Spatial Data Quality in the IoT Era:Management and Exploitation

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications

Fast trajectory search for real-world applications

With the popularity of smartphones equipped with GPS, a vast amount of trajectory data are being produced from location-based services, such as Uber, Google Maps, and Foursquare. We broadly divide trajectory data into three types: 1) commuter trajectories from taxicabs and ride-sharing apps; 2) vehicle trajectories from GPS navigation apps; 3) activity trajectories from social network check-ins and travel blogs. We investigate efficient and effective search on each of the three types of trajectory data, each of which has a real-world application. In particular: 1) commuter trajectory search can serve for the transport capacity estimation and route planning; 2) vehicle trajectory search can help real-time traffic monitoring and trend analysis; 3) activity trajectory search can be used in interactive and personalized trip planning. As the most straightforward trajectory data, a commuter trajectory only contains two points: origin and destination indicating a passenger’s movement, which is valuable for transportation decision making. In this thesis, we propose a novel query RkNNT to estimate the capacity of a bus route in the transport network. Answering RkNNT is challenging due to the high amount of data from commuters. We propose efficient solutions to prune most trajectories which cannot choose a query route as their nearest one. Further, we apply RkNNT to the optimal route planning problem-MaxRkNNT. A vehicle trajectory has more points than a commuter trajectory, as it tracks the whole trace of a vehicle and can further advocate the application of traffic monitoring. We conclude the common queries over trajectory data for monitoring purposes and proposes a search engine Torch to manage and search trajectories with map matching over a road network, instead of storing raw data sampled from GPS with a high cost. Besides improving the efficiency of search, Torch also supports compression, effectiveness evaluation of various existing similarity measures, and large-scale clustering k-paths with a novel similarity measure LORS. Exploring the activity trajectory data which contains textual information can help plan personalized trips for tourists. Based on spatial indexes which we propose for commuter and vehicle trajectory data, we further develop a unified search paradigm to process various top-k queries over activity trajectory and POIs data (hotels, restaurants, and attractions, etc.) at the same time. In particular, a new point-wise similarity measure PATS and an indexing framework with a unified search paradigm are proposed