Configurable Data Stream Generation with Clustering
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Solution Overview
Problem
Existing data stream generation algorithms are limited in their applicability and accuracy, failing to accurately reflect real-world changes and change rates in data streams, making them inadequate for simulating and testing applications in developmental stages.
Innovation Solution
The system generates data streams with customizable change rates and clustering by using a base data stream and creating future generations based on block modification tables, allowing for simulation of changes and efficient data stream alteration, enabling the injection of changes in a clustered, uniform, or random manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing data stream generation algorithms are used, then data streams can be generated, but they fail to accurately reflect real-world changes and change rates
Solution Approach 1:
The patent applies parameter changes by transforming the base data stream through controlled modifications including bit rotation, block permutation, and substitution. These parameter transformations enable the generation of multiple data stream variants with different change rates and patterns, accurately reflecting real-world data variability while maintaining algorithmic efficiency and broad applicability.
2Measurement precision
If data streams are generated with high change rates to reflect real-world dynamics, then accuracy improves, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential transformation operations from complete data stream generation. By using a base data stream and applying selective modifications (bit rotations, block permutations, substitutions) through transformation tables, the system generates high-variability data streams on-demand without storing multiple complete data sets, significantly reducing storage requirements while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-defining transformation rules and tables (bit rotation patterns, block permutation sequences, substitution mappings) that can be applied to generate data streams with specific change rates. This allows accurate simulation of real-world dynamics through controlled transformations rather than storing pre-generated high-variability data streams.
3Measurement precision
If complex transformation operations are applied to achieve clustering and configurable change rates, then simulation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the data stream into blocks and applies transformations at different levels (bit-level rotations, block-level permutations, and overall structure substitutions). This segmentation allows complex simulation requirements to be met through a series of simpler, more manageable operations, reducing overall computational complexity while maintaining high simulation accuracy through the coordinated application of multiple transformation layers.
Data Source
AI summary
One example method includes receiving a generation 0 data stream, creating and/or receiving a mask that includes multiple entries, each of which corresponds to a respective block of the generation 0 data stream, and each mask entry indicating a value n (0<=n<=desired generation N), identifying, in the mask, all non-zero entries, locating, in the generation 0 data stream, the blocks to which this entry corresponds to and based on the value n, modifying the generation 0 block to generate a generation N data stream.