Abstract
Rule-based techniques are gaining importance with their ability to augment large scale data processing systems. However, there still remain key challenges amongst current rule-based techniques, including rule monitoring, adapting and evaluation. Among these challenges, monitoring the precision of rules is highly important as it enables analysts to maintain the accuracy of a rule-based system. In this paper, we propose an Adaptive Rule Monitoring System (ARMS) for monitoring the precision of rules. The approach employs a combination of machine learning and crowdsourcing techniques. ARMS identifies rules deteriorating the performance of a rule based system, using the feedback receives from the crowd. To enable analysts identifying the imprecise rules, ARMS leverage machine learning algorithms to analyze the crowd's feedback. The evaluation results show that ARMS can identify the imprecise rules more successfully compared to the default practice of the system, which rely exclusively on analysts.
Original language | English |
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Title of host publication | SE4COG 2018 Proceedings of the 1st International Workshop on Software Engineering for Cognitive Services |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 45-51 |
Number of pages | 7 |
ISBN (Print) | 9781450357401 |
DOIs | |
Publication status | Published - 28 May 2018 |
Event | ACM/IEEE 1st International Workshop on Software Engineering for Cognitive Services, SE4COG 2018, co-located with the 40th International Conference on Software Engineering, ICSE 2018 - Gothenburg, Sweden Duration: 28 May 2018 → 29 May 2018 |
Conference
Conference | ACM/IEEE 1st International Workshop on Software Engineering for Cognitive Services, SE4COG 2018, co-located with the 40th International Conference on Software Engineering, ICSE 2018 |
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Country/Territory | Sweden |
City | Gothenburg |
Period | 28/05/18 → 29/05/18 |
Keywords
- multi-armed-bandit algorithm
- rule based systems
- rule monitoring system