Push Notification Platform Location Bucket Indexing
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Solution Overview
Problem
Collecting and efficiently utilizing location information from mobile device users for personalized experiences is challenging due to difficulties in processing and managing large volumes of data effectively.
Innovation Solution
A push notification platform that processes location and analytics data to index devices based on location and time, allowing for efficient querying and targeting of specific user groups for personalized notifications, while also providing tools for composing and validating push notifications across different operating systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If location information is collected from mobile device users, then personalized experiences can be provided, but efficient processing and management of large volumes of data becomes difficult
Solution Approach 1:
The system segments location data processing by creating separate indexing structures for different geographic regions (buckets). Each bucket contains location data for a specific geographic area, allowing the system to process and query location information in smaller, manageable units rather than handling all location data as a single large dataset. This segmentation reduces processing complexity while maintaining the ability to provide personalized experiences based on user location.
2Measurement precision
If location data is stored for all users, then precise targeting is possible, but data management efficiency decreases
Solution Approach 1:
The system divides the geographic space into multiple buckets, with each bucket storing location data for users within that specific geographic region. This segmentation allows precise location targeting within each bucket while reducing the overall data management burden, as queries can be restricted to relevant geographic buckets rather than searching through all user location data.
Solution Approach 2:
The system adds a geographic dimension to data organization by structuring location data according to spatial boundaries (buckets). This dimensional change allows the system to efficiently manage location data by organizing it along geographic axes, enabling fast queries based on location criteria without requiring full-scan searches through all user data.
3Adaptability or versatility
If location information is made available for marketers, then personalized marketing can be achieved, but data collection and processing difficulty increases
Solution Approach 1:
The system segments location data into geographic buckets that can be independently queried and managed. This segmentation simplifies data collection and processing for marketing purposes, as marketers can target specific geographic regions by querying relevant buckets rather than managing complex, unstructured location data from all users.
Solution Approach 2:
The system introduces an intermediary indexing layer between raw location data collection and marketing applications. The bucket-based indexing structure acts as a mediator that transforms raw location information into an organized format suitable for marketing queries, reducing the operational complexity of data collection and processing while enabling personalized marketing campaigns.
Data Source
AI summary
Querying for devices based on location is disclosed. A request to send a push notification to a location is received. One or more bucket indexes to search for the presence of a device identifier are determined. The one or more bucket indexes are searched for the presence of the device identifier. One or more device identifiers are received in response to the search. The push notification is sent to one or more devices associated with the received one or more device identifiers.