Sonia Khetarpaul

Bio: Sonia Khetarpaul is an academic researcher from Shiv Nadar University. The author has contributed to research in topic(s): Global warming & Social relation. The author has an hindex of 4, co-authored 15 publication(s) receiving 72 citation(s). Previous affiliations of Sonia Khetarpaul include Indian Institute of Technology Delhi & Indian Institutes of Technology.

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.

Bus Arrival Time Prediction Using a Modified Amalgamation of Fuzzy Clustering and Neural Network on Spatio-Temporal Data

Abstract: This paper presents a dynamic model that can provide prediction for the estimated arrival time of a bus at a given bus stop using Global Positioning System (GPS) data. The proposed model is a hybrid intelligent system combining Fuzzy Logic and Neural Networks. While Neural Networks are good at recognizing patterns and predicting, they are not good at explaining how they decide their input parameters. Fuzzy Logic systems, on the other hand, can reason with imprecise information, but require linguistic rules to explain their fuzzy outputs. Thus combining both helps in countering each other’s limitations and a reliable and effective prediction system can be developed. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions. The accuracy of result is 86.293% obtained

Mining GPS traces to recommend common meeting points

Abstract: Scheduling a meeting is a difficult task for people who have overbooked calendars and many constraints. The complexity increases when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying travel patterns. In this paper, we present a solution that identifies a common meeting point for a group of users who have temporal and spatial locality constraints that vary over time. The problem entails answering an Optimal Meeting Point (OMP) query in spatial databases. Under Euclidean space OMP query solution identification gets reduced to the problem of determining the geometric median of a set of points, a problem for which no exact solution exists. The OMP problem does not consider any constraints as far as availability of users is concerned whereas that is a key constraint in our setting. We therefore focus on finding a solution that uses daily movements information obtained from GPS traces for each user to compute stay points during various times of the day. We then determine interesting locations by analyzing the stay points across multiple users. The novelty of our solution is that the computations are done within the database by using various relational algebra operations in combination with statistical operations on the GPS trajectory data. This makes our solution scalable to larger groups of users and for multiple such requests. Once this list of stay points and interesting locations are obtained, we show that this data can be utilized to construct spatio-temporal graphs for the users that allow us efficiently decide a meeting place. We perform experiments on a real-world dataset and show that our method is effective in finding an optimal meeting point between two users.

Spatiotemporal social (STS) data model: correlating social networks and spatiotemporal data

Abstract: A location-based social network is a network representation of social relations among actors, which not only allow them to connect to other users/friends but also they can share and access their physical locations. Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. This paper aimed to capture this spatiotemporal social network (STS) data of location-based social networks and model it. In this paper, we propose a STS data model which captures both non-spatial and spatial properties of moving users, connected on social network. In our model, we define data types and operations that make querying spatiotemporal social network data easy and efficient. We extend spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014) for social networks. The data model infers individual’s location history and helps in querying social network users for their spatiotemporal locations, social links, influences, their common interests, behavior, activities, etc. We show the some results of applying our data model on a spatiotemporal dataset (GeoLife) and two large real-life spatiotemporal social network datasets (Gowalla, Brightkite) collected over a period of two years. We apply the proposed model to determine interesting locations in the city and correlate the impact of social network relationships on the spatiotemporal behavior of the users.

Analyzing Travel Patterns for Scheduling in a Dynamic Environment

Abstract: Scheduling a meeting is a difficult task for people who have overbooked calendars and many constraints. This activity becomes further complex when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying traveling patterns. We extend the work of previous authors in this domain by incorporating some real life constraints (varying travel patterns, flexible meeting point and considering road network distance). We also generalize the problem by considering variable number of users. The previous work does not consider these dimensions. The search space for optimal meeting point is reduced by considering convex hull of the set of users locations. It can be further pruned by considering other factors, e.g., direction of movement of users. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions.

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.

Using global positioning systems to study health-related mobility and participation.

Abstract: Community participation, as indicated by mobility and engagement in socially meaningful activities, is a central component of health based on the International Classification of Health, Functioning, and Disease (WHO, 2001). Global positioning systems (GPS) technology is emerging as a tool for tracking mobility and participation in health and disability-related research. This paper fills a gap in the literature and provides a thorough description of a method that can be used to generate a number of different variables related to the constructs of mobility and participation from GPS data. Here, these variables are generated with the help of ST-DBSCAN, a spatiotemporal data mining algorithm. The variables include the number of unique destinations, activity space area, distance traveled, time in transit, and time at destinations. Data obtained from five individuals with psychiatric disabilities who carried GPS-enabled cell phones for two weeks are presented. Within- and across- individual variability on these constructs was observed. Given the feasibility of gathering data with GPS, larger scale studies of mobility and participation employing this method are warranted.

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.

Location prediction and mobility modelling for enhanced localization solution

Abstract: Current and future mobile applications massively exploit the knowledge of the user’s location to improve the offered services. However, user localization is by far one of the oldest and most difficult issues, due to its dynamism and to unavailability of some technologies in indoor environments. The enhanced localization solution (ELS) proposed in this paper is an innovative self adaptive solution that smartly combines standard location tracking techniques (e.g., GPS, GSM and WiFi localization), newly built-in technologies, as well as human mobility modelling and machine learning techniques. The main purposes of this solution are: to reduce the impact the service has, on the mobile device resources usage (mainly the battery consumption), when it is asked to provide a continuous localization; to help in preserving the privacy of the user, by running the whole system on the mobile device, without relying on a back-end server; and furthermore, to offer an ubiquitous coverage. The aspects mainly explored in this paper are: location prediction and mobility modelling, required to optimally estimate the current location with ELS. We are finding that people tend to move, for most of the time, among a limited set of places and that this can be modelled with a user prediction graph, which is further used to predict the next movement. Performing experiments on real users data, we show that the proposed prediction and the mobility model method of ELS are able to successfully predict the next location, even if we do not account for time features.

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