Improving Support Ticket Systems Using Machine Learning: A Literature Review

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

A multi-level approach for hierarchical Ticket Classification

The automatic categorization of support tickets is a fundamental tool for modern businesses. Such requests are most commonly composed of concise textual descriptions that are noisy and filled with technical jargon. In this paper, we test the effectiveness of pre-trained LMs for the classification of issues related to software bugs. First, we test several strategies to produce single, ticket-wise representations starting from their BERT-generated word embeddings. Then, we showcase a simple yet effective way to build a multi-level classifier for the categorization of documents with two hierarchically dependent labels. We experiment on a public bugs dataset and compare our results with standard BERT-based and traditional SVM classifiers. Our findings suggest that both embedding strategies and hierarchical label dependencies considerably impact classification accuracy

Machine Learning para Automatizar los Sistemas de Tickets de Soporte: Una Revisión de Literatura

El sistema de tickets de soporte a día de hoy, es un elemento clave para cualquier empresa. La clasificación de estos tickets puede ser una tarea compleja si se realiza manualmente, debido a que la asignación puede ser incorrecta y esto conducir a una reasignación de tickets, utilización innecesaria de recursos y extensión del tiempo de resolución.El avance de la tecnología Machine Learning aplicado en los sistemas de tickets de soporte ha logrado automatizar la clasificación de los tickets, esperando así una mejor asignación de los incidentes a resolver. Este artículo tiene como objetivo encontrar los algoritmos, basados en la tecnología Machine Learning, que obtengan una mayor tasa de precisión en la clasificación de tickets de soporte en la gestión de incidencias de ITIL

Organizational preparedness for the use of large language models in pathology informatics

In this paper, we consider the current and potential role of the latest generation of Large Language Models (LLMs) in medical informatics, particularly within the realms of clinical and anatomic pathology. We aim to provide a thorough understanding of the considerations that arise when employing LLMs in healthcare settings, such as determining appropriate use cases and evaluating the advantages and limitations of these models. Furthermore, this paper will consider the infrastructural and organizational requirements necessary for the successful implementation and utilization of LLMs in healthcare environments. We will discuss the importance of addressing education, security, bias, and privacy concerns associated with LLMs in clinical informatics, as well as the need for a robust framework to overcome regulatory, compliance, and legal challenges

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

Palveluohjauksen prosessien kehitys tietotekniikan palveluyhtiössä

Nopea digitaalinen muutos, uudet teknologiat sekä innovaatiot, muovaa tällä hetkellä monia perinteisiä toimialoja. Monilla perinteisilläkin aloilla se, mikä oli ennen mahdollisuus hakea kilpailullista etua muihin alan toimijoihin nähden, on nyt välttämättömyys alalla selviämiseen. Yhä use ampien palveluiden ja tehtävien siirtyessä vähintään osittain tietotekniikan varaan, myös vaati mukset perustietotekniikan saavutettavuudelle ovat kasvaneet, esimerkiksi valmistavassa teollisuudessa lyhyetkin katkokset tuotantokriittisillä palvelimilla voivat pahimmassa tapauksessa aiheuttaa tuotannon alasajon. Samalla monet organisaatiot ovat ulkoistaneet tietoteknisistä laitteista ja infrastruktuurista huolehtimisen ulkoisille tietoteknisiä palveluita tarjoaville yrityksille, organisaation itse keskittyessä ydinliiketoimintaansa. Laitteiden ja palveluiden saatavuuden toteutumisesta sovitaan organisaatioiden välillä yleisesti palvelutasosopimuksilla, joissa sovitaan muun muassa siitä, miten nopeasti työpyynnöt tulisi ratkaista. Nopea saapuvaan työpyyntöön reagointi ja työpyynnön ratkaisu ovat paitsi kilpailutekijöitä, myös ylläpitää asiakastyytyväisyyttä. Työpyyntöjen nopeassa ratkaisussa ja palvelutasosopimusten vaatimusten täyttymisessä palveluohjaus on keskeisessä roolissa. Palveluohjaaja luokittelee saapuvan työpyynnön, priorisoi ja ohjaa eteenpäin oikealle ratkaisutiimille. Asiakasmäärien kasvaessa työpyyntöjen määrä kuitenkin kasvaa samalla, jolloin palveluohjaukseen kohdistuva kuormitus kasvaa. Uudet teknologiat tarjoavat kuitenkin helpotusta tähän ongelmaan. Ohjelmistorobotiikan avulla voidaan automatisoida tietyin reunaehdoin monia toisteisia manuaalisia tehtäviä, jonka jäljiltä työntekijät voivat keskittyä organisaatiolle arvokkaampaan tietotyöhön. Kun ihmisten tuottavuutta voidaan nostaa vain tiettyyn pisteeseen asti, on luonnollista siirtää osa tehtävistä automaation hoidettavaksi. Tämän laadullisen tapaustutkimuksen tarkoituksena oli tarkastella kohdeorganisaation palveluohjauksen nykytilaa, tunnistaa mahdollisia kehityskohteita sekä luoda näihin kehitysideoita, joiden pohjalta palveluohjaajien työkuormaa voidaan keventää kuitenkin asiakastyytyväisyys säilyttäen. Lisäksi organisaation erillistoiveesta tässä työssä tarkasteltiin myös eri palveluohjauksen prosessien ja tehtävien ohjelmistorobotiikkapotentiaalia. Tämä työ kattaa kirjallisen osuuden, joka toteutettiin kriittisenä kirjallisuuskatsauksena. Kirjallisuuskatsauksessa luotiin yleiskuva sekä palveluohjauksesta, että ohjelmistorobotiikkaan. Palveluohjausta lähestyttiin palvelunhallinnan viitekehysten kautta ja ohjelmistorobotiikasta selvitettiin yleisesti toimintaperiaatetta, käyttöönoton edellytyksiä sekä käyttöönotossa huomioitavia asioita. Empiirisessä osuudessa palveluohjaajien työtä havainnoitiin kehityskohteiden tunnistamiseksi, jonka lisäksi aineistoon haettiin rikkautta teemahaastatteluiden avulla. Teemahaastatteluiden tarkoituksena oli selvittää palveluohjaajien näkemyksiä työstään sekä tunnistaa organisaation ohjelmistorobotiikkakyvykkyydet. Kerätyn aineiston pohjalta tunnistettiin palveluohjauksen prosesseihin liittyvät kehityskohteet sekä laadittiin näille kehitystoimenpiteitä kolmen eri aikahorisontin kautta. Kehityskohteissa käsiteltiin lisäksi tunnistettujen prosessien automatisointipotentiaalia ja mahdollisia toteutuksia

