Compressed Bitset Stream Engine for Activity Tracking
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Solution Overview
Problem
Traditional search engine solutions for recording user activity are costly in terms of processing power and storage capacity, as they require maintaining complete indexes for each user.
Innovation Solution
The use of compressed bitsets to store and manage user activities within a computing environment, allowing for efficient storage and manipulation of activity records by associating each activity with followable aspects and using separate bitsets for each unique followable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engine solutions maintain complete indexes for each user, then user activity associations can be tracked individually, but storage capacity and processing power costs increase significantly
Solution Approach 1:
The patent segments user activity data into separate bitsets for each followable aspect (e.g., separate bitsets for different users, posts, or entities being followed). Instead of maintaining one complete index per user, the system creates multiple compact bitsets where each bitset tracks activities related to a specific followable. This segmentation reduces storage requirements while preserving the ability to track individual user associations with activities.
Solution Approach 2:
The patent changes the data representation parameter from traditional row-based or document-based indexes to bitset-based representation. Each bit in a bitset corresponds to a specific activity, allowing compact storage where entire sets of activity associations can be represented using minimal bits. This parameter change enables efficient storage and processing while maintaining precise tracking of user-activity relationships.
2Measurement precision
If traditional search engine solutions maintain complete indexes for each user, then individual user activity associations can be maintained, but processing power costs increase significantly
Solution Approach 1:
By segmenting activity tracking into separate bitsets for each followable, the system enables independent processing of each bitset. When querying user associations, the system only needs to process relevant bitsets rather than entire user indexes, significantly reducing processing power requirements while maintaining accurate association tracking.
Solution Approach 2:
The transition to bitset representation fundamentally changes the processing model from traditional index scanning to efficient bit manipulation operations. Bitset operations such as bitwise AND, OR, and NOT can be performed extremely quickly using CPU instruction sets, reducing processing power consumption while maintaining precise user-activity association tracking.
3Quantity of substance
If separate bitsets are maintained for each unique followable, then storage efficiency improves, but system complexity increases
Solution Approach 1:
The patent creates a universal bitset management system that handles multiple followables using the same data structure and operations. The same bitset framework is used for all followables, with standardized methods for creating, updating, and querying bitsets. This universality reduces system complexity despite managing multiple separate bitsets, as the same patterns and operations apply across all followables.
Solution Approach 2:
The system uses template-based copying of bitset structures when creating new followables. Instead of designing unique data structures for each followable, the system replicates a standardized bitset template and configures it for each specific followable. This copying approach maintains storage efficiency while reducing the complexity of managing diverse data structures.
Data Source
AI summary
Technologies are described for storing and reporting user activities within a computing environment. For example, bitsets (e.g., compressed and/or uncompressed bitsets) can be used to store activities (e.g., where each activity is a bit in the bitset in chronological order). Separate bitsets can be maintained for followable aspects of the activities (e.g., a separate bitset for each unique followable). Activity streams can be produced from the compressed bitsets (e.g., custom streams reflecting followables designated by users).