Aplicação de Técnicas de Mineração do Uso da Web para Análise de Processos de Negócio: um Estudo de Caso

automação de processos. O interesse em desempenho mudou este foco para a capacidade de análise, monitoração e mensuração de processos. Este trabalho descreve um estudo de caso no qual técnicas de mineração de seqüências originalmente propostas para a Mineração do Uso da Web foram aplicadas para obter informação sobre o fluxo de execução de processos. Abstract. For many years, organizations have focused on the management and automation of processes. The interest on business performance has shifted the focus towards the analysis, monitoring and measurement capacity of these processes. This paper describes a case study in which sequence mining techniques originally proposed for Web Use Mining were used to obtain information on processes execution flow. 1. Introdução Processo de negócio (ou apenas processo) é “um conjunto de um ou mais procedimentos ou atividades relacionados, os quais coletivamente atingem um objetivo de negócio, dentro do contexto de uma estrutura organizacional que define papéis funcionais e relações ” [8]. Co

Bio-Strings: A Relational Database Data-Type for Dealing with Large Biosequences

DNA sequencers output a large set of very long biological data strings that we should persist in databases rather than basic text file systems. Many different data models and database management systems (DBMS) may deal with both storage and efficiency issues regarding genomic datasets. Specifically, there is a need for handling strings with variable sizes while keeping their biological meaning. Relational database management systems (RDBMS) provide several data types that could be further explored for the genomics context. Besides, they enforce integrity, consistency, and enable good abstractions for more conventional data. We propose the relational text data type to represent and manipulate biological sequences and their derivatives. We present a logical schema for representing the core biological information, which may be inferred from a given biological conceptual data schema and the corresponding function manipulations. We implement and evaluate these stored functions into an actual RDBMS for both efficacy and efficiency. We show that it is possible to enforce basic and complex requirements for the genomic domain. We claim that the well-established relational text data type in RDBMS may appropriately handle the representation and persistency of biological sequences. We base our approach on the idea of domain-specific abstract data types that can store data with semantically defined functions while hiding those details from non-technical end-users