Tuple Generation for Anomalous Data in Structured Datasets
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Solution Overview
Problem
Existing big data systems require significant expertise to analyze large datasets effectively, and they often struggle with efficiently identifying outliers and patterns due to the exponential growth of possible feature combinations, which can lead to uninhabited or sparsely inhabited tuples that are not statistically significant.
Innovation Solution
The technology automatically generates tuples from structured records by combining feature values and applies a thresholding criterion to manage the number of tuples, eliminating uninhabited and sparsely inhabited ones, allowing for unsupervised outlier analysis without user specification of tuple combinations, and uses indexing and querying methods to efficiently analyze large data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all possible feature combinations are generated for analysis, then complete coverage of data relationships is achieved, but the number of tuples grows exponentially making processing infeasible
Solution Approach 1:
The patent extracts and retains only the most significant feature combinations (tuples) based on statistical criteria, discarding less important combinations. This is achieved by computing tuple significance scores and selecting only those above a threshold, thereby reducing the exponential tuple space to a manageable subset that preserves the most valuable data relationships for analysis.
Solution Approach 2:
The patent changes the parameter of tuple selection from exhaustive enumeration to significance-based filtering. By introducing statistical significance thresholds and computing relevance scores for each tuple, the system transforms the problem from processing all possible combinations to processing only statistically meaningful ones, effectively managing the exponential growth of tuple space.
2Measurement precision
If expertise-dependent manual analysis is used, then analysis accuracy can be maintained, but the system requires significant engineer expertise and cannot scale automatically
Solution Approach 1:
The patent implements self-service through automatic tuple significance computation and anomaly detection algorithms that do not require manual expert intervention. The system autonomously evaluates feature combinations, computes statistical significance, identifies outliers, and generates insights, thereby maintaining analysis accuracy while eliminating the need for significant engineer expertise in operation.
Solution Approach 2:
The patent replaces manual expert analysis (mechanical human cognition) with automated computational algorithms. Statistical significance tests, tuple scoring mechanisms, and anomaly detection algorithms substitute for human expert judgment, enabling the system to maintain high analysis accuracy while operating automatically without requiring engineer expertise for each analysis task.
3Productivity
If thresholding is applied to reduce tuple numbers, then processing efficiency improves, but some statistically insignificant tuples may be lost
Solution Approach 1:
The patent changes the thresholding parameter from arbitrary fixed values to statistically computed significance thresholds. By using p-values, confidence intervals, and significance scores derived from the data itself, the system dynamically adjusts thresholds to retain tuples that are statistically meaningful while filtering out noise, thereby improving processing efficiency without losing potentially significant patterns.
Solution Approach 2:
The patent incorporates feedback through iterative significance evaluation where the system computes tuple importance based on statistical criteria, applies thresholding, evaluates the results, and adjusts thresholds if needed. This feedback loop ensures that thresholding retains statistically significant patterns while filtering insignificant ones, balancing processing efficiency with information retention.
Data Source
AI summary
The technology disclosed relates to automatic generation of tuples from a record set for outlier analysis. Applying this new technology, user need not specify which 1-tuples to combine into n-tuples. The tuples are generated from structured records organized into features (that also could be fields, objects or attributes.) Tuples are generated from combinations of feature values in the records. Thresholding is applied to manage the number of tuples generated. The technology disclosed further relates to indexing and searching high dimensional tuple spaces in a computer-implemented system.