Probabilistic Model Data Deduplication for Stream Processing
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Solution Overview
Problem
Current event management systems face inefficiencies in processing high volumes of data streams due to redundant data, which can lead to resource-intensive queries and slow performance, especially in real-time tracking scenarios within multi-tenant cloud computing environments.
Innovation Solution
A data accuracy manager utilizing a probabilistic model, such as a bloom filter, to identify and discard redundant data without querying event or metric storage systems, thereby improving efficiency and reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional event management systems process high volumes of data streams, then real-time tracking capability is maintained, but resource consumption increases and performance slows due to redundant data
Solution Approach 1:
The patent applies preliminary action by maintaining a probabilistic model (bloom filter) that pre-records metric identifiers before data stream processing. This allows the system to quickly identify and filter redundant data in real-time without performing expensive storage queries, thus maintaining high throughput while reducing computational resource consumption during actual data processing operations
Solution Approach 2:
The patent introduces a probabilistic model as an intermediary layer between the data stream and the storage system. This intermediary structure enables efficient duplicate detection by providing a fast lookup mechanism that avoids direct interaction with slow storage systems, thereby improving processing speed and reducing resource usage simultaneously
2Speed
If probabilistic model is used to identify redundant data, then query efficiency improves, but measurement precision of data deduplication decreases due to probabilistic nature
Solution Approach 1:
The patent applies partial action by using the probabilistic model to filter out obvious duplicates quickly, then performing verification queries for borderline cases. This two-stage approach achieves high processing speed for the majority of clear-cut cases while maintaining accuracy for uncertain cases through selective verification
Solution Approach 2:
The system incorporates feedback mechanisms where the results of probabilistic model queries inform subsequent verification steps. When the probabilistic model indicates potential duplicates, the system adjusts its verification strategy accordingly, refining the balance between speed and precision based on the confidence level provided by the probabilistic assessment
3Adaptability or versatility
If redundant data is not filtered, then data completeness is maintained, but system scalability is limited due to resource-intensive processing
Solution Approach 1:
The patent extracts and removes redundant data from the processing stream using the probabilistic model as a filtering mechanism. By taking out duplicate metric data before it enters the main processing pipeline, the system reduces the volume of data that requires full processing, thereby improving scalability without compromising the integrity of unique data
Solution Approach 2:
The system performs preliminary filtering of redundant data using the probabilistic model before committing resources to full data processing. This preliminary action reduces the quantity of data that needs to be handled by resource-intensive operations, enabling the system to scale to larger data volumes efficiently
Data Source
AI summary
A method and system of data deduplication for data streams in a multi-tenant system. The method receives, at a data accuracy manager, an event from an activity tracking component, determine whether the event is recorded in a probabilistic model that tracks previously received events from the activity tracking component, where the probabilistic model can accurately identify the event has not been previously received with a possible false positive response where the event has been previously received, determines whether information for the event is stored in a metric storage, where the metric storage is a database of metrics derived from the previously received events, and discards the event in response to determining that the event is recorded in the probabilistic model and in the metric storage.