Block Aggregation Metadata for Data Integrity
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Solution Overview
Problem
Existing data processing systems face challenges in accurately recovering and storing large amounts of data without causing loss or duplication, particularly in real-time analytics, due to errors during data storage, and conventional methods often lead to inferior results or further data loss.
Innovation Solution
The system employs metadata to indicate the structure of data blocks before storage, allowing for exact recreation of data blocks in case of failures and preventing duplication by storing metadata separately from the data blocks, and using it to perform anomaly checks for correct storage verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data blocks are stored in a database for real-time analytics, then data processing capability is improved, but data loss or duplication may occur due to errors in the data storage process
Solution Approach 1:
The patent applies preliminary action by creating and storing metadata instances that describe the structure and content of data blocks before the actual data storage occurs. This metadata includes information about data messages, their positions, and block structures. If data loss occurs, these pre-created metadata instances enable accurate reconstruction of the original data blocks without requiring re-processing of source data, thus preventing data loss while maintaining high processing capability.
2Reliability
If conventional data recovery methods are used to recover lost data, then data recovery is attempted, but data loss or duplication may occur and results are inferior
Solution Approach 1:
The patent uses copying by creating metadata instances that are separate copies containing structural information about the original data blocks. These metadata copies include details about data messages, their offsets, and block organization. When data loss occurs, the system retrieves and uses these metadata copies to accurately reconstruct the original data blocks, avoiding the need for blind recovery attempts that could cause further data loss or duplication.
Solution Approach 2:
The patent implements feedback through anomaly detection mechanisms that compare retrieved data blocks against their corresponding metadata instances. The system performs anomaly checks to verify whether retrieved data matches the expected structure and content described in the metadata. This feedback loop identifies issues like data loss or duplication and triggers appropriate recovery actions, ensuring high reliability while preventing information loss.
3Reliability
If metadata is stored separately from data blocks, then data block recreation is enabled and duplication is prevented, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the data storage system into two independent components: data blocks and metadata instances. Each data block has its corresponding metadata instance that describes its structure and content. This segmentation allows the metadata to be stored, managed, and retrieved separately from the actual data blocks, enabling accurate data recreation and duplication prevention while organizing system complexity into manageable, independent units rather than a monolithic complex system.
Data Source
AI summary
The approaches presented herein may include loading a plurality of metadata instances for a plurality of data blocks stored in a database, each metadata instance of the plurality of metadata instances indicating a respective data block structure applied to aggregate a set of data messages. The approaches may include processing a first metadata instance corresponding to a first set of one or more data blocks of the plurality of data blocks and a second metadata instance corresponding to a second set of one or more data blocks of the plurality of data blocks to perform an anomaly storage check between the first set of one or more data blocks of the plurality of data blocks and the second set of one or more data blocks of the plurality of data blocks. The approaches may include generating an output based at least in part on the anomaly storage check.