Information-dependent processes in incubated and graduated technology-based companies: a comparative case study

Nowadays, competitiveness introduces new behaviors and leads companies to a discomforting situation and often to non adaptation to environmental requirements. A growing number of challenges associated with control of information in organizations with engineering activities can be seen, particularly, the growing amount of information subject to continuous changes. The innovative performance of an organization is directly proportional to its ability to manage information. Thus, the importance of information management is recognized by the search for more competent ways to face current demands. The purpose of this article was to analyze informationdependent processes in technology-based companies, through the four major stages of information management. The comparative method of cases and qualitative research were used. The research was conducted in nine technology-based companies which were incubated or recently went through the incubating process at the Technological Park of Sao Carlos, in the state of Sao Paulo. Among the main results, it was found that in graduated companies information management and its procedures were identified as more conscious and structured in contrast to those of the incubated companies.

MOOGLE: a metamodel-based model search engine

Models are becoming increasingly important in the software development process. As a consequence, the number of models being used is increasing, and so is the need for efficient mechanisms to search them. Various existing search engines could be used for this purpose, but they lack features to properly search models, mainly because they are strongly focused on text-based search. This paper presents Moogle, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed. The paper also presents the results of an evaluation of Moogle, which showed that the metamodel information improves the accuracy of the search.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.