Distributed Subtrajectory Join on Massive Datasets

Author:

Tampakis Panagiotis1,Doulkeridis Christos1,Pelekis Nikos1,Theodoridis Yannis1

Affiliation:

1. University of Piraeus, Piraeus, Greece

Abstract

Joining trajectory datasets is a significant operation in mobility data analytics and the cornerstone of various methods that aim to extract knowledge out of them. In the era of Big Data, the production of mobility data has become massive and, consequently, performing such an operation in a centralized way is not feasible. In this article, we address the problem of Distributed Subtrajectory Join processing by utilizing the MapReduce programming model. Compared to traditional trajectory join queries, this problem is even more challenging since the goal is to retrieve all the “maximal” portions of trajectories that are “similar.” We propose three solutions: (i) a well-designed basic solution, coined DTJb ; (ii) a solution that uses a preprocessing step that repartitions the data, labeled DTJr ; and (iii) a solution that, additionally, employs an indexing scheme, named DTJi . In our experimental study, we utilize a 56GB dataset of real trajectories from the maritime domain, which, to the best of our knowledge, is the largest real dataset used for experimentation in the literature of trajectory data management. The results show that DTJi performs up to 16× faster compared with DTJb , 10× faster than DTJr , and 3× faster than the closest related state-of-the-art algorithm.

Funder

MASTER

Track8Know

Operational Program Competitiveness, Entrepreneurship, and Innovation

European Regional Development Fund of the European Union and Greek national funds

EU Horizon 2020 R8I Programme

datACRON

RESEARCH-CREATE-INNOVATE

Publisher

Association for Computing Machinery (ACM)

Subject

Discrete Mathematics and Combinatorics,Geometry and Topology,Computer Science Applications,Modelling and Simulation,Information Systems,Signal Processing

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