with relatively lower crime rates, roads with better scenic beauty and so on. In practice, given that users naturally follow some of these paths significantly more as compared to other paths, the popularity of a given path often reflects such user preferences. Moreover, users typically prefer diverse paths over similar paths for gaining flexibility in path selection. Given a set of user traversals in a road network and a set of paths between a given source and destination pair, we address the problem of performing top-k ranking of the paths based on both path popularity and path diversity. We proposed a model to compute the popularity score of the given path. Given a road network, a set of user traversals, and the source and destination pair, we have proposed an efficient framework by extending the transactional framework and frequent pattern mining technique for computing the popularity score of the given path between the source and destination pair. We conducted an extensive performance evaluation with two real datasets to demonstrate the effectiveness of the proposed approach. Second, we have proposed an efficient framework to improve the billboard operator’s revenue in the billboard advertisement scenario based on the user traversal data. A billboard operator manages the ad slots of a set of billboards. Normally, a user traversal is exposed to multiple billboards. It is possible to improve the billboard operator’s revenue by meeting the advertising demands of an increased number of clients if the user encounters different ads on the billboards during the traversal. We introduce the problem of billboard advertisement allocation to clients for improving the billboard operator revenue. We have proposed an improved billboard ad allocation scheme by extending the transactional framework and coverage pattern mining approach. We conducted a performance evaluation with a real dataset to demonstrate the effectiveness of the proposed scheme. Overall, we have proposed improved approaches to have better travel experience and improve the revenue of the billboard operator. We have demonstrated the effectiveness of the proposed schemes by conducting experiments on real datasets.