Web Analytics Indexing via Exponential Decay
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Solution Overview
Problem
The increasing volume of web analytics data poses challenges in storage and processing, leading to complexity and reduced relevance over time, making it difficult for website providers to efficiently manage and extract valuable insights.
Innovation Solution
The method involves generating and updating index values using exponential decay, which reduces the influence of older data and emphasizes more recent activity, allowing for efficient storage and processing by representing large datasets with a smaller index value, stored in locations like cookies or databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If web analytics data is stored in a large database to preserve all historical data, then data completeness is improved, but storage space requirements and processing complexity increase
Solution Approach 1:
The patent extracts only the essential features from raw web analytics data to create index values, rather than storing complete historical data. Index values are generated by processing logfiles or tagged data to extract key metrics (e.g., visit counts, unique visitors, page views) that represent the essential information needed for analysis, thereby reducing storage requirements while maintaining data utility
Solution Approach 2:
Instead of storing all raw data and processing it when needed, the patent inverts the approach by pre-processing data into index values that can be directly used for analysis. The index values serve as simplified representations that eliminate the need to process large volumes of raw historical data, reducing processing complexity while preserving analytical capabilities
2Loss of information
If all historical web analytics data is retained for analysis, then data availability is improved, but the relevance of older data decreases while still consuming storage and processing resources
Solution Approach 1:
The patent applies parameter changes by transforming raw data into index values with specific properties (aggregated metrics, time-based weighting). Index values change over time based on decay parameters, where older data automatically reduces in influence. This allows the system to maintain data availability through index values while automatically reducing the processing resources allocated to older, less relevant data through decay mechanisms
3Measurement precision
If index values are updated frequently to maintain data accuracy, then data freshness is improved, but processing overhead increases
Solution Approach 1:
The patent implements periodic action through scheduled index value updates at predetermined time intervals (e.g., daily, weekly, monthly). Instead of continuous processing, the system updates index values periodically, which maintains data accuracy for time-sensitive analyses while significantly reducing processing overhead compared to real-time or frequent updates. The decay parameter ensures that index values remain meaningful between updates
Data Source
AI summary
Provided is a method that includes a method for updating index data. The method includes receiving index data, including an index value indicative of user activity on a network site and an index time corresponding to a time used for calculating the index value, receiving an update index time corresponding to a time used for updating the index data, determining an updated index value using an exponential decay of the index value from the index time to the update index time, wherein the updated index value comprises a decayed value of the index value corresponding to the update time, and storing updated index data including the updated index value and the update index time.