Cloud service data collection for cloud service selection

Data collection for cloud DSS tools is a huge challenge not only because of the lack of integration of quality of experience with existing cloud data but also by not having a holistic view of security characteristics in cloud. We solve it by using crowdsourcing techniques&providing a security V too

Developing a Community: Qualitative Approaches to Understanding the Role of Community Engagement in Gameswork

Through multiple qualitative approaches, this dissertation contributes to understanding the increased role of addressing, engaging, and managing online communities in gameswork. It pays particular attention to how individual actors – such as game developers, content creators, community managers, and game journalists – collectively react to shifting industry trends that prioritize community engagement and building. It contributes to the literature on games by highlighting the experiences and perspectives of those working within the industry – such as community managers and game developers – as their industry undergoes significant shifts in priorities. In addition, it contributes to media and platform studies by examining the impacts on the production and consumption of media when audiences demand more intimate and direct access to creators. It pays specific attention to the workers who act as the filter between those who produce and those who consume. This dissertation draws together four individual projects with distinct methodologies, research partners, and questions to illustrate the impacts of this shift. Chapter 2 examines critical games journalism to show how a lack of investment in community engagement leads to a breakdown of the community. Chapter 3 uses qualitative interviews and observation of drag content creators to show how they grapple with building their online communities amidst changing platform dynamics. Chapter 4 uses qualitative interviews with game developers to highlight how they choose to or choose not to work with content creators as they adapt to new priorities in their industry. Chapter 5 uses qualitative interviews with community managers to examine how their work has changed, continues to change, and leaves lingering anxieties and questions about the future of their work. These individual projects are tied together through the complementary theme of servitization (Vandermerwe & Rada, 1988; Weststar & Dubois, 2022), which captures the trend of traditionally individually produced, packaged, and consumed products moving to a system of continuous access and consumption. As gameswork produces more products designed as a service for consumers, it changes the needs and expectations of gaming communities. I argue that this increased emphasis on community changes priorities for those working within creative and cultural industries that have implications for developers, community managers, and players. As these priorities change, new concerns arise regarding the working conditions, career, and educational pathways for those in community-focused roles

Automated network optimisation using data mining as support for economic decision systems

The evolution from wired voice communications to wireless and cloud computing services has led to the rapid growth of wireless communication companies attempting to meet consumer needs. While these companies have generally been able to achieve quality of service (QoS) high enough to meet most consumer demands, the recent growth in data hungry services in addition to wireless voice communication, has placed significant stress on the infrastructure and begun to translate into increased QoS issues. As a result, wireless providers are finding difficulty to meet demand and dealing with an overwhelming volume of mobile data. Many telecommunication service providers have turned to data analytics techniques to discover hidden insights for fraud detection, customer churn detection and credit risk analysis. However, most are illequipped to prioritise expansion decisions and optimise network faults and costs to ensure customer satisfaction and optimal profitability. The contribution of this thesis in the decision-making process is significant as it initially proposes a network optimisation scheme using data mining algorithms to develop a monitoring framework capable of troubleshooting network faults while optimising costs based on financial evaluations. All the data mining experiments contribute to the development of a super–framework that has been tested using real-data to demonstrate that data mining techniques play a crucial role in the prediction of network optimisation actions. Finally, the insights extracted from the super-framework demonstrate that machine learning mechanisms can draw out promising solutions for network optimisation decisions, customer segmentation, customers churn prediction and also in revenue management. The outputs of the thesis seek to help wireless providers to determine the QoS factors that should be addressed for an efficient network optimisation plan and also presents the academic contribution of this research