An Emperical Study on Application of Big Data Analytics to Automate Service Desk Business Process

Lo, Dan; Tiba, Karl Kevin; Buciumas, Sergiu; Ziller, Frank

doi:10.1109/compsac.2019.10285

Cited by 4 publications

(7 citation statements)

References 33 publications

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“…Manually labeling thousands of tickets is mostly an unthankful work, therefore it would be nice to apply unsupervised learning solutions for training data labeling. Actually, Lo, Tiba [25] did some pioneer work in this field. Unfortunately, their unsupervised Kmeans and DBSCAN algorithms did not work very well.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, we were able to carve out some general findings:  As in nearly any research in the field of Machine Learning the accessibility and quality of training data importantly influences the outcome of the project [1,3,25]  The metrics precision and recall are by far the most-used metrics for evaluating ML ticket classification tools [5,6,18,36].  Classification tools work more precisely the fewer classes they have to classify to [1,3,25].…”

Section: Specific Topics According To the Special Use Case -The Category Othermentioning

confidence: 95%

“…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%

“… Automate labeling of tickets, either for training or for easier ticket resolution by a support agent [34]  Chatbots [15,33,44]  Spam detection [3]  Performance optimization [24]  Automated analysis of pictures attached to a support ticket [11]  Business/process/text mining for better support system architecture [25,45]  AI explainability in support ticket automating [22]  Ticket resolution time prediction [10]  Automated STSs in context of Internet of Things (IoT) [28]  Using answering bot (Microsoft LUIS) for automated request responses [29] The findings and results in these topics were as diverse as the topics itself. Nevertheless, we were able to carve out some general findings:  As in nearly any research in the field of Machine Learning the accessibility and quality of training data importantly influences the outcome of the project [1,3,25]  The metrics precision and recall are by far the most-used metrics for evaluating ML ticket classification tools [5,6,18,36].  Classification tools work more precisely the fewer classes they have to classify to [1,3,25].…”

Section: Specific Topics According To the Special Use Case -The Category Othermentioning

confidence: 99%

See 3 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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Section: Discussionmentioning

confidence: 99%

Section: Specific Topics According To the Special Use Case -The Category Othermentioning

confidence: 95%

Section: Sent Pred Othermentioning

confidence: 99%

Section: Specific Topics According To the Special Use Case -The Category Othermentioning

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

View full text Add to dashboard Cite

show abstract

“…the ability of using AI to infer useful trends, patterns and opportunities for improving the effectiveness of business processes through the analysis of log data (Zhang et al 2020) 5.2 Human Implications AI can support the digitalisation of Human Resource Management (HRM) in the workplace, influencing methods and environments, ensuring greater activity effectiveness and efficiency both in terms of time and costs, and in the quality of the activity carried out offering itself as a valid ally to human work (Zehir, Karaboğa, and Başar 2020). Further opportunities might be identified in applying AI to Big Data analysis to automate service-desk business process (Lo et al 2019). The continuous evolution of technology and business environments impose continuous challenges for managers who must face the challenge to create knowledge and develop internal skills (De Mauro et al 2018;Gatouillat et al 2018).…”

Section: Business Implicationsmentioning

confidence: 99%

Leveraging Artificial Intelligence in Business: Implications, Applications and Methods

Sestino

Mauro

2021

Technology Analysis & Strategic Management

115

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The concept of Artificial Intelligence (AI) as a business-disruptive technology has developed in academic and professional literature in a chaotic and unstructured manner. This study aims to provide clarity over the phenomenon of business activation of AI by means of a comprehensive and systematic literature review, aimed at suggesting a clear description of what Artificial Intelligence is today. The study analyses a corpus of 3780 contributions through an original combination of two established machine learning algorithms (LDA and hierarchical clustering). The review produced a structured classification of the various streams of current research and a list of promising emerging trends. Results have shed light on six topics attributable to three different themes, namely Implications, Applications and Methods (IAM model). Our analysis could provide researchers and practitioners with a meaningful overview of the body of knowledge and research agenda, to exploit AI as an effective enabler to drive business value.

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Relevance classification on service desk texts using Natural Language Processing

Degasperi,

Cavalieri,

Castro

2023

Preprint

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Service desk systems have a vast and rich base of information, consisting of the history of calls made, which can and should be used as a reference base for subsequent calls. Common search tools, such as keyword searches, prove to be unfeasible in large datasets, in addition to being able to bring results not necessarily related to the problem. “State-of-the-art” techniques exist, but they require high computational and operational costs for their training and use. In this sense, the purpose of this work is to investigate the sensitivity of machine learning algorithms in finding the characteristic defined here as “relevance”: the characteristic of texts with knowledge that can be reused. The motivation is that non-relevant texts can be removed in advance from the database, allowing complex algorithms to be employed in a more condensed database, reducing computational costs. Tests were performed with several combinations between the TF-IDF vectorizer and the word embedding Doc2Vec and the classic classifiers Naive-Bayes, Adaptive Boosting, Random Forest, Stochastic Gradient Descent, Logistic Regression, Support Vector Machine and Light Gradient Boosting Machine, and the classifier TextConvoNet, an architecture based on Convolutional Neural Networks. The TextConvoNet classifier presented the best results, with metrics close to 0.93, showing that the concept is detectable and that the technique is viable for removing non-relevant texts from a database.

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An Emperical Study on Application of Big Data Analytics to Automate Service Desk Business Process

Cited by 4 publications

References 33 publications

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Leveraging Artificial Intelligence in Business: Implications, Applications and Methods

Relevance classification on service desk texts using Natural Language Processing

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