Features spaces and a learning system for structural-temporal data, and their application on a use case of real-time
Guido Schwenk1, Ben Jochinke2, Klaus-Robert Müller1,3,4
1Machine Learning Group, Technische Universität Berlin, Berlin, Germany.
Plos One
|February 7, 2020
Summary
This study introduces a novel feature space for analyzing real-time communication network data, improving problem detection and prediction accuracy. The new method enhances system dependability and reduces manual analysis costs.
Area of Science:
- Network Engineering
- Data Science
- Communication Systems
Background:
- Real-time communication network quality relies on analyzing monitored data to find problems.
- Existing methods struggle to address both structural and temporal data properties simultaneously.
Purpose of the Study:
- Propose a new feature space for analyzing structural-temporal properties of communication network data.
- Develop a system for automatic detection and prediction of sequence behaviors.
- Evaluate the proposed feature space against existing methods.
Main Methods:
- Developed a novel feature space analyzing both structural and temporal properties.
- Implemented a system for automatic detection and prediction of pre-defined sequence behaviors.
- Evaluated performance on real-world mobile communication data.
Main Results:
- Achieved over 93% precision at 100% recall.
- Demonstrated up to 6.7% higher effective recall than alternatives.
- Outperformed deep learning, kernel learning, and ensemble learning approaches.
Conclusions:
- The proposed feature space effectively addresses structural-temporal properties for improved network analysis.
- The system significantly reduces costs associated with manual data analysis.
- System calibration enhances the reliability of predictions in practical applications.
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