The ModelCC Model-Driven Parser Generator

Syntax-directed translation tools require the specification of a language by means of a formal grammar. This grammar must conform to the specific requirements of the parser generator to be used. This grammar is then annotated with semantic actions for the resulting system to perform its desired function. In this paper, we introduce ModelCC, a model-based parser generator that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual syntax and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a reference resolution mechanism so that ModelCC is able to instantiate abstract syntax graphs, rather than mere abstract syntax trees.Comment: In Proceedings PROLE 2014, arXiv:1501.0169

NOESIS: A Framework for Complex Network Data Analysis

Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. ­e proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. ­is framework is available under a BSD open-source software license.The NOESIS project was partially supported by the Spanish Ministry of Economy and the European Regional Development Fund (FEDER), under grant TIN2012–36951, and the Spanish Ministry of Education under the program “Ayudas para contratos predoctorales para la formación de doctores 2013” (predoctoral grant BES–2013–064699)

FINE-GRAINED PERFORMANCE EVALUATION AND MONITORING USING ASPECTS A Case Study on the Development of Data Mining Techniques

Abstract: This paper illustrates how aspect-oriented programming techniques support some tasks whose implementation using conventional object-oriented programming would be extremely time-consuming and error-prone. In particular, we have successfully employed aspects to evaluate and monitor the I/O performance of alternative data mining techniques. Without having to modify the source code of the system under analysis, aspects provide an unintrusive mechanism to perform this kind of performance analysis. In fact, aspects let us probe a system implementation so that we can identify potential bottlenecks, detect redundant computations, and characterize system behavior

Interestingness Measures for Association Rules within Groups

Abstract. The study of association rules within groups of individuals in a database is interesting to define their characteristics and their behavior. In this paper, we define group association rules and we study interestingness measures for them. These evaluation measures can be used to rank groups of individuals and also rules within each group

An Overview of Alternative Rule Evaluation Criteria and Their Use in Separate-and-Conquer Classifiers

Separate-and-conquer classifiers strongly depend on the criteria used to choose which rules will be included in the classification model. When association rules are employed to build such classifiers (as in ART [3]), rule evaluation can be performed attending to different criteria (other than the traditional confidence measure used in association rule mining). In this paper, we analyze the desirable properties of such alternative criteria and their effect in building rule-based classifiers using a separate-and-conquer strategy