Abstract: Methods, computer systems, and computer-storage media are provided for increasing the efficiency of a multi-tenant geospatial data index. Efficiency is increased by using a multi-tenant model for storing and serving the data, processing raw geospatial data received from tenants into a runtime-optimized format, and by partitioning tenant geospatial data into a processor memory portion and a file system memory portion. Efficiency is also increased by executing a staged upload of the processor memory portion and the file system memory portion to a subset of host machines in order to check for invalid data before uploading the data to the remaining host machines. Additionally, efficiency is increased by optimizing geospatial search queries using query filters stored in a query filter cache, and executing the query filters initially against the processor memory.

Abstract: Methods, computer systems, and computer-storage media are provided for increasing the efficiency of a multi-tenant geospatial data index. Efficiency is increased by using a multi-tenant model for storing and serving the data, processing raw geospatial data received from tenants into a runtime-optimized format, and by partitioning tenant geospatial data into a processor memory portion and a file system memory portion. Efficiency is also increased by executing a staged upload of the processor memory portion and the file system memory portion to a subset of host machines in order to check for invalid data before uploading the data to the remaining host machines. Additionally, efficiency is increased by optimizing geospatial search queries using query filters stored in a query filter cache, and executing the query filters initially against the processor memory.

Abstract: Methods, computer systems, and computer-storage media are provided for increasing the efficiency of a multi-tenant geospatial data index. Efficiency is increased by using a multi-tenant model for storing and serving the data, processing raw geospatial data received from tenants into a runtime-optimized format, and by partitioning tenant geospatial data into a processor memory portion and a file system memory portion. Efficiency is also increased by executing a staged upload of the processor memory portion and the file system memory portion to a subset of host machines in order to check for invalid data before uploading the data to the remaining host machines. Additionally, efficiency is increased by optimizing geospatial search queries using query filters stored in a query filter cache, and executing the query filters initially against the processor memory.

Abstract: Methods, computer systems, and computer-storage media are provided for increasing the efficiency of a multi-tenant geospatial data index. Efficiency is increased by using a multi-tenant model for storing and serving the data, processing raw geospatial data received from tenants into a runtime-optimized format, and by partitioning tenant geospatial data into a processor memory portion and a file system memory portion. Efficiency is also increased by executing a staged upload of the processor memory portion and the file system memory portion to a subset of host machines in order to check for invalid data before uploading the data to the remaining host machines. Additionally, efficiency is increased by optimizing geospatial search queries using query filters stored in a query filter cache, and executing the query filters initially against the processor memory.

Abstract: Geocoding architecture based on information retrieval. The geocoding information retrieval system operates on locations and geographic entities. The architecture uses parameters in addition to the query string, if available. The parameters provide contextual information that enables filtering and sorting the results. The additional parameters include market, culture, map view, and user location. A geocoding triage process uses the query and context information provided and output of a machine-learning parser to select the data-lookup geocoders for processing the query. A results processing component collects the results from the underlying data-lookup geocoders, merges the results into a single list after normalizing the scores, and then re-sorts the results list.

Abstract: Geocoding architecture based on information retrieval. The geocoding information retrieval system operates on locations and geographic entities. The architecture uses parameters in addition to the query string, if available. The parameters provide contextual information that enables filtering and sorting the results. The additional parameters include market, culture, map view, and user location. A geocoding triage process uses the query and context information provided and output of a machine-learning parser to select the data-lookup geocoders for processing the query. A results processing component collects the results from the underlying data-lookup geocoders, merges the results into a single list after normalizing the scores, and then re-sorts the results list.