Dual-Mode Data Hashing for Redundancy Elimination
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Solution Overview
Problem
Existing data processing techniques lack a balance between speed and effectiveness in identifying and eliminating data redundancies, with fast methods often failing to identify most redundancies and slower methods being too time-consuming.
Innovation Solution
A dual-mode approach using a faster initial selection function, such as a boxcar sum function, to pre-select data blocks or chunks, followed by a slower but more accurate function, like a Rabin fingerprint function, to refine boundary points and identify redundancies, thereby improving data processing speed without compromising quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If fast hashing functions are used for data selection, then processing speed is improved, but data redundancy elimination effectiveness deteriorates
Solution Approach 1:
The patent segments the data processing into two distinct phases: a fast selection phase using simple hashing functions to identify candidate blocks, and a refinement phase using more accurate but slower functions to verify and finalize redundancy identification. This segmentation allows the system to benefit from both speed and accuracy without being constrained by the trade-off in single-phase approaches.
Solution Approach 2:
The patent applies preliminary action by using fast hashing functions to pre-select candidate data blocks that may contain redundancies before applying the more computationally intensive accurate hashing functions. This preliminary filtering step reduces the overall computation burden while maintaining high redundancy elimination effectiveness.
2Measurement precision
If accurate hashing functions are used for data selection, then redundancy elimination effectiveness is improved, but processing time increases
Solution Approach 1:
The patent divides the processing workflow into two segments: an initial fast filtering stage that quickly identifies potential redundancies, and a subsequent refinement stage that applies accurate hashing only to the reduced set of candidates. This segmentation minimizes the time loss by limiting the application of slow but accurate functions to necessary cases only.
Solution Approach 2:
The patent performs preliminary action by applying fast hashing functions first to eliminate obviously non-redundant blocks, thereby reducing the dataset size before applying the more time-consuming accurate hashing functions. This approach significantly reduces total processing time while maintaining high redundancy identification accuracy.
3Speed
If simple hashing functions are used, then processing speed is improved, but data block boundary detection accuracy deteriorates
Solution Approach 1:
The patent segments boundary detection into two stages: rapid initial boundary identification using simple hashing to locate candidate regions, followed by precise boundary refinement using more sophisticated functions only in those candidate regions. This ensures both speed in covering the entire dataset and precision in final boundary determination.
Solution Approach 2:
The patent applies preliminary action by using simple hashing functions to quickly identify candidate boundary regions before applying more accurate boundary detection methods. This preliminary step reduces the search space significantly, allowing the system to achieve high boundary accuracy without the time cost of applying complex algorithms uniformly across all data.
Data Source
AI summary
Systems and methods for data management and data processing are provided. Embodiments may include systems and methods relating to fast data selection with reasonably high quality results, and may include a faster data selection function and a slower data selection function. Various embodiments may include systems and methods relating to data hashing and/or data redundancy identification and elimination for a data set or a string of data. Embodiments may include a first selection function is used to pre-select boundary points or data blocks/windows from a data set or data stream and a second selection function is used to refine the boundary points or data blocks/windows. The second selection function may be better at determining the best places for boundary points or data blocks/windows in the data set or data stream. In various embodiments, data may be processed by a first faster hash function and slower more discriminating second hash function.