Source-Agnostic Data Transformation via Taxonomy Re-categorization
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Solution Overview
Problem
Data from various sources in online advertising, such as usage data from different data content providers, often comes in different formats and has varying attributes, making it difficult to consolidate and compare accurately, leading to inconsistent and inaccurate views for media buyers.
Innovation Solution
A data transformation application that extracts relevant data, cleanses it, and converts it into source-agnostic data by re-categorizing platforms uniformly, allowing for easy comparison and aggregation of data from multiple sources, thereby providing a consistent view for media buyers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple data sources with different formats and attributes, then the quantity and variety of data increases, but the consistency and accuracy of data analysis deteriorates
Solution Approach 1:
The patent transforms data from multiple sources by changing its parameters through re-categorization. A taxonomy system reassigns category values to data elements, converting source-specific categories into a unified classification scheme. This parameter transformation enables consistent aggregation and comparison of data from diverse sources while preserving the original data quantity.
2Ease of operation
If data from multiple sources is consolidated without transformation, then the ease of aggregation improves, but the accuracy of comparison deteriorates due to format variations
Solution Approach 1:
The patent introduces a taxonomy system as an intermediary layer between diverse data sources and the aggregation process. This intermediary re-categorizes data elements from different sources into a unified classification framework, enabling accurate comparisons while maintaining ease of aggregation. The taxonomy acts as a mediator that standardizes category values without requiring complex custom processing for each data source.
3Reliability
If source-specific data formats are preserved, then the information integrity from each source is maintained, but the versatility of data analysis across sources deteriorates
Solution Approach 1:
The patent segments the data transformation process into distinct components: extraction of original data, identification of source-specific categories, re-categorization through taxonomy, and aggregation. This segmentation allows the system to maintain information integrity from each source while enabling versatile cross-source analysis. The original source-specific data is preserved and linked to the re-categorized data, allowing both fidelity to source and versatility in analysis.
Data Source
AI summary
A system and method to convert source dependent data into source-agnostic data includes extracting, by a processing unit associated with a data transformation application, relevant data from impression data received from a data source to obtain extracted data, cleansing the extracted data for obtaining cleansed data, and converting the cleansed data into source-agnostic data by re-categorizing at least some of the cleansed data. The system and method also include retrievably storing the source-agnostic data as persisted data into a memory associated with the data transformation application, receiving a query via a dashboard associated with the data transformation application to retrieve the persisted data, and displaying a portion of the persisted data that satisfies the query on the dashboard.