Map matching has long been a fundamental yet challenging problem. However, there are currently only a few public small-scale map matching benchmark datasets. Both the GPS trajectories and the road network in the existing map matching datasets are represented by location only, which cannot support the development of data-driven and semantic-enriched map matching algorithms that have increasingly emerged in recent years. To bridge the gap, we present the first large-scale attribute-rich map matching benchmark dataset covering two cities in Southeast Asia (i.e., Singapore and Jakarta). Our GPS trajectories contain rich contextual information including the accuracy level, bearing, speed, and transport mode in addition to the latitude and longitude geo-coordinates. The underlying road network is a snapshot of the OpenStreetMap where roads are associated with rich attributes such as road type, speed limit, etc. To ensure the quality of our dataset, the annotation of the map-matched routes has been conducted by a team of professional map operators. Analysis on our dataset provides new insights into the challenges and opportunities in map matching algorithms.

Grab is a Singapore-based technology company offering ride-hailing transport service, food delivery and payment solutions for Southeast Asia. One crucial part of transport service is to provide users with desired POIs as pickups and dropoffs based on their locations with as less effort as possible, which can be measured by the clicking times on the screen before clicking the booking button. As a geo-based service, POI (point of interest) discovery and recommendation involves a lot of geometric calculation and high traffic throughput. It is important to ensure the high availability and stability of POI discovery and recommendation. We adapt Golang-based service architecture to ensure the stability of the backend system. Elastic search is utilized to organize millions of POI data on the database layer. Redis is used to shorten the response time of each request as cache. In this paper, we will introduce our Golang-based service architecture and how we tackle the online challenges by deploying cutting-edge techniques such as Elastic Search and Redis according to unique business scenarios.

Many data exploration applications require the ability to identify the top-k results according to a scoring function. We study a class of top-k ranking problems where top-k candidates in a dataset are scored with the assistance of another set. We call this class of workloads cross aggregate ranking. Example computation problems include evaluating the Hausdorff distance between two datasets, finding the medoid or radius within one dataset, and finding the closest or farthest pair between two datasets. In this paper, we propose a parallel and distributed solution to process cross aggregate ranking workloads. Our solution subdivides the aggregate score computation of each candidate into tasks while constantly maintains the tentative top-k results as an uncertain top-k result set. The crux of our proposed approach lies in our entropy-based scheduling technique to determine result-yielding tasks based on their abilities to reduce the uncertainty of the tentative result set. Experimental results show that our proposed approach consistently outperforms the best existing one in two different types of cross aggregate rank workloads using real datasets.