Machine learning in incident categorization automation

IT incident management process requires a correct categorization to attribute incident tickets to the right resolution group and obtain an operational system as quickly as possible, having the lowest possible impact on the business and costumers. In this work, we introduce a module to automatically categorize incident tickets, turning the responsible teams for incident management more productive. This module can be integrated as an extension into an incident ticket system (ITS), which contributes to reduce the time wasted on incident ticket route and reduce the amount of errors on incident categorization. To automate the classification, we use a support vector machine (SVM), obtaining an accuracy of 89%, approximately, on a dataset of real-world incident tickets.info:eu-repo/semantics/acceptedVersio

IT service management for campus environment - practical concerns in implementation

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ITSM automation - Using machine learning to predict incident resolution category

Problem resolution is a key issue in the IT service industry, and it is still difficult for large enterprises to guarantee the service quality of the Incident Management (IM) process because of the difficulty in handling frequent incidents timely, even though IT Service Management (ITSM) standard process have already been established (Zhao & Yang, 2013). In this work, we propose an approach to predict the incident solution category, by exploring and combining the application of natural language processing techniques and machine learning algorithms on a real dataset from a large organization. The tickets contain information across a vast range of subjects from inside the organization with a vocabulary specific to these subjects. By exploring the text-based attributes, our findings show that the full description of an incident is better than the short description and after stop words removal, the use of additional preprocessing techniques and the addition of tickets nominal attributes such as have no impact to the classification performance.info:eu-repo/semantics/acceptedVersio

Automatization of incident categorization

To be able to keep up with the grow of the created incidents quantity in an organization nowadays, there was the need to increase the resources to ensure the management of all incidents. Incident Management is composed by several activities, being one of them, Incident Categorization. Merging Natural Language and Text Mining techniques and Machine Learning algorithms, we propose improve this activity, specifically the Incident Management Process. For that, we propose replace the manual sub-process of Categorization inherent to the Incident Management Process by an automatic sub-process, without any human interaction. The goal of this dissertation is to propose a solution to categorize correctly and automatically the incidents. For that, there are real data provided by a company, which due to privacy questions will not be mention along dissertation. The datasets are composed by incidents correctly categorized, which leverage us to apply supervised learning algorithms. It is supposed to obtain as output a developed method through the merge of Natural Language Processing techniques and classification algorithms with better performance on the data. At the end, the proposed method is assessed comparatively with the current categorization done to conclude if our proposal really improves the Incident Management Process and which are the advantages brought by the automation.De forma a acompanhar o crescimento da quantidade de incidentes criados no diaa-dia de uma organização, houve a necessidade de aumentar a quantidade de recursos, de maneira a assegurar a gestão de todos os incidentes. A gestão de incidentes é composta por várias atividades, sendo uma delas, a categorização de incidentes. Através da junção de técnicas de Linguagem Natural e Processamento de Texto e de Algoritmos de Aprendizagem Automática propomos melhorar esta atividade, especificamente o Processo de Gestão de Incidentes. Para tal, propomos a substituição do subprocesso manual de Categorização inerente ao Processo de Gestão de Incidentes por um subprocesso automatizado, sem qualquer interação humana. A dissertação tem como objetivo propor uma solução para categorizar corretamente e automaticamente incidentes. Para tal, temos dados reais de uma organização, que devido a questões de privacidade não será mencionada ao longo da dissertação. Os datasets são compostos por incidentes corretamente categorizados o que nos leva a aplicar algoritmos de aprendizagem supervisionada. Pretendemos ter como resultado final um método desenvolvido através da junção das diferentes técnicas de Linguagem Natural e dos algoritmos com melhor performance para classificar os dados. No final será avaliado o método proposto comparativamente à categorização que é realizada atualmente, de modo a concluir se a nossa proposta realmente melhora o Processo de Gestão de Incidentes e quais são as vantagens trazidas pela automatização

