7 research outputs found
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
Origin-Destination Travel Time Oracle for Map-based Services
Given an origin (O), a destination (D), and a departure time (T), an
Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of
the time it takes to travel from O to D when departing at T. ODT-Oracles serve
important purposes in map-based services. To enable the construction of such
oracles, we provide a travel-time estimation (TTE) solution that leverages
historical trajectories to estimate time-varying travel times for OD pairs.
The problem is complicated by the fact that multiple historical trajectories
with different travel times may connect an OD pair, while trajectories may vary
from one another. To solve the problem, it is crucial to remove outlier
trajectories when doing travel time estimation for future queries.
We propose a novel, two-stage framework called Diffusion-based
Origin-destination Travel Time Estimation (DOT), that solves the problem.
First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that
enables building a diffusion-based PiT inference process by learning
correlations between OD pairs and historical trajectories. Specifically, given
an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a
Masked Vision Transformer~(MViT) that effectively and efficiently estimates a
travel time based on the inferred PiT. We report on extensive experiments on
two real-world datasets that offer evidence that DOT is capable of
outperforming baseline methods in terms of accuracy, scalability, and
explainability.Comment: 15 pages, 12 figures, accepted by SIGMOD International Conference on
Management of Data 202
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change