A Survey on Big Data Processing Frameworks for Mobility Analytics

Author:

Doulkeridis Christos1,Vlachou Akrivi2,Pelekis Nikos1,Theodoridis Yannis1

Affiliation:

1. University of Piraeus, Greece

2. University of the Aegean, Greece

Abstract

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems,Software

Reference67 articles.

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1. DICER: Data Intensive Computing Environment and Runtime for Evaluating Unprecedented Scale of Geospatial-Temporal Human Mobility Data;2024 25th IEEE International Conference on Mobile Data Management (MDM);2024-06-24

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3. MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data;2023 IEEE International Conference on Big Data (BigData);2023-12-15

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