A demonstration of qarta: An ml-based system for accurate map services

Sofiane Abbar, Rade Stanojevic, Mashaal A Musleh, Mohamed Elshrif, Mohamed Mokbel

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

This demo presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to: (a) construct its own highly accurate map in terms of both map topology and edge weights, and (b) calibrate its query answers based on contextual information, including transportation modality, underlying algorithm, and time of day/week. The demo is based on actual deployment of QARTA in all Taxis in the State of Qatar and in the third-largest food delivery company in the country, and receiving hundreds of thousands of daily API calls with a real-time response time. Audience will be able to interact with the demo through various scenarios that show QARTA map and query accuracy as well as internals of QARTA.

Original languageEnglish (US)
Pages (from-to)2723-2726
Number of pages4
JournalProceedings of the VLDB Endowment
Volume14
Issue number12
DOIs
StatePublished - 2021
Event47th International Conference on Very Large Data Bases, VLDB 2021 - Virtual, Online
Duration: Aug 16 2021Aug 20 2021

Bibliographical note

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© The authors.

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