The topic of this diploma thesis is focused on the analysis of the company's requirements for the selection of a system to support communication with customers - helpdesk. The beginning of the work is devoted to theoretical basics of helpdesk systems such as trouble ticket life cycle or knowledge base system. Furthermore, the use of the helpdesk system is described from the point of view of usage and then from the point of view of the roles that occur when using this system. At the end of the theoretical part are described ECS systems, which the helpdesk system is part of. The practical part describes the company for which this work is processed and its functional requirements for the selected system and systems directly related to it. In addition, 4 available solutions are selected, which include their pros and cons and then are evaluated based on seven criteria. The best solution is recommended to the company for selection. In the end of the work, the course of implementation of the solution chosen by the company is outlined.

Internet Service Providers are companies that deliver services managing a complex network of apparatus and cables. Given the complexity of the network, it often happens that alarms are generated. When a problem within the network occurs, a ticket is issued from an alarm and the company starts to supervise it to manage the situation and solve the problem. This work aims to present how can be designed a system that estimates how much time will the trouble ticket take to be solved. The situation is presented within the context of a real case scenario and takes into consideration how the involved company processes the available information and manages the problem. The achieved result is pursued by the company to deliver the information to the final customer that will be able to understand how much time the problem he is facing is going to take before it will be solved. This work will focus on estimating the resolution time for a subset of all the tickets: those that are classified as low priority network problems. The work started with a study of the company that led to the understanding of the available information about the problem, then it focused on the understanding of the procedure adopted by the company to face the solution. It studies the processes that lie behind the ticket creation, the alarm generation and the human intervention, and it concludes with the design of the proposed solution. The proposed solution leverages the company’s processes to produce a result as valuable as possible given the specific use case. / Internetleverantörer är företag som tillhandahåller tjänster genom att hantera ett komplext nätverk av apparater och kablar. Med tanke på nätets komplexitet händer det ofta att larm genereras. När ett problem i nätverket uppstår utfärdas en biljett från ett larm och företaget börjar övervaka det för att hantera situationen och lösa problemet. Syftet med detta arbete är att presentera hur man kan utforma ett system som uppskattar hur lång tid det kommer att ta att lösa problemet. Situationen presenteras inom ramen för ett verkligt scenario och tar hänsyn till hur det berörda företaget behandlar den tillgängliga informationen och hanterar problemet. Företaget strävar efter att leverera information till slutkunden som kan förstå hur lång tid det kommer att ta innan problemet är löst. Detta arbete kommer att inriktas på att uppskatta lösningstiden för en delmängd av alla biljetter: de som klassificeras som nätproblem med låg prioritet. Arbetet inleddes med en studie av företaget som ledde till att man förstod den tillgängliga informationen om problemet, och sedan fokuserade man på att förstå det förfarande som företaget använde för att lösa problemet. Det studeras vilka processer som ligger bakom skapandet av biljetter, alarmeringen och det mänskliga ingripandet, och det avslutas med utformningen av den föreslagna lösningen. Den föreslagna lösningen utnyttjar företagets processer för att ge ett så värdefullt resultat som möjligt med tanke på det specifika användningsfallet.

Com o crescimento do número e da heterogeneidade dos equipamentos presentes nas atuais redes de computadores, o gerenciamento eficaz destes recursos toma-se crítico. Esta atividade exige dos gerentes de redes a disponibilidade de uma grande quantidade de informações sobre os seus equipamentos, as tecnologias envolvidas e os problemas associados a elas. Sistemas de registro de problemas (trouble ticket systems) tem lido utilizados para armazenar os incidentes ocorridos, servindo como uma memória histórica da rede e acumulando o conhecimento derivado do processo de diagnose e resolução de problemas. Todavia, o crescente número de registros armazenados torna a busca manual nestes sistemas por situações similares ocorridas anteriormente muito morosa e imprecisa. Assim, uma solução apropriada para consolidar a memória histórica das redes é o desenvolvimento de um sistema especialista que utilize o conhecimento armazenado nos sistemas de registro de problemas para propor soluções para um problema corrente. Uma abordagem da Inteligência Artificial que tem atraído enorme atenção nos últimos anos e que pode ser utilizada para tal fim é o raciocínio baseado em casos (casebased reasoning). Este paradigma de raciocínio visa propor soluções para novos problemas através da recuperação de um caso similar ocorrido no passado, cuja solução pode ser reutilizada na nova situação. Além disso, os benefícios deste paradigma incluem a capacidade de aprendizado com a experiência, permitindo que novos problemas sejam incorporados e se tomem disponíveis para use em situações futuras, aumentando com isso o conhecimento presente no sistema. Este trabalho apresenta um sistema que utiliza o paradigma de raciocínio baseado em casos aplicado a um sistema de registro de problemas para propor soluções para um novo problema. Esse sistema foi desenvolvido com o propósito