Mining Text in Incident Repositories: Experiences and Perspectives on Adopting Machine Learning Solutions in Practice

Misra, Janardan; Podder, Sanjay

doi:10.1109/asew.2019.00039

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…In most cases, a practical use case was on hand, such that the authors mainly developed such an artifact for or optimized an existing one in a distinct situation (for example in [1]; [8]; [6]; [29]; or [17]). This of course leads to a problem of generalizing their results as already ascertained by Misra and Podder [27]. The accuracy data presented by those papers should therefore also be read with caution.…”

Section: Incident Management Toolmentioning

confidence: 89%

“…İşcen and Gürbüz [2] x Lo, Tiba [25] x x Mukunthan and Selvakumar [26] x Nayebi, Dicke [6] x x Mandal, Agarwal [11] x Misra and Podder [27] (x) x Palacios, Morillas [28] (x) x Shanmugalingam, Chandrasekara [29] x…”

Section: Sent Pred Othermentioning

confidence: 99%

“…Misra and Podder [27] also stated that theoretical ML knowledge obtained by the scientific literature often has problems in unfolding its full potential when being applied in practical technical solutions, because in reality either training data is not accessible like in experiments or real-world data is more noisy or diverse than polished data for university experiments.…”

Section: Incident Management Toolmentioning

confidence: 99%

See 2 more Smart Citations

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.

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