Inferential Data Mining System for Log Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data analytics and visualization methods face challenges in efficiently processing random log and dump data due to the nature of the data and lack of interfaces providing intelligence for proper processing, leading to inefficient data mining and failure in capturing correct correlations between different data sets.
Innovation Solution
A computer-implemented method and system that generates inference reports for a predetermined dataset using a learning module, which cleanses the data with dictionaries, prioritizes, classifies, groups, and compares to identify insights, ultimately matching them to a solution dictionary for domain-based and service-based suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional single-centric mining methodologies are used, then the processing is simpler, but the ability to capture correct correlations between different data sets is lost
Solution Approach 1:
The patent segments the data mining process into multiple specialized modules: learning module for pattern recognition, cleaning module for data preprocessing, priority mapping module for importance assessment, classifying module for categorization, grouping module for clustering, comparing module for correlation analysis, and report generation module for output. This segmentation allows each module to focus on specific aspects of data analysis, improving correlation accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system dynamically adapts its processing approach based on the characteristics of the input data. The learning module continuously learns from data patterns, and the system adjusts its analysis depth, cleaning intensity, and correlation methods according to the specific requirements of each dataset, enabling reliable multi-perspective analysis without fixed complexity constraints.
2Ease of operation
If static capability is applied to create interpretations and inferences, then the processing is faster, but clarity on end user expectations is not provided
Solution Approach 1:
The system incorporates feedback mechanisms where the learning module continuously refines its understanding based on data patterns and user interactions. The comparing module provides feedback on correlation strengths, and the report generation module adapts its output based on identified insights, ensuring that the final results align with end-user expectations while maintaining efficient processing through learned patterns.
Solution Approach 2:
The cleaning module performs preliminary data preprocessing and the learning module conducts initial pattern recognition before the main analysis. This preliminary action prepares the data in advance, reducing the time required for subsequent interpretation steps while ensuring that the final output meets user expectations through pre-established data quality and pattern frameworks.
3Productivity
If efficient processing of random log and dump data is attempted, then the data analysis speed increases, but the lack of intelligence interface causes processing failures
Solution Approach 1:
The learning module acts as an intelligent intermediary between the raw random log and dump data and the analysis processes. It learns the specific patterns and characteristics of the input data format, translating unstructured data into a processed form that subsequent modules can efficiently analyze. This intermediary layer enables fast processing of random data formats while maintaining reliability through learned data understanding.
Solution Approach 2:
The cleaning module performs preliminary data validation and standardization on random log and dump data before they enter the main processing pipeline. This preliminary action identifies and corrects format inconsistencies, ensuring that subsequent high-speed processing operations work with reliable, standardized data, thus maintaining processing reliability at high speeds.
Data Source
AI summary
A system and method for inferential mining comprising a learning module to receive a predetermined dataset for generating at least one inference report and clean the received dataset using a cleaning dictionary and anthology dictionary to generate a cleansed data, a priority mapping module to associate a priority with each of the cleansed data, a classifying module to classify each of the cleansed data in a plurality of buckets, a grouping module to group each of the plurality of buckets to generate all combinations of each of the cleansed data in each of the plurality of buckets, a comparing module to compare the generated all possible combinations of each of the cleansed data to a clustering dictionary to identify insights associated with the cleansed data and a report generation module configured to generate an inference report for identified insights by matching the identified insights to a solution dictionary.