Automated IT Service Desk Systems Using Machine Learning Techniques

Paramesh, S. P.; Shreedhara, K. S.

doi:10.1007/978-981-13-2514-4_28

Cited by 20 publications

(16 citation statements)

References 13 publications

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“…It is worth mentioning that ITIL is widely used, having been ranked in the ten top-paying IT certifications for 2020 based on the survey conducted in the USA (Global Knowledge, 2020). Moreover, managing IT tickets, in general, remains a crucial concern for the IT service industry (Paramesh and Shreedhara, 2019).…”

Section: Business Process Complexitymentioning

confidence: 99%

Assessing business process complexity based on textual data: Evidence from ITIL IT ticket processing

Rizun

Revina

Maister

2021

BPMJ

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PurposeThis study aims to draw the attention of business process management (BPM) research and practice to the textual data generated in the processes and the potential of meaningful insights extraction. The authors apply standard natural language processing (NLP) approaches to gain valuable knowledge in the form of business process (BP) complexity concept suggested in the study. It is built on the objective, subjective and meta-knowledge extracted from the BP textual data and encompassing semantics, syntax and stylistics. As a result, the authors aim to create awareness about cognitive, attention and reading efforts forming the textual data-based BP complexity. The concept serves as a basis for the development of various decision-support solutions for BP workers.Design/methodology/approachThe starting point is an investigation of the complexity concept in the BPM literature to develop an understanding of the related complexity research and to put the textual data-based BP complexity in its context. Afterward, utilizing the linguistic foundations and the theory of situation awareness (SA), the concept is empirically developed and evaluated in a real-world application case using qualitative interview-based and quantitative data-based methods.FindingsIn the practical, real-world application, the authors confirmed that BP textual data could be used to predict BP complexity from the semantic, syntactic and stylistic viewpoints. The authors were able to prove the value of this knowledge about the BP complexity formed based on the (1) professional contextual experience of the BP worker enriched by the awareness of cognitive efforts required for BP execution (objective knowledge), (2) business emotions enriched by attention efforts (subjective knowledge) and (3) quality of the text, i.e. professionalism, expertise and stress level of the text author, enriched by reading efforts (meta-knowledge). In particular, the BP complexity concept has been applied to an industrial example of Information Technology Infrastructure Library (ITIL) change management (CHM) Information Technology (IT) ticket processing. The authors used IT ticket texts from two samples of 28,157 and 4,625 tickets as the basis for the analysis. The authors evaluated the concept with the help of manually labeled tickets and a rule-based approach using historical ticket execution data. Having a recommendation character, the results showed to be useful in creating awareness regarding cognitive, attention and reading efforts for ITIL CHM BP workers coordinating the IT ticket processing.Originality/valueWhile aiming to draw attention to those valuable insights inherent in BP textual data, the authors propose an unconventional approach to BP complexity definition through the lens of textual data. Hereby, the authors address the challenges specified by BPM researchers, i.e. focus on semantics in the development of vocabularies and organization- and sector-specific adaptation of standard NLP techniques.

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Section: Business Process Complexitymentioning

confidence: 99%

Assessing business process complexity based on textual data: Evidence from ITIL IT ticket processing

Rizun

Revina

Maister

2021

BPMJ

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“…Under the assumption that is a small constant, the complexity of distance computations is ( 2 ). 2 We use the O(…) notation of Complexity Theory…”

Section: C: Meta-knowledge Aspectmentioning

confidence: 99%

“…These developments reveal a fundamental role of IT support systems in any organization's support operations. Two essential steps of any IT ticket processing, which are their correct prioritization and assignment, keep getting the attention of practitioners and the scientific community, especially in the context of the everincreasing amount of tickets, errors, long lead times, and lack of human resources [2]- [7]. While small companies still tend to perform these steps manually, large organizations dedicate large budgets to the implementation of various commercial text classification solutions.…”

Section: Introductionmentioning

confidence: 99%

IT Ticket Classification: The Simpler, the Better

2020

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Recently, automatic classification of IT tickets has gained notable attention due to the increasing complexity of IT services deployed in enterprises. There are multiple discussions and no general opinion in the research and practitioners' community on the design of IT ticket classification tasks, specifically the choice of ticket text representation techniques and classification algorithms. Our study aims to investigate the core design elements of a typical IT ticket text classification pipeline. In particular, we compare the performance of TF-IDF and linguistic features-based text representations designed for ticket complexity prediction. We apply various classifiers, including kNN, its enhanced versions, decision trees, naïve Bayes, logistic regression, support vector machines, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. Finally, we discuss the evaluation results and their practical implications. As our study shows, linguistic representation not only proves to be highly explainable but also demonstrates a substantial prediction quality increase over TF-IDF. Furthermore, our experiments evidence the importance of feature selection. We indicate that even simple algorithms can deliver high-quality prediction when using appropriate linguistic features.

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“…In their survey, Kamran and other researchers analyzed text feature extractions [9], [10], dimentionality reduction methods, existing algorithms and techniques, evaluation methods and limitations [7] and advantages based on applications. Paramesh et al and Seongwook et al compare the different classification algorithms such as multinomial naive bayes logistic regression, K-Nearest neighbour and Support Vector Machines (SVM) on real-world IT infrastructure ticket classifier system data, using different evaluation metrics in their research [11], [12]. They claimed that SVM to have performed well on all the data samples.…”

Section: Related Workmentioning

confidence: 99%

Corporate IT-Support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach

Shanmugalingam

Chandrasekara

Hindle

et al. 2019

2019 Digital Image Computing: Techniques and Applications (DICTA)

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Comprehensive IT support teams in large scale organizations require more man power for handling engagement and requests of employees from different channels on a 24×7 basis. Automated email technical queries help desk is proposed to have instant real-time quick solutions and email categorisation. Email topic modelling with various machine learning, deeplearning approaches are compared with different features for a scalable, generalised solution along with sure-shot static rules. Email's title, body, attachment, OCR text, and some feature engineered custom features are given as input elements. XGBoost cascaded hierarchical models, Bi-LSTM model with word embeddings perform well showing 77.3 overall accuracy For the real world corporate email data set. By introducing the thresholding techniques, the overall automation system architecture provides 85.6 percentage of accuracy for real world corporate emails. Combination of quick fixes, static rules, ML categorization as a low cost inference solution reduces 81 percentage of the human effort in the process of automation and real time implementation.

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Automated IT Service Desk Systems Using Machine Learning Techniques

Cited by 20 publications

References 13 publications

Assessing business process complexity based on textual data: Evidence from ITIL IT ticket processing

Assessing business process complexity based on textual data: Evidence from ITIL IT ticket processing

IT Ticket Classification: The Simpler, the Better

Corporate IT-Support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach

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