Rashmi Chauhan

Bio: Rashmi Chauhan is an academic researcher from Banasthali Vidyapith. The author has contributed to research in topics: Schema matching & Schema (genetic algorithms). The author has an hindex of 1, co-authored 2 publications receiving 44 citations.

Mining GPS data to determine interesting locations

Abstract: It is possible to obtain fine grained location information fairly easily using Global Positioning System (GPS) enabled devices. It becomes easy to track an individual's location and trace her trajectory using such devices. By aggregating this data and analyzing multiple users' trajectory a lot of useful information can be extracted. In this paper, we aim to analyze aggregate GPS information of multiple users to mine a list of interesting locations and rank them. By interesting locations we mean the geographical locations visited by several users. It can be an office, university, historical place, a good restaurant, a shopping complex, a stadium, etc. To achieve this various relational algebra operations and statistical operations are applied on the GPS trajectory data of multiple users. The end result is a ranked list of interesting locations. We show the results of applying our methods on a large real life GPS dataset of sixty two users collected over a period of two years.

SR-Match: A Novel Schema Matcher Based on Semantic Relationship

Abstract: In data integration, schema matching plays an important role Present schema matching tools combine various match algorithms, each employing a particular technique to improve matching accuracy However there is still no fully automatic tool is available and also there is lack of accuracy As a step in this direction, we have proposed a new and efficient Semantic-Relationship schema matching (SR-Match) approach which considers the semantic relationships as one of the parameters for matching Here in SR-Match, the initial mappings performed by the basic schema mapping techniques, acts as input to the relationship matcher Relationship matcher compares the remaining unmapped elements based on their semantic relationship with their parents It is observed that, if both semantics and relationships are taken into account, the degree of accuracy in matching results is improved

Availability, Reliability, and Security in Information Systems and HCI

Abstract: In a hospital, information exchange is essential to save lives and to prevent life-endangering mistakes. Information exchange is supported by a hospital information system (HIS). From a theoretical perspective, the deployment of an HIS is promising because it reduces errors and duplication of information. In practice, however, there are some major problems concerning the usage of such a system. One way to deal with these problems is introduced in this paper: the integration of sensor data into social media. The paper concentrates on the conceptual benefits and risks such an integration may generate. It focuses on the case of a patient room.

Inferring high-level behavior from low-level sensors

Abstract: We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.

Unsupervised Interesting Places Discovery in Location-Based Social Sensing

Abstract: This paper presents an unsupervised approach toaccurately discover interesting places in a city from location-basedsocial sensing applications, a new sensing applicationparadigm that collects observations of physical world fromLocation-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests(POI) recommendation systems, they used supervised learningapproaches that did not work for users who have little orno historic (training) data. In this paper, we focused onan interesting place discovery problem where the goal isto accurately discover the interesting places in a city thataverage people may have strong interests to visit (e.g., parks, museums, historic sites, etc.) using unsupervised approaches. Inparticular, we develop a new Physical-Social-aware InterestingPlace Discovery (PSIPD) scheme which jointly exploits thelocation's physical dependency and the visitor's social dependencyto solve the interesting place discovery problem using anunsupervised approach. We compare our solution with state-of-the-art baselines using two real world data traces from LBSN. The results showed that our approach achieved significantperformance improvements compared to all baselines in termsof both estimation accuracy and ranking performance.

Discovering socially important locations of social media users

Abstract: Socially important locations are places that are frequently visited by social media users in their social media life Discovering socially interesting, popular or important locations from a location based social network has recently become important for recommender systems, targeted advertisement applications, and urban planning, etc However, discovering socially important locations from a social network is challenging due to the data size and variety, spatial and temporal dimensions of the datasets, the need for developing computationally efficient approaches, and the difficulty of modeling human behavior In the literature, several studies are conducted for discovering socially important locations However, majority of these studies focused on discovering locations without considering historical data of social media users They focused on analysis of data of social groups without considering each user’s preferences in these groups In this study, we proposed a method and interest measures to discover socially important locations that consider historical user data and each user’s (individual’s) preferences The proposed algorithm was compared with a naive alternative using real-life Twitter dataset The results showed that the proposed algorithm outperforms the naive alternative