Enriching Retrieval Process for Case Based Reasoning by using Vertical Association Knowledge with Correlation

In case-based reasoning (CBR) the most important step is retrieval. The main purpose of the case-based reasoning is to get the cases which are useful to find out the solution for the given problem. For retrieving relevant data the CBR systems mainly uses the similarity knowledge. Most of the retrieving systems use similarity knowledge and association rules for retrieving the required cases. But the existing algorithms strongly rely on similarity knowledge and ignore the other forms of knowledge that can be used to improve the retrieval performance. The well known algorithm Apriori algorithm is used to extract desired relevant cases based on the knowledge system of the association rules with the efficient correlation methods. The goal of this paper is to provide detailed review about retrieving useful cases by using different methods and showing the effectiveness of each algorithm

Data-Driven Application Maintenance: Views from the Trenches

In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents to business processes. In the context of IT services, these problems are frequently encountered, yet there is a gap in bringing automation and optimization. Despite long-standing research around mining and analysis of software repositories, such research outputs are not adopted well in practice due to the constraints these solutions impose on the users. We discuss need for designing pragmatic solutions with low barriers to adoption and addressing right level of complexity of problems with respect to underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP), IEEE Press, pp. 48-54, 201

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

Predictive analysis of incidents based on software deployments

A high number of IT organizations have problems when deploying their services, this alongside with the high number of services that organizations have daily, makes Incident Management (IM) process quite demanding. An effective IM system need to enable decision makers to detect problems easily otherwise the organizations can face unscheduled system downtime and/or unplanned costs. By predicting these problems, the decision makers can better allocate resources and mitigate costs. Therefore, this research aims to help predicting those problems by looking at the history of past deployments and incident ticket creation and relate them by using machine learning algorithms to predict the number of incidents of a certain deployment. This research aims to analyze the results with the most used algorithms found in the literature.info:eu-repo/semantics/publishedVersio

Predictive analysis of incidents based on software deployments

A high number of information technology organizations have several problems during and after deploying their services, this alongside with the high number of services that they provide daily, it makes Incident Management (IM) process quite demanding. An effective IM system needs to enable decision-makers to detect problems easily. Otherwise, the organizations can face unscheduled system downtime and/or unplanned costs. This study demonstrates that is possible to introduce a predictive process that may lead to an improvement of the response time to incidents and to the reduction of the number of incidents created by deployments. By predicting these problems, the decision-makers can better allocate resources and mitigate costs. Therefore, this research aims to investigate if machine learning algorithms can help to predict the number of incidents of a certain deployment. The results showed with some security, that it is possible to predict, if a certain deployment will have or not an incident in the future.Um número elevado de organizações de tecnologias de informação têm um grande número de problemas no momento e após lançarem os seus serviços, se juntarmos a isto o número elevado de serviços que estas organizações prestam diariamente, dificulta bastante o processo de Incident Management (IM). Um sistema de IM eficaz deve permitir aos decisores de negócio detetar facilmente estes problemas, caso contrário, as organizações podem ter de enfrentar imprevistos nos seus serviços (custos ou falhas). Esta tese irá demonstrar que é possível introduzir um processo de previsão que poderá levar a um melhoramento do tempo de resposta aos incidentes, assim como uma redução dos mesmo. Prevendo estes problemas estes podem alocar melhor os recursos assim como mitigar os incidentes. Como tal, esta tese irá analisar como prever esses incidentes, analisando os deployments feitos nos últimos anos e relacionando-os usando algoritmos de machine learning para prever os incidentes. Os resultados mostraram que é possível prever com confiança se um determinado deploymente vai ou não ter incidentes

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

Construcción de un Datamart que apoye en la toma de decisiones de la gestión de incidencias en una mesa de ayuda: caso Consorcio Peruano de Empresas

Construye un Datamart basado en el funcionamiento de la gestión de incidencias de la mesa de ayuda del Consorcio Peruano de Empresas con el objetivo de obtener tendencias e indicadores de grandes cantidades de datos de los incidentes reportados por los usuarios a la mesa de ayuda. Para el análisis rápido y oportuno se generan reportes de manera automática con gráficos estadísticos y dashboards, con esto el usuario logra identificar los focos de incidencias de manera rápida y tomar las acciones correctivas que sean necesarias para mitigarlos ya que el impacto de las incidencias reportadas a la mesa de ayuda afecta la operatividad de los usuarios y esto impacta de manera directa a la operación del servicio de la organización.Tesi

A new strategy for case-based reasoning retrieval using classification based on association

Cased Based Reasoning (CBR) is an important area of research in the field of Artificial Intelli-gence. It aims to solve new problems by adapting solutions, that were used to solve previous similar ones. Among the four typical phases - retrieval, reuse, revise and retain, retrieval is a key phase in CBR approach, as the retrieval of wrong cases can lead to wrong decisions. To ac-complish the retrieval process, a CBR system exploits Similarity-Based Retrieval (SBR). How-ever, SBR tends to depend strongly on similarity knowledge, ignoring other forms of knowledge, that can further improve retrieval performance.The aim of this study is to integrate class association rules (CARs) as a special case of associa-tion rules (ARs), to discover a set (of rules) that can form an accurate classifier in a database. It is an efficient method when used to build a classifier, where the target is pre-determined. The proposition for this research is to answer the question of whether CARs can be integrated into a CBR system. A new strategy is proposed that suggests and uses mining class association rules from previous cases, which could strengthen similarity based retrieval (SBR). The propo-sition question can be answered by adapting the pattern of CARs, to be compared with the end of the Retrieval phase. Previous experiments and their results to date, show a link between CARs and CBR cases. This link has been developed to achieve the aim and objectives.A novel strategy, Case-Based Reasoning using Association Rules (CBRAR) is proposed to improve the performance of the SBR and to disambiguate wrongly retrieved cases in CBR. CBRAR uses CARs to generate an optimum frequent pattern tree (FP-tree) which holds a val-ue of each node. The possible advantage offered is that more efficient results can be gained, when SBR returns uncertain answers. In addition, CBRAR has been evaluated using two sources of CBR frameworks - Jcolibri and Free CBR. With the experimental evaluation on real datasets indicating that the proposed CBRAR is a better approach when compared to CBR systems, offering higher accuracy and lower error rate