de auxiliar no diagnostico e resolução dos problemas em redes. Os problemas típicos deste domínio, a abordagem adotada e os resultados obtidos com o protótipo construído são descritos. / With the increasing number of computer equipments and their increasing heterogeneity, the efficient management of those resources has become a hard job. This activity demands from the network manager a big amount of expertise on network equipments, technologies involved, and eventual problems that may arise. So far, trouble ticket systems (TTS) have been used to store network problems, working like a network historical memory and accumulating the knowledge derived from the diagnosis and troubleshooting of such problems. However, the increasing number of stored tickets makes the manual search of similar situations very slow and inaccurate in these kind of systems. So, an adequate approach to consolidate the network historic memory is the development of an expert system that uses the knowledge stored in the trouble ticket systems to propose a solution for a current problem. Case-based reasoning (CBR), an approach borrowed from Artificial Intelligence that recently had attracted many researchers attention, may be applied to help diagnosing and troubleshooting networking management problems. This reasoning paradigm proposes solution to new problems by retrieving a similar case occurred in the past, whose solution can be reused in the new situation. Furthermore, the benefits of this paradigm include the experience learning capability, allowing new problems being added and becoming available to use in future situations, expanding the knowledge of the system. This work presents a system that uses case-based reasoning applied to a trouble ticket system to propose solutions for a new problem in the network. This system was developed with the aim of helping the diagnostic and troubleshooting of network problems. It describes the typical problems of this domain, the adopted approach and the results obtained with the prototype built.

Com o crescimento do número e da heterogeneidade dos equipamentos presentes nas atuais redes de computadores, o gerenciamento eficaz destes recursos toma-se crítico. Esta atividade exige dos gerentes de redes a disponibilidade de uma grande quantidade de informações sobre os seus equipamentos, as tecnologias envolvidas e os problemas associados a elas. Sistemas de registro de problemas (trouble ticket systems) tem lido utilizados para armazenar os incidentes ocorridos, servindo como uma memória histórica da rede e acumulando o conhecimento derivado do processo de diagnose e resolução de problemas. Todavia, o crescente número de registros armazenados torna a busca manual nestes sistemas por situações similares ocorridas anteriormente muito morosa e imprecisa. Assim, uma solução apropriada para consolidar a memória histórica das redes é o desenvolvimento de um sistema especialista que utilize o conhecimento armazenado nos sistemas de registro de problemas para propor soluções para um problema corrente. Uma abordagem da Inteligência Artificial que tem atraído enorme atenção nos últimos anos e que pode ser utilizada para tal fim é o raciocínio baseado em casos (casebased reasoning). Este paradigma de raciocínio visa propor soluções para novos problemas através da recuperação de um caso similar ocorrido no passado, cuja solução pode ser reutilizada na nova situação. Além disso, os benefícios deste paradigma incluem a capacidade de aprendizado com a experiência, permitindo que novos problemas sejam incorporados e se tomem disponíveis para use em situações futuras, aumentando com isso o conhecimento presente no sistema. Este trabalho apresenta um sistema que utiliza o paradigma de raciocínio baseado em casos aplicado a um sistema de registro de problemas para propor soluções para um novo problema. Esse sistema foi desenvolvido com o propósito de auxiliar no diagnostico e resolução dos problemas em redes. Os problemas típicos deste domínio, a abordagem adotada e os resultados obtidos com o protótipo construído são descritos. / With the increasing number of computer equipments and their increasing heterogeneity, the efficient management of those resources has become a hard job. This activity demands from the network manager a big amount of expertise on network equipments, technologies involved, and eventual problems that may arise. So far, trouble ticket systems (TTS) have been used to store network problems, working like a network historical memory and accumulating the knowledge derived from the diagnosis and troubleshooting of such problems. However, the increasing number of stored tickets makes the manual search of similar situations very slow and inaccurate in these kind of systems. So, an adequate approach to consolidate the network historic memory is the development of an expert system that uses the knowledge stored in the trouble ticket systems to propose a solution for a current problem. Case-based reasoning (CBR), an approach borrowed from Artificial Intelligence that recently had attracted many researchers attention, may be applied to help diagnosing and troubleshooting networking management problems. This reasoning paradigm proposes solution to new problems by retrieving a similar case occurred in the past, whose solution can be reused in the new situation. Furthermore, the benefits of this paradigm include the experience learning capability, allowing new problems being added and becoming available to use in future situations, expanding the knowledge of the system. This work presents a system that uses case-based reasoning applied to a trouble ticket system to propose solutions for a new problem in the network. This system was developed with the aim of helping the diagnostic and troubleshooting of network problems. It describes the typical problems of this domain, the adopted approach and the results obtained with the prototype built.

