Abstract: Systems and methods herein describe receiving a target location from a computing device, using a machine learning model: determining a first access point and a second access point associated with the target location, causing presentation of the first access point as a first selectable user interface element and the second access point as a second selectable user interface within a graphical user interface on the computing device, receiving a first selection of the first selectable user interface element from the computing device; and in response to receiving the first selection, and initiating a trip request based on the refined map coordinates of the first access point.

Abstract: Systems and methods herein describe receiving a target location from a computing device, using a machine learning model: determining a first access point and a second access point associated with the target location, causing presentation of the first access point as a first selectable user interface element and the second access point as a second selectable user interface within a graphical user interface on the computing device, receiving a first selection of the first selectable user interface element from the computing device; and in response to receiving the first selection, and initiating a trip request based on the refined map coordinates of the first access point.

Abstract: Systems and methods are provided for receiving a start location and a destination location, determining a road segment corresponding to the destination location, and identifying a node nearest the road segment corresponding to the destination location, whereby the node corresponds to a nearest left-hand node for a right-hand driving country or a nearest right-hand node for a left-hand driving country. The systems and methods further provide for generating a first route from the start location to the node nearest the road segment corresponding to the destination location, generating a second route from the node nearest the road segment corresponding to the destination location, to the destination location, and combining the first route and the second route to generate a final route. Thus, the final route comprises a route from the start location, through the node nearest the road segment corresponding to the destination location, to the destination location.

Abstract: Systems and methods are provided for receiving a start location and a destination location, determining a road segment corresponding to the destination location, and identifying a node nearest the road segment corresponding to the destination location, whereby the node corresponds to a nearest left-hand node for a right-hand driving country or a nearest right-hand node for a left-hand driving country. The systems and methods further provide for generating a first route from the start location to the node nearest the road segment corresponding to the destination location, generating a second route from the node nearest the road segment corresponding to the destination location, to the destination location, and combining the first route and the second route to generate a final route. Thus, the final route comprises a route from the start location, through the node nearest the road segment corresponding to the destination location, to the destination location.

Abstract: Systems and methods herein describe receiving a target location from a computing device, using a machine learning model: determining a first access point and a second access point associated with the target location, causing presentation of the first access point as a first selectable user interface element and the second access point as a second selectable user interface within a graphical user interface on the computing device, receiving a first selection of the first selectable user interface element from the computing device; and in response to receiving the first selection, and initiating a trip request based on the refined map coordinates of the first access point.

Abstract: Systems and methods herein describe receiving a target location from a computing device, using a machine learning model: determining a first access point and a second access point associated with the target location, causing presentation of the first access point as a first selectable user interface element and the second access point as a second selectable user interface within a graphical user interface on the computing device, receiving a first selection of the first selectable user interface element from the computing device; and in response to receiving the first selection, and initiating a trip request based on the refined map coordinates of the first access point.

Abstract: Systems and methods herein describe receiving a target location from a computing device, using a machine learning model: determining a first access point and a second access point associated with the target location, causing presentation of the first access point as a first selectable user interface element and the second access point as a second selectable user interface within a graphical user interface on the computing device, receiving a first selection of the first selectable user interface element from the computing device; and in response to receiving the first selection, and initiating a trip request based on the refined map coordinates of the first access point.

Abstract: An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Abstract: Systems and methods are provided for receiving a start location and a destination location, determining a road segment corresponding to the destination location, and identifying a node nearest the road segment corresponding to the destination location, whereby the node corresponds to a nearest left-hand node for a right-hand driving country or a nearest right-hand node for a left-hand driving country. The systems and methods further provide for generating a first route from the start location to the node nearest the road segment corresponding to the destination location, generating a second route from the node nearest the road segment corresponding to the destination location, to the destination location, and combining the first route and the second route to generate a final route. Thus, the final route comprises a route from the start location, through the node nearest the road segment corresponding to the destination location, to the destination location.

Abstract: The disclosed embodiments provide a system for managing a materialized view of data in a data store. During operation, the system obtains a first creation time of a first materialized view of the data in the data store. Next, the system verifies that the first materialized view is not affected by deletions applied to the data since the first creation time. The system then identifies one or more additions to the data that are not included in the first materialized view. Finally, the system applies the additions to the first materialized view to maintain an eventual consistency of the first materialized view with the data store.

Abstract: An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Abstract: An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Abstract: The disclosed embodiments provide a system for managing a materialized view of data in a data store. During operation, the system obtains a first creation time of a first materialized view of the data in the data store. Next, the system verifies that the first materialized view is not affected by deletions applied to the data since the first creation time. The system then identifies one or more additions to the data that are not included in the first materialized view. Finally, the system applies the additions to the first materialized view to maintain an eventual consistency of the first materialized view with the data store.

Abstract: The disclosed embodiments provide a system for performing multi-tenancy distribution of a graph database cache. During operation, the system obtains a cache of a set of records in a graph database storing a graph, wherein the graph includes a set of nodes, a set of edges between pairs of nodes in the set of nodes, and a set of predicates. Next, the system identifies one or more records in the cache that fail a validation test. The system then merges a remainder of the cache with online updates to the subset of records to produce a merged version of the cache. Finally, the system distributes the merged version to one or more instances of the graph database for use in processing queries of the graph database.

Abstract: An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Abstract: The disclosed embodiments provide a system for performing multi-tenancy distribution of a graph database cache. During operation, the system obtains a cache of a set of records in a graph database storing a graph, wherein the graph includes a set of nodes, a set of edges between pairs of nodes in the set of nodes, and a set of predicates. Next, the system identifies one or more records in the cache that fail a validation test. The system then merges a remainder of the cache with online updates to the subset of records to produce a merged version of the cache. Finally, the system distributes the merged version to one or more instances of the graph database for use in processing queries of the graph database.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines a current incoming queries per second (QPS) to one or more components for processing queries of a graph database, wherein the graph database is replicated across multiple clusters and distributed among a set of storage nodes in each of the clusters. Next, the system uses the current incoming QPS to estimate, for the one or more components, an expected QPS associated with fanning out of the queries to the clusters. The system then selects a number of clusters in the multiple clusters for fanning out of a query based on the expected QPS and one or more throughput limits for the one or more components. Finally, the system transmits the query to one or more of the storage nodes in the selected number of clusters.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a graph of a social network, wherein the graph includes a set of nodes representing users in the social network and a set of edges representing relationships between pairs of the users. Next, the system stores, on a single computer system, a static representation of the graph, wherein the static representation includes a first set of fixed-size blocks representing the nodes and the edges and a first index that maps a set of identifiers for the nodes and the edges to offsets of the first set of fixed-size blocks. The system then uses the static representation of the graph to process, by the single computer system, one or more queries of the graph.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies a query type of a query of a graph database storing a graph, wherein the graph comprises nodes, edges between pairs of nodes, and predicates. Next, the system determines, based on the query type, a number of clusters on which the graph database is replicated for fanning out of the query. The system then selects a set of storage nodes in the determined number of clusters for processing of the query. Finally, the system generates a fan-out of the query to the selected storage nodes.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system generates a first distribution of a set of partitions comprising a graph database across a first set of storage nodes in a first cluster. Next, the system replicates the graph database by generating a second, different distribution of the set of partitions across a second set of storage nodes in a second cluster. The system then identifies one or more partitions storing data associated with the query and uses a set of mappings comprising the set of partitions, the first and second sets of storage nodes, and the first and second clusters to select one or more storage nodes containing the one or more partitions. Finally, the system transmits one or more portions of the query to the selected storage nodes.