A Demonstration of QARTA: An ML-based System for Accurate Map Services

Authors:

Sofiane Abbar (Qatar Computing Research Institute), Rade Stanojevic (Qatar Computing Research Institute), Mashaal Musleh (University of Minnesota), Mohamed M Elshrif (Qatar Computing Research Institute), Mohamed Mokbel (University of Minnesota - Twin Cities)

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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.

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Volume 14, No. 12

A Demonstration of QARTA: An ML-based System for Accurate Map Services

Abstract