Distributed Probabilistic Models for Cached Data Validity
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Solution Overview
Problem
In distributed database systems, maintaining the validity of cached data is challenging due to the inefficiencies in data replication and the reliance on centralized machine learning systems, which can lead to data inconsistencies and increased network traffic, as well as single points of failure.
Innovation Solution
A method is implemented where clients in a distributed database system host probabilistic models to assess the validity of cached data using machine learning algorithms, with adaptive models exchanged between clients to update validity values and reduce the need for redundant data retrieval, leveraging peer-to-peer data replication to distribute the computation load and enhance resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized machine learning systems are used to assess cached data validity, then data consistency can be maintained, but network traffic increases and single points of failure are created
Solution Approach 1:
The patent segments the centralized machine learning system into distributed probabilistic models deployed across multiple clients. Each client independently hosts and executes its own probabilistic model to assess cached data validity, eliminating the single centralized control point while maintaining data consistency through distributed decision-making.
Solution Approach 2:
Clients perform self-service by autonomously assessing their own cached data validity using locally-hosted probabilistic models. This eliminates the need for centralized validation services, reducing network traffic and eliminating single points of failure while maintaining data consistency through decentralized validation.
2Productivity
If cached data is used to reduce network traffic, then efficiency improves, but data validity becomes difficult to maintain
Solution Approach 1:
The patent implements feedback mechanisms where probabilistic models continuously assess cached data validity and provide feedback for model adaptation. When cached data is accessed, the system evaluates its validity probability and uses this feedback to adapt and refine the probabilistic models, ensuring data validity is maintained while preserving caching efficiency.
Solution Approach 2:
The system performs preliminary validity assessment using probabilistic models before returning cached data to clients. This preliminary action filters out potentially invalid cached data before it reaches the user, maintaining high efficiency while ensuring data validity through pre-validation.
3Loss of energy
If probabilistic models are hosted at each client, then network traffic is reduced, but model accuracy may deteriorate due to distributed computation
Solution Approach 1:
The patent merges the capabilities of multiple distributed probabilistic models through model adaptation and sharing. Clients exchange and integrate models from other clients, combining the collective intelligence of the distributed system to maintain or improve assessment accuracy while preserving the network traffic benefits of distributed computation.
Solution Approach 2:
The system dynamically changes model parameters through adaptation based on local data characteristics and performance feedback. This allows probabilistic models to be optimized for specific client environments while maintaining overall system accuracy through parameter adjustments rather than requiring identical centralized models.
Data Source
AI summary
Database queries are processed by a first client storing cached data. The first client and a second client host a probabilistic model yielding validity values associating with the cached data indicating a probability that the cached data stored coincides with corresponding original data. The first client randomly selects queries at the time the respective query is received from one of the plurality of clients. For each of the queries randomly selected, a first piece of the cached data stored at the first client and matching the randomly selected query is retrieved, and a first piece of the original data matching the randomly selected query from the at least one original data source is retrieved. The probabilistic model is adapted using a machine learning algorithm based on the retrieved first piece of the cached data and the retrieved first piece of the original data and made available to the second client.