EVENTO
Managing large-scale scientific hypotheses as uncertain and probabilistic data
Tipo de evento: Seminário de Avaliação - Série A
In view of the paradigm shift that makes science ever more data-intensive, in this thesis we propose a suit of techniques for managing large-scale scientific hypotheses as uncertain and probabilistic data. In the form of mathematical equations, hypotheses symmetrically relate aspects of the studied phenomena. For computing predictions, however, deterministic hypotheses are used asymmetrically as functions. We refer to Simon's notion of structural equations in order to extract the (so-called) causal ordering embedded in a hypothesis. We encode it into a set of functional dependencies (FD's) that is basic input to a design-theoretic method for the synthesis of U-relational databases (DB's). The causal ordering captured from a formally-specified system of mathematical equations into FD's is shown to determine not only the constraints (structure), but also correlations (uncertainty chaining) hidden in the hypothesis predictive data. We show then how to process it effectively through original algorithms for encoding and reasoning on the given hypotheses as constraints and correlations into U-relational DB's. The proposed method is applicable to both quantitative and qualitative hypotheses and has underwent tests in scale in a realistic use case from computational science.
Data Início: 27/11/2014 Hora: 09:30 Data Fim: 27/11/2014 Hora: 12:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio A
Aluno: Bernardo Gonçalves - Laboratório Nacional de Computação Científica - LNCC
Orientador: Fabio Andre Machado Porto - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Artur Ziviani - Laboratório Nacional de Computação Científica - LNCC Fabio Andre Machado Porto - Laboratório Nacional de Computação Científica - LNCC Marco Antonio Casanova - Pontifícia Universidade Católica do Rio de Janeiro - PUC-RIO Pedro Leite da Silva Dias - - IAG/USP
