Content Order Classification Using Bit Vectors for Faster Duplicate Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital video systems face challenges in efficiently determining whether incoming content item orders correspond to previous orders, leading to slow and resource-intensive database queries when processing large volumes of orders.
Innovation Solution
A message classification engine uses bit vectors generated from message IDs and filter buffers to determine if incoming messages correspond to previous messages, reducing the need for database queries by employing Bloom filters and multiple filter buffers to manage false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional database query methods are used to determine whether incoming orders correspond to previous orders, then accuracy in identifying duplicate orders is maintained, but processing speed decreases and computational resources are consumed
Solution Approach 1:
The system pre-computes and stores bit vectors for message IDs in filter buffers before actual message classification occurs. When a message arrives, its bit vector is already available in the filter buffer for immediate comparison, eliminating the need for real-time database queries and enabling rapid duplicate detection
Solution Approach 2:
Instead of querying the original database for each incoming message, the system creates and uses simplified copies (bit vectors) of message identifiers stored in filter buffers. These bit vector copies enable fast comparison operations while maintaining the essential functionality of duplicate detection without the overhead of full database access
2Reliability
If database queries are performed for every incoming message to check for previous messages, then message classification accuracy is maintained, but computational resource consumption increases
Solution Approach 1:
The system extracts only the essential identifying features of messages (message IDs) and converts them to compact bit vectors, separating this identification function from the full database query process. This extraction allows duplicate detection to proceed with minimal computational resources while maintaining reliability through subsequent verification steps
Solution Approach 2:
The system transforms message IDs into bit vectors with different parameter representations (from full message data to compact binary form). This parameter transformation enables efficient comparison operations in filter buffers while maintaining the ability to accurately identify duplicate messages through the preserved bit pattern relationships
3Productivity
If filter buffers are used to reduce database queries, then processing speed increases, but false positive rate increases
Solution Approach 1:
The system implements a feedback mechanism where bit vector comparisons in filter buffers serve as a preliminary filtering stage. When potential duplicates are identified through bit vector matching, the system provides feedback by performing subsequent verification queries to confirm whether they are actual duplicates, thereby reducing false positives while maintaining high throughput
Solution Approach 2:
The duplicate detection process is segmented into multiple stages: first, rapid bit vector comparison in filter buffers identifies potential duplicates; second, verification queries confirm actual duplicates. This segmentation allows the system to achieve high productivity in the first stage while maintaining measurement precision through the second verification stage
Data Source
AI summary
Systems, apparatuses, and methods are described for message classification and management. A message may indicate a request for placement of a content item, such as an advertisement or advertisement campaign, into one or more content streams. A message ID associated with the message may be converted to a bit vector to compare to one or more filter data sets. The comparison of the bit vector to the filter data sets may determine whether the message may correspond to a previous message, such as a previous advertisement or advertisement campaign. The message may be classified as a new message or an update message.