Com o crescimento do número e da heterogeneidade dos equipamentos presentes nas atuais redes de computadores, o gerenciamento eficaz destes recursos toma-se crítico. Esta atividade exige dos gerentes de redes a disponibilidade de uma grande quantidade de informações sobre os seus equipamentos, as tecnologias envolvidas e os problemas associados a elas. Sistemas de registro de problemas (trouble ticket systems) tem lido utilizados para armazenar os incidentes ocorridos, servindo como uma memória histórica da rede e acumulando o conhecimento derivado do processo de diagnose e resolução de problemas. Todavia, o crescente número de registros armazenados torna a busca manual nestes sistemas por situações similares ocorridas anteriormente muito morosa e imprecisa. Assim, uma solução apropriada para consolidar a memória histórica das redes é o desenvolvimento de um sistema especialista que utilize o conhecimento armazenado nos sistemas de registro de problemas para propor soluções para um problema corrente. Uma abordagem da Inteligência Artificial que tem atraído enorme atenção nos últimos anos e que pode ser utilizada para tal fim é o raciocínio baseado em casos (casebased reasoning). Este paradigma de raciocínio visa propor soluções para novos problemas através da recuperação de um caso similar ocorrido no passado, cuja solução pode ser reutilizada na nova situação. Além disso, os benefícios deste paradigma incluem a capacidade de aprendizado com a experiência, permitindo que novos problemas sejam incorporados e se tomem disponíveis para use em situações futuras, aumentando com isso o conhecimento presente no sistema. Este trabalho apresenta um sistema que utiliza o paradigma de raciocínio baseado em casos aplicado a um sistema de registro de problemas para propor soluções para um novo problema. Esse sistema foi desenvolvido com o propósito de auxiliar no diagnostico e resolução dos problemas em redes. Os problemas típicos deste domínio, a abordagem adotada e os resultados obtidos com o protótipo construído são descritos. / With the increasing number of computer equipments and their increasing heterogeneity, the efficient management of those resources has become a hard job. This activity demands from the network manager a big amount of expertise on network equipments, technologies involved, and eventual problems that may arise. So far, trouble ticket systems (TTS) have been used to store network problems, working like a network historical memory and accumulating the knowledge derived from the diagnosis and troubleshooting of such problems. However, the increasing number of stored tickets makes the manual search of similar situations very slow and inaccurate in these kind of systems. So, an adequate approach to consolidate the network historic memory is the development of an expert system that uses the knowledge stored in the trouble ticket systems to propose a solution for a current problem. Case-based reasoning (CBR), an approach borrowed from Artificial Intelligence that recently had attracted many researchers attention, may be applied to help diagnosing and troubleshooting networking management problems. This reasoning paradigm proposes solution to new problems by retrieving a similar case occurred in the past, whose solution can be reused in the new situation. Furthermore, the benefits of this paradigm include the experience learning capability, allowing new problems being added and becoming available to use in future situations, expanding the knowledge of the system. This work presents a system that uses case-based reasoning applied to a trouble ticket system to propose solutions for a new problem in the network. This system was developed with the aim of helping the diagnostic and troubleshooting of network problems. It describes the typical problems of this domain, the adopted approach and the results obtained with the prototype built.

