Eventos

Prova de CAT

21 Novembro 2017, 12:14 - Fátima Sampaio


Candidate: Sérgio Ricardo de Oliveira Esteves N.º 54564/D

Title: Techniques for Enhancing the Performance of Data-intensive Management Systems

Date: 27/11/2017

Time: 14h30

Location: Sala 0.20, Pavilhão Informática II, IST, Alameda

Advisors: Professor Luís Manuel Antunes Veiga/ Professor João Nuno De Oliveira e Silva

Abstract: The demand for storing and analyzing vast volumes of data is today on the rise as web-based enterprises introduce innovative and interactive applications, that attract more and more users on a global scale. To cope with such data volumes, data management systems have been evolving to deliver increasingly better performance and efficiency at lower costs in large-scale scenarios. A fundamental property of these systems is data consistency. In storage systems, consistency refers to how accurate, fresh and synchronized is the state of data replicas residing in different machines and locations. Most of these systems, namely NoSQL data stores, sacrifice consistency in favor of availability and performance for cross cluster synchronization; while others, provide strong consistency and sacrifice availability.In data processing systems, namely in dataflow processing, consistency refers to the completeness state of the input that is reflected in the output within a time frame. Most dataflow management systems are strongly consistent by enforcing strict temporal synchronization across processing steps. The main goal of our research is to study, design, implement and evaluate performance optimizations for data-intensive management systems. At the heart of these optimizations resides the tuning of data consistency. In particular, we take into account the semantics of data in order to trade-off consistency for performance in storage and data processing systems. We are able to achieve substantial performance gains, namely in terms of latency, throughput, bandwidth, and resource utilization, while keeping application outputs within acceptable levels, as defined by applications.As contributions we propose: (i) VFC3, a consistency model, equipping a framework for NoSQL data stores, that enables multiple consistency levels over groups of data with different replication urgencies; (ii) Fluχ, a dataflow model, empowering a framework for dataflow managers, that enables deferred triggering of computation stages based on the assessed impact of the input on changing the output; and (iii) WaaS, a scheduling algorithm, inspired by the F luχ model, to allocate machines to dataflow tasks based on time, budget and consistency constraints.


Prova de Dissertação MEIC- T

17 Novembro 2017, 15:56 - Elisabete Maria Santos Madeira Ribeiro da Fonseca

Semana de 20 a 24 de Novembro de 2017


Candidato: Diogo Agostinho Xavier (71052)

Título da Dissertação: Tracklt (Searchlt!)

Data: 22 de novembro de 2017

Hora:
15:00h

Local:
Sala E5, Pavilhão de Electricidade no piso 1 , IST – Alameda

Orientação:
Prof. João Manuel Brisson Lopes

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Candidato: André Filipe Salgado de Ascenção (66355)

Título da Dissertação: Digital Transformation in Portuguese Courts

Data: 22 de novembro de 2017

Hora:
18:30h

Local:
Sala de Reuniões 1.38 - IST – TagusPark

Orientação:
Prof. Miguel Leitão Bignolas Mira da Silva

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Prova de Dissertação MISE

17 Novembro 2017, 10:53 - Elisabete Maria Santos Madeira Ribeiro da Fonseca


Candidato: Rui Pedro Rodrigues Coelho (85356)

Título da Dissertação: Caso de Estudo de Implementação de ITIL numa Pequena Organização

Data: 20 de novembro de 2017

Hora:
11:30h

Local:
Sala 2.6,  IST - Taguspark

Orientação:
Prof. Prof. Miguel Leitão Bignolas Mira da Silva

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Prova de Dissertação METI

10 Novembro 2017, 11:03 - Elisabete Maria Santos Madeira Ribeiro da Fonseca

Semana de 20 a 24 de novembro de 2017


Candidato: Eliana Neuza Pinheiro Rodrigues Gordino da Silva (76455)

Título da Dissertação: Monitoring and Assessement  of the Wi-Fi Signal Quality and Detection of Unauthorized Aps on High- End Wireless Networks

Data: 20 de Novembro de 2017

Hora: 10:00h

Local:
Sala 2.10, IST - TagusPark

Orientação:
Prof. Fernando Henrique Côrte-Real Mira da Silva/Prof. Rui Manuel Rodrigues Rocha

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Candidata: Mariana Marçal Vargas (76407)

Título da Dissertação: WakeUpNet - Smart Pomodoro for productivity management

Data: 22 de Novembro de 2017

Hora: 11:30h

Local:
Sala 1.38, IST - TagusPark

Orientação:
Prof. Teresa Maria Sá Ferreira Vazão Vasques

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Candidato: Miguel José Matos de Almeida (70503)

Título da Dissertação: Parking Spot - Sistema de Gestão e Informaçãoo de Parques de Estacionamento

Data: 22 de Novembro de 2017

Hora: 13:00h

Local:
Sala 1.38, IST - TagusPark

Orientação:
Prof. Teresa Maria Sá Ferreira Vazão Vasques

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Candidato: Diogo Manuel Caldeira Coutinho (74008)

Título da Dissertação: Contributions for the Standardisation of a SDN Northbound Interface for Load Balancing Applications

Data: 22 de Novembro de 2017

Hora: 14:30h

Local:
Sala 1.38, IST - TagusPark

Orientação:
Prof. Fernando Henrique Côrte-Real Mira da Silva

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Prova de Tópicos de Investigação

7 Novembro 2017, 09:48 - Fátima Sampaio


Candidate: Ali Kordia N.º 85554/D


Title: Improved Dynamic Movement Primitives using Reinforcement Learning by Kinesthetic Demonstrations

Date: 13/11/2017

Time: 14h30

Location: Sala 2N7.1, IST, Taguspark

Advisor: Professor Francisco António Chaves Saraiva de Melo

Abstract: One of the important issues in robotics concerns motion and its achievement through the lowest cost possible. It is important to determine the robot’s shortest path to a desired state, crucial for safely executing several activities, such as carrying and moving objects with minimal cost. These movements are represented by several possible trajectories, each with a given cost. The presented research aims to obtain the best trajectory of lowest cost, ignoring existent obstructions in the previously known environment. We use exploration and evaluate several possible trajectories to choose the best one. To do so, we apply Reinforcement Learning (RL) on Dynamic Movement Primitives (DMP) to improve the trajectory and get the optimal one, where the RL uses samples obtained using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. we do experiments and apply our work on Baxter robot.