Categorical Format Drift Detection With Distribution Analysis
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Solution Overview
Problem
The challenge of managing and analyzing vast amounts of diverse machine data in IT environments, including detecting data drift and maintaining data system operability, is exacerbated by the increasing availability of inexpensive storage, which leads to the need for efficient data retention and analysis strategies.
Innovation Solution
A data intake and query system utilizing a late-binding schema and flexible extraction rules to process, index, and store machine data, enabling field-searchable events and real-time query capabilities, with components for drift detection and anomaly analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If massive quantities of raw data are stored for later retrieval and analysis, then data analysis flexibility is improved, but data management complexity increases
Solution Approach 1:
The patent segments raw data into structured events with defined schemas during ingestion. Each event is parsed into discrete fields with data types, allowing the system to manage complexity through structured organization while preserving access to all original data for flexible analysis.
Solution Approach 2:
The system performs preliminary data processing and schema validation at ingestion time rather than at query time. This preliminary action structures the data upfront, reducing management complexity during storage and retrieval while maintaining analytical flexibility.
2Reliability
If data drift detection is implemented to monitor data system health, then system reliability is improved, but detection precision requirements increase complexity
Solution Approach 1:
The patent implements feedback mechanisms where detected data drift automatically triggers alerts and can modify system behavior. The system continuously monitors incoming data against expected schemas and provides feedback when deviations occur, improving reliability through automated detection and response.
Solution Approach 2:
The system performs self-diagnosis by automatically detecting data drift and identifying anomalies without requiring external intervention. The drift detection subsystem autonomously monitors data quality and notifies administrators, reducing the operational burden while maintaining high reliability.
Data Source
AI summary
A computerized method for detection of format drift and format anomalies is described. A format representation for each data point of a first data sample is extracted. Transformations of each format representation is conducted, resulting in a first plurality of count values (reference) and a second plurality of count values. Each count value identifies a number of occurrences of a transformed format representation within that data sample. Thereafter, a first probability distribution for the first plurality of count values and a second probability distribution for the second plurality of count values are computed. Analytics using the first and probability distributions are conducted to produce a first metric. A format drift is determined based on an evaluation of the first metric to a second metric operating as a threshold metric. Format anomalies are detected based on analytics of hashed format representation and determination of infrequent usage of a particular format representation.