Automatization of incident resolution

Incident management is a key IT Service Management sub process in every organization as a way to deal with the current volume of tickets created every year. Currently, the resolution process is still extremely human labor intensive. A large number of incidents are not from a new, never seen before problem, they have already been solved in the past and their respective resolution have been previously stored in an Incident Ticket System. Automation of repeatable tasks in IT is an important element of service management and can have a considerable impact in an organization. Using a large real-world database of incident tickets, this dissertation explores a method to automatically propose a suitable resolution for a new ticket using previous tickets’ resolution texts. At its core, the method uses machine learning, natural language parsing, information retrieval and mining. The proposed method explores machine learning models like SVM, Logistic Regression, some neural networks architecture and more, to predict an incident resolution category for a new ticket and a module to automatically retrieve resolution action phrases from tickets using part-of-speech pattern matching. In the experiments performed, 31% to 41% of the tickets from a test set was considered as solved by the proposed method, which considering the yearly volume of tickets represents a significant amount of manpower and resources that could be saved.A Gestão de incidentes é um subprocesso chave da Gestão de Serviços de TI em todas as organizações como uma forma de lidar com o volume atual de tickets criados todos os anos. Atualmente, o processo de resolução ainda exige muito trabalho humano. Um grande número de incidentes não são de um problema novo, nunca visto antes, eles já foram resolvidos no passado e sua respetiva resolução foi previamente armazenada em um Sistema de Ticket de Incidentes. A automação de tarefas repetíveis em TI é um elemento importante do Gestão de Serviços e pode ter um impacto considerável em uma organização. Usando um grande conjunto de dados reais de tickets de incidentes, esta dissertação explora um método para propor automaticamente uma resolução adequada para um novo ticket usando textos de resolução de tickets anteriores. Em sua essência, o método usa aprendizado de máquina, análise de linguagem natural, recuperação de informações e mineração. O método proposto explora modelos de aprendizagem automática como SVM, Regressão Logística, arquitetura de algumas redes neurais e mais, para prever uma categoria de resolução de incidentes para um novo ticket e um módulo para extrair automaticamente ações de resolução de tickets usando padrões de classes gramaticais. Nas experiências realizados, 31% a 41% dos tickets de um conjunto de testes foram considerados como resolvidos pelo método proposto, que considerando o volume anual de tickets representa uma quantidade significativa de mão de obra e recursos que poderiam ser economizados

Business process modelling to improve incident management process

Business process management (BPM) is an approach focused on the continuous improvement of business processes, providing for this a collection of best practices. These best practices enable the redesign of business processes to meet the desired performance. IT service management (ITSM) defines the management of IT operations as a service. There are several ITSM frameworks available, consisting in best practices that propose standardizing these pro- cesses for the respective operations. By adopting these frameworks, organisations can align IT with their business objectives. Therefore, the objective of this research is to understand how BPM can be used to improve of ITSM processes. An exploratory case study in a multinational company based in Lisbon, Portugal, is conducted for the improvement of the time performance of an inci- dent management process. Data were gained through documentation, archival records, interviews and focus groups with a team involved in IT support service. So far, the as-is process was elicited, and respective incongruences clarified. During the next months the authors intend to identify the main problems and simulate the appropriate BPM heuristics to understand the impact in the busi- ness organisation.info:eu-repo/semantics/acceptedVersio

IT-palvelun tietotöiden automatisointi: Koneoppimismalli ohjelmistorobotille

Aging population, legacy systems and pressure on cost savings are all growing problems for modern-day companies. One relief for these problems is to automate business processes and IT-service desk tasks with software robots. The aim of the automation is to reduce employees' growing workload so that they have time to work with the more valuable tasks. One of the most limitating factors of using software robots are that the automated processes must be strictly rule-based and the input data must be highly structured. In business there has been a lot of talk about using machine learning and other AI-techniques for achieving more generic solutions, but the actual results have not yet been publicly recognised. In this thesis it is intended to examine the suitability of machine learning for software robotics by automating company's internal IT-services. The goal is to build a working solution and find a machine learning platform for the company that provides software robotic solutions as a service. The end result is a viable automation solution which uses machine learning model for probability-based decision making. Based on this research it is possible say that there exist synergy benefits between the two technologies, as long as there is a suitable application for them.Ikääntyvä väestö, vanhat järjestelmät ja paine kustannussäästöille ovat nykypäivän yritysten kasvavia ongelmia. Yksi helpotus näihin ongelmiin on liiketoimintaprosessien ja IT-palveluiden automatisointi ohjelmistorobottien avulla. Automatisoinnin pyrkimyksenä on vähentää työntekijöiden kasvavaa työtaakkaa, jotta heillä jää enemmän aikaa liiketoiminnalle tärkeämpien tehtävien hoitoon. Yksi suurimmista ohjelmistorobotiikan käyttöä rajoittavista tekijöistä on se, että automatisoitavien prosessien tulee olla tarkasti sääntöihin perustuvia, sekä hyödynnettävän tiedon tarkkaan jäsenneltyä. Alalla on ollut paljon puhetta koneoppimisen ja tekoälyn hyödyntämisestä geneerisempien ratkaisujen saavuttamiseen, mutta tuloksia näiden projektien onnistumisesta ei ole kantautunut suuren yleisön tietoisuuteen. Tässä työssä on tarkoitus tutkia koneoppimisen soveltuvuutta ohjelmistorobotiikkaan automatisoimalla yrityksen sisäisiä IT-palveluja. Päämääränä on rakentaa toimiva prototyyppi ja löytää koneoppimismalleja hyödyntävä alusta, jota prosessien automatisointia tarjoava yritys voisi alkaa käyttämään osana ratkaisukokonaisuuttaan. Lopputuloksena saatiin toimiva ratkaisu, joka höydyntää koneoppimista todennäköisyyksiin perustuvassa päätöksenteossa. Tutkimuksen avulla voidaan sanoa, että kahden tekniikan välillä on synergiahyötyjä, kunhan niille löydetään sopiva käyttökohde