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

Web crawler research methodology

In economic and social sciences it is crucial to test theoretical models against reliable and big enough databases. The general research challenge is to build up a well-structured database that suits well to the given research question and that is cost efficient at the same time. In this paper we focus on crawler programs that proved to be an effective tool of data base building in very different problem settings. First we explain how crawler programs work and illustrate a complex research process mapping business relationships using social media information sources. In this case we illustrate how search robots can be used to collect data for mapping complex network relationship to characterize business relationships in a well defined environment. After that extend the case and present a framework of three structurally different research models where crawler programs can be applied successfully: exploration, classification and time series analysis. In the case of exploration we present findings about the Hungarian web agency industry when no previous statistical data was available about their operations. For classification we show how the top visited Hungarian web domains can be divided into predefined categories of e-business models. In the third research we used a crawler to gather the values of concrete pre-defined records containing ticket prices of low cost airlines from one single site. Based on the experiences we highlight some conceptual conclusions and opportunities of crawler based research in e-business. --e-business research,web search,web crawler,Hungarian web,social network analyis

Abmash: Mashing Up Legacy Web Applications by Automated Imitation of Human Actions

Many business web-based applications do not offer applications programming interfaces (APIs) to enable other applications to access their data and functions in a programmatic manner. This makes their composition difficult (for instance to synchronize data between two applications). To address this challenge, this paper presents Abmash, an approach to facilitate the integration of such legacy web applications by automatically imitating human interactions with them. By automatically interacting with the graphical user interface (GUI) of web applications, the system supports all forms of integrations including bi-directional interactions and is able to interact with AJAX-based applications. Furthermore, the integration programs are easy to write since they deal with end-user, visual user-interface elements. The integration code is simple enough to be called a "mashup".Comment: Software: Practice and Experience (2013)

A reflective characterisation of occasional user

This work revisits established user classifications and aims to characterise a historically unspecified user category, the Occasional User (OU). Three user categories, novice, intermediate and expert, have dominated the work of user interface (UI) designers, researchers and educators for decades. These categories were created to conceptualise user's needs, strategies and goals around the 80s. Since then, UI paradigm shifts, such as direct manipulation and touch, along with other advances in technology, gave new access to people with little computer knowledge. This fact produced a diversification of the existing user categories not observed in the literature review of traditional classification of users. The findings of this work include a new characterisation of the occasional user, distinguished by user's uncertainty of repetitive use of an interface and little knowledge about its functioning. In addition, the specification of the OU, together with principles and recommendations will help UI community to informatively design for users without requiring a prospective use and previous knowledge of the UI. The OU is an essential type of user to apply user-centred design approach to understand the interaction with technology as universal, accessible and transparent for the user, independently of accumulated experience and technological era that users live in