Customer Data Linkage via Union-Find and Path Compression
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Solution Overview
Problem
Retailers face challenges in accurately attributing customer data from various internal and external sources, such as in-store and online purchases, and advertisement activities to the correct customer, leading to difficulties in tracking customer behavior effectively.
Innovation Solution
A computing device is configured to obtain linkage data, partition it, and execute algorithms like union find, global shuffle, and path compression to assign identifiers to nodes, thereby linking customer data from different sources and ensuring accurate attribution of customer activities across multiple channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer data from multiple sources is collected and linked manually, then data accuracy may be maintained, but processing time and operational complexity increase significantly
Solution Approach 1:
The patent segments the customer data linkage process into distinct algorithmic components: union-find algorithms for establishing connections, path compression for optimizing query performance, and parallel processing for accelerating computation. This segmentation enables automated processing while maintaining accuracy standards.
Solution Approach 2:
The patent replaces manual mechanical data linkage processes with automated computational algorithms. The union-find data structure and path compression techniques substitute human-operated matching processes, dramatically reducing processing time while maintaining or improving attribution accuracy through consistent algorithmic application.
2Productivity
If automated algorithms are used to link customer data, then processing speed increases, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces intermediary data structures (union-find sets, parent pointers, and compressed paths) that mediate between raw customer data from multiple sources and the final attributed results. These intermediaries simplify the computational complexity by organizing data relationships in a manageable hierarchical structure that enables parallel processing.
Solution Approach 2:
The patent performs preliminary actions by pre-processing customer data into standardized formats, pre-establishing union-find structures before main processing, and pre-defining partitioning schemes. This preliminary organization reduces the complexity of the main linkage operation and enables more efficient parallel execution.
3Productivity
If customer data is partitioned for parallel processing, then processing efficiency improves, but data consistency and linkage accuracy across partitions may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where path compression algorithms continuously adjust and refine the linkage structures based on query patterns. This feedback ensures that even when data is partitioned, the union-find structures maintain global consistency by compressing paths and updating parent pointers based on actual usage patterns across all partitions.
Solution Approach 2:
The patent merges results from multiple partitions by combining the union-find structures and applying path compression across partition boundaries. This merging process ensures that linkage accuracy is maintained globally by integrating the localized processing results into a consistent overall attribution framework.
Data Source
AI summary
This application relates to apparatus and methods for automatically associating customer data to a corresponding customer. A computing device may receive linking data identifying a plurality of links, where each like associates at least two nodes that each represent customer data. The computing device may partition the linking data into multiple partitions, and cause a union find algorithm to be executed for each partition in parallel to associate each node with a parent ID. The computing device may iteratively execute a global shuffle algorithm to place all same nodes in a same partition, and may assign a same parent ID to the same nodes. The computing device may iteratively execute a path compression algorithm across all partitions to generate a graph output that associates all child nodes of a same parent node with the same parent ID.