In-Memory Database Engine for Early Emerging Issue Detection
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Solution Overview
Problem
Existing technologies lack efficient methods for early identification and resolution of emerging issues in machines, leading to potential downtime and increased costs, as they struggle to analyze vast amounts of data effectively.
Innovation Solution
A computer-implemented method using an in-memory database engine that performs structured exploration by applying specialized information sources and filter criteria to machine data sets, creating evidence packages for future reference and insight into emerging issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional data analysis methods are used to analyze machine data sets, then the system can process data, but it cannot efficiently identify emerging issues early enough to prevent downtime and reduce costs
Solution Approach 1:
The system performs preliminary actions by proactively analyzing machine data sets before issues manifest as failures. The in-memory database engine continuously monitors and analyzes data patterns, applying specialized information sources and filter criteria to identify emerging issues early, enabling preventive maintenance before actual machine downtime occurs.
Solution Approach 2:
The system segments the data analysis process into structured exploration levels, where each level applies specific specialized information sources and filter criteria to portions of the machine data set. This segmentation allows the system to handle vast amounts of data systematically through multiple manageable analysis layers, improving both speed and effectiveness of issue identification.
2Difficulty of detecting and measuring
If the system analyzes vast amounts of machine data to identify emerging issues, then detection capability improves, but the complexity of data processing increases
Solution Approach 1:
The in-memory database engine serves as an intermediary between the raw machine data and the analysis process. It provides a structured framework with specialized information sources and filter criteria that mediate the complexity of processing vast data sets, making the detection capability more manageable while maintaining high analytical power.
Solution Approach 2:
The system adds dimensional structure to data analysis by organizing it into multiple exploration levels with different specialized information sources and filter criteria. This dimensional organization transforms the complexity of analyzing vast data into a structured multi-level process, improving detection capability while managing system complexity through systematic categorization.
3Loss of information
If the system stores all exploration results for future reference, then accessibility of historical data improves, but memory consumption increases
Solution Approach 1:
The system extracts only the essential evidence from each exploration level and stores it in the evidence package, rather than storing all raw data and processing results. This extraction approach maintains accessibility of historical exploration data while significantly reducing memory consumption by retaining only the critical information needed for future reference.
Solution Approach 2:
The system applies local quality by storing evidence with specific metadata indicating chains of specialized information sources and filter criteria used at each exploration level. This allows the system to maintain high-quality, accessible historical data while managing memory efficiently by storing only the essential local characteristics of each exploration rather than all raw data.
Data Source
AI summary
Structured exploration of available data provides insight allowing early detection/analysis of emerging issues. An in-memory database engine applies specialized information sources and filter criteria to an original data set to successively produce various exploration levels. Evidence relating to a particular exploration level (e.g., resulting data subset, metadata indicating chains of information sources/filter criteria) are stored at the user's instruction within an evidence package of the in-memory database. Information sources may be licensed from third parties, and may be present in the in-memory database. To improve computer performance, embodiments may delete results of previous exploration levels outside of the evidence package. Evidence from the evidence package may be displayed to afford insight into relationships between data subsets and an emerging issue. One structured exploration references a source of geographic information (e.g., pipeline location) and another source of (tractor) warranty claim information, to correlate tractor location with an emerging fuel pump issue.