Distributed trajectory similarity search

Citations Over TimeTop 10% of 2017 papers

Abstract

Mobile and sensing devices have already become ubiquitous. They have made tracking moving objects an easy task. As a result, mobile applications like Uber and many IoT projects have generated massive amounts of trajectory data that can no longer be processed by a single machine efficiently. Among the typical query operations over trajectories, similarity search is a common yet expensive operator in querying trajectory data. It is useful for applications in different domains such as traffic and transportation optimizations, weather forecast and modeling, and sports analytics. It is also a fundamental operator for many important mining operations such as clustering and classification of trajectories. In this paper, we propose a distributed query framework to process trajectory similarity search over a large set of trajectories. We have implemented the proposed framework in Spark, a popular distributed data processing engine, by carefully considering different design choices. Our query framework supports both the Hausdorff distance the Fréchet distance. Extensive experiments have demonstrated the excellent scalability and query efficiency achieved by our design, compared to other methods and design alternatives.

Related Papers

• → Big data anonymization with spark(2017)19 cited
• → A Comparative Study of Bigdata Tools: Hadoop Vs Spark Vs Storm(2023)7 cited
• → Big Data Analysis using Apache Hadoop and Spark(2019)5 cited
• → Research on Big Data Computing Model based on Spark and Big Data Application(2021)3 cited
• → SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data(2021)2 cited