Magistriniame darbe nagrinėjamas pakartotinis informacijos ir žinių panaudojimas IT pagalbos tarnybose. Trumpai apžvelgti svarbiausi tokio tipo organizacijų veiklos principai, kurie standartizuojami taikant ITIL metodologiją. Darbo tikslas – sukurti IT trikčių registravimo sistemą, kuri panaudodama sukauptą informaciją apie praeityje įvykusias IT triktis spręstų dabartines problemas. Siekiant šio tikslo pasirinkta taikyti neraiškiąją logiką ir atveju paremtą samprotavimą. Darbe pateikiama neraiškiosios logikos ir atveju paremto samprotavimo teorinė apžvalga ir praktinės pritaikymo galimybės, apžvelgiamos intelektualios IT trikčių registravimo sistemos. Atlikus tyrimą ir įvertinus anksčiau sukurtas intelektualias IT trikčių registravimo sistemas, formuojamas naujosios modelis. Nauja sistema, pritaikiusi atveju paremtą samprotavimą ir neraiškiąją logiką, pateikia galimus IT trikčių sprendimus jos naudotojui, tai yra IT pagalbos tarnybos darbuotojui. Sistema yra nepriklausoma nuo kalbos, nes atskirų atvejų paieška vykdoma taikant neraiškiąją logiką. Darbą sudaro 4 dalys: įvadas, IT organizacijos pagal ITIL samprata, teorinė neraiškiosios logikos ir atveju paremto samprotavimo apžvalga, praktinis neraiškiosios logikos pritaikymas IT trikčių registravime bei išvados ir siūlymai, literatūros sąrašas. Darbo apimtis – 75 p. teksto be priedų, 26 iliustr., 5 lent., 60 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / The final Master thesis analyses the reuse of information and knowledge in IT helpdesk organizations. Briefly are discussed most important activities of such companies, that are standardized according ITIL. The main goal of the Master thesis is development of IT help-desk ticketing tool, which would reuse past information and knowledge in order to resolve current problems. Adaptation of fuzzy logic and case based reasoning is chosen to accomplish this goal. The second part of work contains theoretical information about fuzzy logic and cased based reasoning, practical application of these sciences; also discusses intelligent IT help-desk incident ticketing systems. After the research of scientific articles about intelligent IT help-desk systems and application of fuzzy logic and case based reasoning, the new model of IT trouble tickets system is created. The new system suggests possible solutions of an IT incident. The solution is found among previous incidents using fuzzy logic. Volume of the thesis – 75 p. of text without appendices, 26 figures, 5 tables, 60 bibliographic sources.

This report explores whether machine learning methods such as regression and classification can be used with the goal of estimating the resolution time of trouble tickets in a telecommunications network. Historical trouble ticket data from Telenor were used to train different machine learning models. Three different machine learning classifiers were built: a support vector classifier, a logistic regression classifier and a deep neural network classifier. Three different machine learning regressors were also built: a support vector regressor, a gradient boosted trees regressor and a deep neural network regressor. The results from the different models were compared to determine what machine learning models were suitable for the problem. The most important features for estimating the trouble ticket resolution time were also investigated. Two different prediction scenarios were investigated in this report. The first scenario uses the information available at the time of ticket creation to make a prediction. The second scenario uses the information available after it has been decided whether a technician will be sent to the affected site or not. The conclusion of the work is that it is easier to make a better resolution time estimation in the second scenario compared to the first scenario. The differences in results between the different machine learning models were small. Future work can include more information and data about the underlying root cause of the trouble tickets, more weather data and power outage information in order to make better predictions. A standardised way of recording and logging ticket data is proposed to make a future trouble ticket time estimation easier and reduce the problem of missing data. / Den här rapporten undersöker om maskininlärningsmetoder som regression och klassificering kan användas för att uppskatta hur lång tid det tar att lösa en felanmälan i ett telenät. Data från tidigare felanmälningar användes för att träna olika maskininlärningsmodeller. Tre olika klassificerare byggdes: en support vector-klassificerare, en logistic regression-klassificerare och ett neuralt nätverk-klassificerare. Tre olika regressionsmodeller byggdes också: en support vector-regressor, en gradient boosted trees-regressor och ett neuralt nätverk-regressor. Resultaten från de olika modellerna jämfördes för att se vilken modell som är lämpligast för problemet. En undersökning om vilken information och vilka datavariabler som är viktigast för att uppskatta tiden det tar att lösa felanmälan utfördes också. Två olika scenarion för att uppskatta tiden har undersökts i rapporten. Det första scenariot använder informationen som är tillgänglig när en felanmälan skapas. Det andra scenariot använder informationen som finns tillgänglig efter det har bestämts om en tekniker ska skickas till den påverkade platsen. Slutsatsen av arbetet är att det är lättare att göra en bra tidsuppskattning i det andra scenariot jämfört med det första scenariot. Skillnaden i resultat mellan de olika maskininlärningsmodellerna var små. Framtida arbete inom ämnet kan använda information och data om de bakomliggande orsakerna till felanmälningarna, mer väderdata och information om elavbrott. En standardiserad metod för att samla in och logga data för varje felanmälan föreslås också för att göra framtida tidsuppskattningar bättre och undvika problemet med datapunkter som saknas.