Architecture for Provenance Systems

This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies

An Architecture for Provenance Systems

This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies

Collaboratory for Multi-scale Chemical Science DOE grant FG02-01ER25444

Motivation for the Project Progress on the many multi-scale problems in the chemical sciences is significantly hindered by the difficulties researchers working at each scale have in accessing and translating the best available information and methods from the other scales. Very often there are "gaps" between scales which cannot be bridged at present, often because there is an unresolved technical or mathematical issue in addition to the pervasive lack of translation software and problems with connecting the mismatched data models used at each scale. Problems are particularly severe for complex systems involving combustion and pyrolysis chemistry. For example, simulations used to design high-efficiency, low-emission homogeneous-charge compression-ignition (HCCI) engines typically contain thousands of different chemical species and reactions. The engine designer running the macroscopic simulation is typically not an expert in chemistry -the macroscopic engine scale is quite complicated enough -so he or she needs all the important microscopic chemical details to be handled more or less automatically by software, and in a way that the chemistry models can be easily updated as additional information becomes available. All these microscopic chemistry details must be documented electronically in a way that is easy visible to the chemistry community, and these chemistry databases must be extensible, to make it practical to capture the benefits of the very large, but also very thinly spread (i.e. each chemist is expert in only a few types of molecules and reactions, under a limited range of conditions), expertise in the chemistry community. The numerical methods used by the engine designer were not designed to handle all this chemical detail, so intermediate preprocessing model-reduction software is needed to reduce the size of the chemical model. It is crucial that the approximation errors introduced in this step be properly controlled, so we do not lose significant accuracy in the final simulation results. Again, all the assumptions and calculations involved in this model-reduction process need to be documented, to facilitate future progress and to allow the engine model to be updated as more information on the combustion chemistry becomes available

Collaborating for Multi-Scale Chemical Science

Advanced model reduction methods were developed and integrated into the CMCS multiscale chemical science simulation software. The new technologies were used to simulate HCCI engines and burner flames with exceptional fidelity

Um modelo de proveniência para extração de tendências em séries temporais

Orientador : Prof. Dr. Marcos Sfair SunyeCo-orientadora : Profa. Dra. Maria Salete Marcon Gomes VazTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 29/08/2014Inclui referências : f. 201-216Resumo: Muitas áreas do conhecimento estão relacionadas com a análise de séries temporais, as quais são constituídas por uma sequencia de observações de dados sobre o tempo. A análise de séries temporais difere da análise de dados tradicional, dada sua natureza intrínseca, onde as observações são dependentes. Nesse caso, procedimentos estatísticos considerando a independência dos dados não se aplicam, sendo necessário o uso de métodos específicos. Geralmente, a análise de séries temporais ocorre em duas fases, pré-processamento e análise dos dados. Na fase de pré-processamento, são feitas correções para remoção de fenômenos que ocorrem ao longo do tempo, como a extração de tendências (detrending). Vários softwares de detrending podem ser aplicados para esse fim, melhorando a análise, assim como a maioria dos métodos estatísticos são desenvolvidos para séries temporais estacionárias. Em um processo de detrending, informações de proveniência sobre as séries temporais e como as mesmas foram corrigidas de tendências nem sempre são explícitas e de fácil interpretação. Tais informações podem ser obtidas pelo uso de metadados, os quais podem gerar ambiguidades nos resultados gerados, assim como podem ser insuficientes para semanticamente enriquecer o processo de detrending. Por outro lado, ontologias permitem gerar e compartilhar conhecimento sobre as séries temporais e sobre os métodos estatísticos aplicados para sua correção, assim como permitem inferências. O principal objetivo desta tese é definir um modelo de proveniência usando ontologias para enriquecer semanticamente a extração de tendências em séries temporais. O modelo é validado por um estudo de caso com séries temporais fotométricas reais. A principal contribuição é a geração de conhecimento semântico, permitindo identificar, além dos dados, agentes e processos envolvidos, informações quanto aos métodos estatísticos usados para detrending, facilitando o entendimento de como as séries temporais foram geradas e corrigidas, melhorando a tomada de decisão quanto ao uso de métodos estatísticos. O ineditismo desta tese é a definição de um modelo de proveniência para extração de tendências, apresentando um projeto modular, centrado no reuso e na extensão de ontologias para gerar proveniência sobre séries temporais e processos de detrending, enriquecendo semanticamente um passo relevante da fase de pré-processamento da análise de séries temporais, contribuindo para a geração do conhecimento científico. Palavras-chave: Modelo de Proveniência, Ontologias, OWL, Séries Temporais Não-Estacionárias, Extração de TendênciasAbstract: Nowadays, many knowledge areas are related with the time series analysis, which are constituted by a sequence of data observation at the time. The time series analysis is different from the traditional data analysis, due to their intrinsic nature, where the observations are dependent. In this case, statistical procedures considering the data?s independence are not applied, being necessary the use of specific methods. Usually, the time series analysis occurs in two phases, preprocessing and data analysis. In the preprocessing phase, corrections are done to remove phenomena that occur throughout the time, like the trend extraction (detrending). Many detrending software can be applied for this objective, improving the analysis, as well as the most of statistical methods are developed to stationary time series. In a detrending process, provenance information about the time series and how the time series were detrended are not always explicit and easy to interpret. Such information can be obtained by metadata, which can generate ambiguity in the results generated and they can also be insufficient to semantically enrich the detrending process. On the other hand, ontologies allow generating and sharing knowledge about the time series and on the statistical methods used for it?s correction, as well as allow inferences. The main goal of this doctoral thesis is to define a provenance model using ontologies to semantically enrich the trend extraction of time series. The model is validated by a case study involving real photometric time series. The main contribution is the semantic knowledge generation, allowing to identify, besides the data, agents and process involved, information about the statistical methods used for detrending, facilitating the understanding about how the time series were generated and corrected, improving the decision making related with the statistical methods applicability. The novelty of this doctoral thesis is the definition of a provenance model for trend extraction, presenting a modular design, centered on reuse and on the ontologies extension to generate provenance about time series and detrending processes, enriching semantically a relevant step of preprocessing phase of the time series analysis, contributing to the generation of the scientific knowledge. Keywords: Provenance Model, Ontologies, OWL, Nonstationary Time Series, Detrendin