Abstract
Driven by the ubiquity of location-based services, that produces a massive amount of moving objects. Querying and analyzing these data become a must for a wide range of applications. This demonstration presents a scalable data management framework. The proposed system is well-suited to efficiently support several basic queries, such as range, k NN, and similarity queries. These queries and the architectural design of the proposed system are extendable, in a way that enables users to build various applications and operations.
Original language | English (US) |
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Title of host publication | Proceedings - 2020 21st IEEE International Conference on Mobile Data Management, MDM 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 226-227 |
Number of pages | 2 |
ISBN (Electronic) | 9781728146638 |
DOIs | |
State | Published - Jun 2020 |
Event | 21st IEEE International Conference on Mobile Data Management, MDM 2020 - Versailles, France Duration: Jun 30 2020 → Jul 3 2020 |
Publication series
Name | Proceedings - IEEE International Conference on Mobile Data Management |
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Volume | 2020-June |
ISSN (Print) | 1551-6245 |
Conference
Conference | 21st IEEE International Conference on Mobile Data Management, MDM 2020 |
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Country/Territory | France |
City | Versailles |
Period | 6/30/20 → 7/3/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.