Sonia Khetarpaul

Bio: Sonia Khetarpaul is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 4, co-authored 15 publications receiving 72 citations. Previous affiliations of Sonia Khetarpaul include Indian Institute of Technology Delhi & Indian Institutes of Technology.

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.

Correlating Social Interconnections of Users with Spatio-Temporal Check-Ins Behavior

Abstract: Acquiring the knowledge about the relationship among friendship network properties and check-in behavior of users (connected in the friendship network) has several benefits such as planning advertising strategies and recommending the friends or places. This paper aims to find the impact of structural patterns hidden in the nodes of a friendship network and external environment changes in the check-in patterns of the users. First, we categorize each spatial check-in event based on its cause into either self reinforcing behavior or social influence or external effect. Then, we explore how network and spatial properties and external factors affect the number of check-ins and influences. Using check-ins data from four major cities/states and its users' friendship graph, we show how network and spatial properties like centrality, network neighborhood overlap, spatial check-ins overlap, strong ties effects the check-ins and influential behavior of individuals.

Mining location based social networks to understand the citizen’s check-in patterns

Abstract: Analyzing and understanding the movement patterns of the citizen’s with in a city, plays an important role in urban and transportation planning. Though many recent research papers focused on mining LBSN services data and performed in-depth analysis of users’ mobility patterns and their impact on their social inter-connections and friends. This paper focuses on understanding the Citizen’s movement patterns of socially interconnected users in friendship networks, by analyzing their spatial-temporal footprints/check-ins. The aim of this paper is to find the impact of structural patterns hidden in the nodes of a friendship network and external environment changes on the check-in patterns of the users. First, we classify each spatial check-in event based on its cause into either self reinforcing behavior or social influence or external stimulus. Then we mine the collective behavior of the all the users during some special events.

Optimal Placement of Taxis in a City Using Dominating Set Problem

Abstract: Mobile application based ride-hailing systems, eg, DiDi, Uber have become part of day to day life and natural choices of transport for urban commuters However, the pick-up demand in any area is not always matching with the supply or drop-off request in the same area Urban planners and researchers are working hard to balance this demand and supply situation for taxi requests The existing approaches have mainly focused on clustering of the spatial regions to identify hotspots, which refer to the locations with a high demand for pick-up requests In our study, we determined that if the hotspots focus on the clustering of high demand for pick-up requests, most of the hotspots pivot near the city center or two-three spatial regions, ignoring the other parts of the city In this work, we proposed a method, which can help in finding a local hotspot to cover the whole city area We proposed a dominating set problem based solution, which covers every part of the city This will help the drivers looking for near-by next customer in the region wherever they drop their last customer It will also reduce the waiting time for customers as well as for a driver looking for next pick-up request This would maximize their profit as well as help in improving their services

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.

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Abstract: The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation. First published online 02 May 2017

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.