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The prevalence of positioning technologies has fostered massive trajectory data. Stay points from trajectories indicate the visiting of moving objects to locations, which provide an opportunity to understand the locations comprehensively. Many existing works rely on stay points to analyze locations. However, they are ad-hoc solutions to tackle specific problems, and it is time-consuming and tedious to develop each application. In this paper, we propose a universal StAy point-based LOcation aNalysis platform, i.e., SALON, with the characteristics of universality, efficiency and flexibility. It can retrieve stay points using flexible conditions, associate stay points with locations, extract comprehensive location profiles and visualize the analysis results to users. Based on the combination of these functions, we demonstrate three different location analysis scenarios, i.e., illegal location discovery, popular location ranking, location temporal analysis to show its characteristics.

People often refer to a place of interest (POI) by an alias. In e-commerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.

The rapid development of positioning technology produces an extremely large volume of spatio-temporal data with various geometry types such as point, line string, polygon, or a mixed combination of them. As one of the most basic but time-consuming operations, k nearest neighbors join (kNN join) has attracted much attention. However, most existing works for kNN join either ignore temporal information or consider point data only. This paper proposes a novel and useful problem, i.e., ST-kNN join, which considers both spatial closeness and temporal concurrency. To support ST-kNN join over a huge amount of spatio-temporal data with any geometry types efficiently, we propose a novel distributed solution based on Apache Spark. Specifically, our method adopts a two-round join framework. In the first round join, we propose a new spatio-temporal partitioning method that achieves spatio-temporal locality and load balance at the same time. We also propose a lightweight index structure, i.e., Time Range Count Index (TRC-index), to enable efficient ST-kNN join. In the second round join, to reduce the data transmission among different machines, we remove duplicates based on spatio-temporal reference points before shuffling local results. Extensive experiments are conducted using three real big datasets, showing that our method is much more scalable and achieves 9X faster than baselines. A demonstration system is deployed and the source code is released.

With the rapid development of the Internet of Things (IoT), massive trajectories have been generated. Trajectory data is beneficial for many urban applications. This demo presents a holistic trajectory data management system based on distributed platforms, such as Spark and HBase, namely JUST-Traj. It provides a variety of indexes to efficiently support spatio-temporal queries and analyses on massive trajectories. Additionally, it provides a convenient SQL engine to execute all operations (storage, queries, analyses) through a SQL-like statement. Finally, we design a web portal for developers and demonstrate different operations in the portal.

Chemical materials are useful but sometimes hazardous, which requires strict regulation from the government. However, due to the potential economic benefits, many illegal hazardous chemical facilities are running underground, which poses a significant public safety threat. However, the traditional solutions, e.g., on-field screening and the anonymous tip-offs, involve a lot of human efforts. In this paper, we propose a ubiquitous approach called ICFinder to detecting illegal chemical facilities with chemical transportation trajectories. We first generate candidate locations by clustering stay points extracted from trajectories, and filter out known locations. Then, we rank those locations in suspicion order by modeling whether it has the loading/unloading events. ICFinder is evaluated over the real-world dataset from Nantong in China, and the deployed system identified 20 illegal chemical facilities in 3 months.