Predictive Modeling for Storage Diversion Anomaly Detection
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Solution Overview
Problem
The exponential growth of data in various environments leads to inefficiencies as much of it is ignored or not effectively utilized, creating undesirable outcomes due to the time required for sorting and analysis.
Innovation Solution
A system and method for aggregating data from disparate sources using a transformative processing engine that integrates, processes, and stores data from various components and user devices, enabling predictive modeling to identify anomaly events by analyzing data attributes from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is stored and evaluated, then data availability is improved, but time required for sorting and analysis increases
Solution Approach 1:
The system performs preliminary data aggregation and transformation before analysis is needed. By pre-processing data from multiple sources and organizing it in a unified format, the system eliminates the need for time-consuming sorting and analysis operations when actual analysis is required, thus resolving the contradiction between data availability and analysis time.
2Productivity
If data from multiple sources is aggregated, then data utilization is improved, but system complexity increases
Solution Approach 1:
The system employs a universal data transformation layer that can handle multiple data sources and formats through a single unified interface. This multi-functional approach allows the system to aggregate and process data from diverse sources without requiring separate processing mechanisms for each source type, thereby improving data utilization while controlling system complexity.
Solution Approach 2:
The system introduces an intermediary data transformation layer that acts as a mediator between disparate data sources and the analysis engine. This intermediary layer standardizes data formats and structures, enabling efficient aggregation from multiple sources while isolating the complexity of data heterogeneity from the core analysis functionality.
3Productivity
If data is transformed into unified format, then analysis efficiency is improved, but processing requirements increase
Solution Approach 1:
The system applies partial transformation rather than complete reformatting of all data. By transforming only the necessary portions of data into a unified format and maintaining the rest in their original forms where possible, the system achieves analysis efficiency while minimizing the processing energy and resources required.
Data Source
AI summary
Approaches are provided for a prediction model determining a prediction indicative of the authorized user being associated with at least one anomaly event. In one example, a computer system may receive data from different sources. For example, the system may receive data associated with use of one or more monitored units of an automated storage and retrieval location. The system may also receive request data associated with a request for execution of the monitored controlled unit by an authorized user to a target user. The prediction model of the system may utilize the received data to determine the prediction that the authorized user is associated with at least one anomaly event associated with a diversion of a monitored unit away from the target user. The system may then provide the prediction for presentation.