Home // GEOProcessing 2019, The Eleventh International Conference on Advanced Geographic Information Systems, Applications, and Services // View article

A Universal Large-Scale Trajectory Indexing for Cloud-Based Moving Object Applications

Authors:
Omar Alqahtani
Tom Altman

Keywords: big data, moving objects, distribution algorithms, spatial indexing, Apache Spark.

Abstract:
The tremendous upsurge in low-cost geospatial chipsets brings out huge volumes of moving object trajectories, which catalyze a wide range of trajectory-driven applications (e.g., sustainable cities, smart transportation, green routing, intelligent homeland security, etc.). Consequently, there has been an emergence of more divergent queries and increased processing complexity. Instead of developing a query-specific approach for limited applications, we propose a Universal Moving Object Index, a flexible index that is capable of fine-tuning based on the application needs, without any structural modification. Also, we introduce a Light-Weight Hybrid Index for heavily-loaded memory. Besides the ability to support trajectory-driven applications universally, both approaches are designed to be easily adopted by cloud-compatible MapReduce platforms. An extensive empirical study is conducted to validate our approaches and to highlight some critical challenges.

Pages: 42 to 51

Copyright: Copyright (c) IARIA, 2019

Publication date: February 24, 2019

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-61208-687-3

Location: Athens, Greece

Dates: from February 24, 2019 to February 28, 2019