Data Ingestion Layer Converts Non-Time Series Data for Anomaly Detection
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Solution Overview
Problem
Existing anomaly detection systems are ineffective in identifying anomalies in non-time series data, as they rely on time-based patterns and are not designed to handle data without temporal information.
Innovation Solution
A data ingestion layer is introduced to convert non-time series data into time series data by appending time information, allowing existing time-based anomaly detection models to identify anomalies across various metrics and attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time-based anomaly detection models are used, then anomaly detection capability is improved for time series data, but the system cannot detect anomalies in non-time series data
Solution Approach 1:
A data ingestion layer is introduced as an intermediary component that converts non-time series data into time series format by appending time information. This mediator enables existing time-based anomaly detection models to process diverse data types without modifying the core detection algorithms, thus improving adaptability while maintaining detection reliability.
Solution Approach 2:
The system changes the temporal parameters of non-time series data by adding time stamps and organizing data into time-based windows or batches. This parameter transformation allows the data to be processed by time-series aware anomaly detection models, expanding the system's capability to handle various data formats.
2Adaptability or versatility
If non-time series data is converted to time series data by appending time information, then anomaly detection capability is improved, but data processing complexity increases
Solution Approach 1:
The data processing pipeline is segmented into distinct functional layers: a data ingestion layer that handles conversion and a separate anomaly detection layer that performs analysis. This segmentation allows the complexity of time-series conversion to be isolated in one layer while keeping the detection logic simple and reusable, thus managing overall system complexity.
Solution Approach 2:
The data ingestion layer automatically detects the type of incoming data and applies appropriate conversion logic without requiring manual intervention. The system self-adjusts based on data characteristics, reducing the operational complexity for users while maintaining robust processing capabilities.
Data Source
AI summary
Systems and methods are described for to detecting anomalies in various forms of data, including non-time series data. In one example, a data ingestion interval for customer data may be determined, where the data ingestion interval specifies a frequency at which data is analyzed to detect analogies in portions of the data corresponding to time windows. A portion of the customer data may then be obtained and aggregated from a data source according to the data ingestion interval. The portion of data may be converted into time series data by appending a time stamp corresponding to the time window of the portion of the data. The anomaly detection service may then process the time series data, using a time series data anomaly model, to detect one or more anomalies in the time series data.