Concise server-wide causality management for eventually consistent data stores

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of net work partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts. Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. Weintroduceanewcausalitymanagementframeworkforeventuallyconsistentdatastores,thatleveragesnodelogicalclocks(BitmappedVersion Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.

Epidemic store for massive scale systems

The MAP-i doctoral program of the Universities of Minho, Aveiro and Porto

A bayesian approach for NDT data fusion: The Saint Torcato Church case study

This paper presents a methodology based on the Bayesian data fusion techniques applied to non-destructive and destructive tests for the structural assessment of historical constructions. The aim of the methodology is to reduce the uncertainties of the parameter estimation. The Young's modulus of granite stones was chosen as an example for the present paper. The methodology considers several levels of uncertainty since the parameters of interest are considered random variables with random moments. A new concept of Trust Factor was introduced to affect the uncertainty related to each test results, translated by their standard deviation, depending on the higher or lower reliability of each test to predict a certain parameter.

A domain-specific language for ETL patterns specification in Data Warehousing Systems

During the last few years many research efforts have been done to improve the design of ETL (Extract-Transform-Load) systems. ETL systems are considered very time-consuming, error-prone and complex involving several participants from different knowledge domains. ETL processes are one of the most important components of a data warehousing system that are strongly influenced by the complexity of business requirements, their changing and evolution. These aspects influence not only the structure of a data warehouse but also the structures of the data sources involved with. To minimize the negative impact of such variables, we propose the use of ETL patterns to build specific ETL packages. In this paper, we formalize this approach using BPMN (Business Process Modelling Language) for modelling more conceptual ETL workflows, mapping them to real execution primitives through the use of a domain-specific language that allows for the generation of specific instances that can be executed in an ETL commercial tool.

High strain rate constitutive modeling forhistorical structures subjected to blast loading

Doctoral Thesis Civil Engineering

An innovative framework for probabilistic-based structural assessment with an application to existing reinforced concrete structures

A novel framework for probabilistic-based structural assessment of existing structures, which combines model identification and reliability assessment procedures, considering in an objective way different sources of uncertainty, is presented in this paper. A short description of structural assessment applications, provided in literature, is initially given. Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented. Special attention is given to both experimental and numerical errors, to be considered in this algorithm convergence criterion. An updated numerical model is obtained from this process. The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure. The developed model is then updated, as new data is acquired, through a Bayesian inference algorithm, explicitly addressing statistical uncertainty. Finally, the developed framework is validated with a set of reinforced concrete beams, which were loaded up to failure in laboratory.

Big data e data analysis: visualização de informação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

A multi–physics approach applied to masonry structures with non–hydraulic lime mortars

Tese de Doutoramento em Engenharia Civil (área de especialização em Engenharia de Estruturas).