A5036993630- Data Management and Algorithms
- Human Mobility and Location-Based Analysis
- Traffic Prediction and Management Techniques
- Transportation Planning and Optimization
- Geographic Information Systems Studies
- Complex Network Analysis Techniques
- Advanced Database Systems and Queries
- Opinion Dynamics and Social Influence
- Time Series Analysis and Forecasting
- Evacuation and Crowd Dynamics
- Context-Aware Activity Recognition Systems
- Optimization and Search Problems
- Graph Theory and Algorithms
- Transportation and Mobility Innovations
- Constraint Satisfaction and Optimization
- Automated Road and Building Extraction
- Energy Efficient Wireless Sensor Networks
- Data-Driven Disease Surveillance
- Data Mining Algorithms and Applications
- Advanced Optical Network Technologies
- Robotic Path Planning Algorithms
- Music and Audio Processing
- Opportunistic and Delay-Tolerant Networks
- Advanced Wireless Network Optimization
- IoT and Edge/Fog Computing
University of Hull
2023-2024
Indian Institute of Technology Ropar
2017-2024
Indian Institute of Technology Delhi
2015-2018
Indraprastha Institute of Information Technology Delhi
2015-2018
University of Minnesota
2011-2015
University of Minnesota System
2015
Indian Institute of Technology Kanpur
2010
Explosive growth in geospatial and temporal data as well the emergence of new technologies emphasize need for automated discovery spatiotemporal knowledge. Spatiotemporal mining studies process discovering interesting previously unknown, but potentially useful patterns from large databases. It has broad application domains including ecology environmental management, public safety, transportation, earth science, epidemiology, climatology. The complexity intrinsic relationships limits...
Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability spatial computing technologies. This paper addresses emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories cellphones GPS devices, vehicle engine measurements, temporally detailed road maps, etc. has potential to transform society via next-generation routing services as eco-routing. However, envisaged SBD-based pose several...
Efficient tools are needed to identify routes and schedules evacuate affected populations safety in the event of natural disasters. Hurricane Rita recent tsunami revealed limitations traditional approaches provide emergency preparedness for evacuees predict effects evacuation route planning (ERP). Challenges arise during evacuations due spread people over space time multiple paths that can be taken reach them; key assumptions such as stationary ranking alternative optimal substructure...
Given a spatio-temporal network, source, destination, and desired departure time interval, the All-departure-time Lagrangian Shortest Paths (ALSP) problem determines set which includes shortest path for every in given interval. ALSP is important critical societal applications such as eco-routing. However, computationally challenging due to non-stationary ranking of candidate paths across distinct departure-times. Current related work reducing redundant work, consecutive departure-times...
Modern vehicles are increasingly being equipped with rich instrumentation that enables them to collect location aware data on a wide variety of travel related phenomena such as the real-world performance engines and powertrain, driver preferences, context vehicle respect others nearby, and--indirectly--traffic transportation network itself. Combined their increased access Internet, these connected opening up vast opportunities improve safety, environmental friendliness, overall experience...
Given a spatio-temporal network (STN) and set of STN operations, the goal Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient method storing data that minimizes disk I/O costs for given operations. The SSTN important many societal applications, such as surface air transportation management systems. NP hard, challenging due inherently large volume novel semantics (e.g., Lagrangian reference frame). Related works rely on orthogonal partitioning approaches snapshot...
Our paper aims to build a classification-model which delineates the typical motion-related activities performed at metro station using smart phone sensors. We focus on movements, such as climbing stairs or moving in lift, waiting security, turnstile check out and, platform while for train. Such classifier estimates crowd levels metro-station (and trains an indirect sense), thereby adding towards vision of efficient travel. However, building is challenging due non-trivial decision boundaries...
The size, variety, and update rate of spatial datasets are increasingly exceeding the capacity commonly used computing technologies to learn, manage, process data with reasonable effort. We refer these as Spatial Big Data (SBD). Examples emerging SBD include temporally detailed (TD) roadmaps that provide speeds every minute for road-segment, GPS track from cell-phones, engine measurements fuel consumption, greenhouse gas (GHG) emissions, etc. Harnessing has a transformative potential. For...
This paper presents a system for distinguishing non-exercise activity thermogenesis (NEAT) and non-NEAT activities at home. NEAT includes energy expended on apart from sleep, eating, or traditional exercise. Our study focuses specific like cooking, sweeping, mopping, walking, climbing, descending, as well such driving, working laptop, texting, cycling, watching TV/idle time. We analyse parameters classification features, upload rate, data sampling frequency, window length, their impact...