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Research Project
LASIGE - Extreme Computing
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Publications
Errors of identifiers in anonymous databases: impact on data quality
Publication . Pombinho, Paulo; Cavique, Luís; Correia, Luís
Data quality is essential for a correct understanding of the concepts they represent. Data mining is especially relevant when data with inferior quality is used in algorithms that depend on correct data to create accurate models and predictions. In this work, we introduce the issue of errors of identifiers in an anonymous database. The work proposes a quality evaluation approach that considers individual attributes and a contextual analysis that allows additional quality evaluations. The proposed quality analysis model is a robust means of minimizing anonymization costs.
Typability and type inference in atomic polymorphism
Publication . Protin, M. Clarence; Ferreira, Gilda
It is well-known that typability, type inhabitation and type inference are
undecidable in the Girard-Reynolds polymorphic system F. It has recently been proven
that type inhabitation remains undecidable even in the predicative fragment of system F
in which all universal instantiations have an atomic witness (system Fat). In this paper we
analyze typability and type inference in Curry style variants of system Fat and show that
typability is decidable and that there is an algorithm for type inference which is capable of
dealing with non-redundancy constraints.
A data science maturity model applied to students' modeling
Publication . Cavique, Luís; Pombalinho, Paulo; Correia, Luís
Maturity models define a series of levels, each representing an increased complexity in information systems. Data Science appears in the Business Intelligence (BI) and Business Analytics (BA) literature. This work applies the _IABE maturity model, which includes two additional levels: Data Engineering (DE) at the bottom and Business Experimentation (BE) at the top. This study uses the _IABE model for students' modeling in the ModEst project. For this purpose, the Public Administration organism is the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Education Ministry. DGEEC provided vast data on two million students per year in the Portuguese school system, from pre-scholar to doctoral programs. This work presents the comprehensible _IABE maturity model to extract new knowledge from the DGEEC dataset. The method applied is _IABE, where after the DE level, wh-questions are formulated and answered with the most appropriate techniques at each maturity level. This work's novelty is applying the maturity model _IABE to a unique dataset for the first time. Wh-questions are stated at the BI level using data summarization; at the BA level, predictive models are performed, and counterfactual approaches are presented at the BE level.
How to avoid the commuting conversions of IPC
Publication . Espírito Santo, José; Ferreira, Gilda
Since the observation in 2006 that it is possible to embed IPC into the atomic polymorphic λ-calculus (a predicative fragment of system F with universal instantiations restricted to atomic formulas) different such embeddings appeared in the literature. All of them comprise the Russell-Prawitz translation of formulas, but have different strategies for the translation of proofs. Although these embeddings preserve proof identity, all fail in delivering preservation of reduction steps. In fact, they translate the commuting conversions of IPC to β-equality, or to other kinds of reduction or equality generated by new principles added to system F. The cause for this is the generation of redexes by the translation itself. In this paper, we present an embedding of IPC into atomic system F, still based on the same translation of formulas, but which maps commuting conversions to syntactic identity, while simulating the other kinds of reduction steps present in IPC by βη-reduction. In this sense the translation achieves a truly commuting-conversion-free image of IPC in atomic system F.
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Funding agency
Fundação para a Ciência e a Tecnologia
Funding programme
6817 - DCRRNI ID
Funding Award Number
UIDB/00408/2020
