Data Lineage Anomaly Detection for Faulty Processor Identification
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Solution Overview
Problem
Existing anomaly detection systems fail to proactively identify anomalous data processors in a lineage stream, leading to downstream errors and faults.
Innovation Solution
A method and system that analyze data processor behavior using historical lineage records to establish baselines and detect deviations in real-time, enabling early identification of anomalous data processors through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time analysis of lineage records is performed to detect anomalies, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the anomaly detection process into distinct functional modules: a lineage stream analyzer that processes individual lineage records, a baseline generator that creates expected behavior profiles, and a deviation detector that compares actual behavior against baselines. This modular segmentation enables real-time anomaly detection while managing system complexity through clear separation of concerns and independent component optimization.
2Measurement precision
If historical lineage records are analyzed to establish baselines, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-computing and storing baseline behavior profiles for each data processor during system initialization or off-peak periods. These baselines capture expected data flow patterns, transformation behaviors, and timing characteristics. When anomalies need to be detected in real-time, the pre-established baselines are immediately available for comparison, eliminating the need to analyze historical records at the moment of detection and thus reducing time loss while maintaining high measurement precision.
3Speed
If deviation detection compares real-time behavior against baselines, then anomaly detection speed is improved, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical or manual comparison processes with automated computational mechanisms. The deviation detector uses algorithmic comparisons between real-time lineage record metrics and pre-established baseline profiles, employing efficient data structures and mathematical operations to rapidly identify anomalies. This substitution of automated computational mechanisms for manual or mechanical analysis enables high-speed anomaly detection while managing processing complexity through optimized algorithms and data representations.
Data Source
AI summary
Systems and methods of the present disclosure enable anomaly detection based on data lineage by using at least one processor to receive initial lineage records that record initial changes in data items at each data processor of a lineage stream. The processor(s) generates a historical distribution as a data processor baseline for a particular data processor based on each change in each data item at the particular data processor. For lineage records in a subsequent time period, the processor(s) identifies subsequent changes in data items at the particular data processor and generates a real-time dynamic distribution to represent a current data processor behavior for the particular data processor based on each change for the subsequent lineage records. The processor(s) determines a deviation between the data processor baseline and the current data processor behavior based on the historical distribution and the real-time dynamic distribution to identify a data processor anomaly.