Rule-based machine learning for knowledge discovering in weather data

Coulibaly, Lassana; Kamsu-Foguem, Bernard; Tangara, Fana

doi:10.1016/j.future.2020.03.012

Cited by 19 publications

(9 citation statements)

References 37 publications

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“… Rule-based engine A rule-based model allows domain experts to specify heuristics. This is in line with the emerging field of weak supervision [ 15 , 66 ], which enables domain experts to specify mostly valid rules [ 32 ]. Contextual bandits engine Contextual bandit engines can handle the cold-start problem very well [ 89 ], but they are also data-driven and can detect new patterns early [ 2 ].…”

Section: Methodssupporting

confidence: 61%

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Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

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Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.

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Section: Methodssupporting

confidence: 61%

Section: Methodssupporting

confidence: 61%

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

View full text Add to dashboard Cite

show abstract

“…Moreover, while our approach considers rules provided by the DevOps as the primary input, techniques such as association rules [40,41] have been implemented, allowing the discovery of new rules, based on the received monitoring data [42]. These statements can subsequently be used to automatically create new rules which would have helped the application to improve its response, had they been introduced in the past.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Severity: a QoS-aware approach to cloud application elasticity

et al. 2021

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While a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The feasibility of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.

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“…Moreover, while our approach considers rules provided by the DevOps as the primary input, techniques such as association rules [33], [34] have been implemented, allowing the discovery of new rules, based on the received monitoring data [35]. These statements can subsequently be used to automatically create new rules which would have helped the application to improve its response, had they been introduced in the past.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Severity: A QoS-aware approach to cloud application elasticity

Τσαγκαρόπουλος

Verginadis

Papageorgiou

et al. 2021

Preprint

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While a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The efficacy of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.

show abstract

Rule-based machine learning for knowledge discovering in weather data

Cited by 19 publications

References 37 publications

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Severity: a QoS-aware approach to cloud application elasticity

Severity: A QoS-aware approach to cloud application elasticity

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