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Sobre

João Gama é Professor Associado da Faculdade de Economia da Universidade do Porto. É investigador e vice-diretor do LIAAD, INESC TEC. Concluiu o doutoramento na Universidade do Porto, em 2000. É Sénior member do IEEE. Trabalhou em vários projetos nacionais e europeus sobre sistemas de aprendizagem incremental e adaptativo, descoberta de conhecimento em tempo real, e aprendizagem de dados massivos e estruturados. Foi PC chair no ECML2005, DS2009, ADMA2009, IDA '2011 e ECML / PKDD'2015. Foi track chair ACM SAC de 2007 a 2018. Organizou uma série de Workshops sobre Descoberta de Conhecimento de fluxos de dados no ECMLPKDD, ICML, e no ACM SIGKDD. É autor de vários livros em Data Mining e autoria de uma monografia sobre Descoberta de Conhecimento a partir de fluxos de Dados. É autor de mais de 250 papéis peer-reviewed em áreas relacionadas com a aprendizagem automática, aprendizagem de dados em tempo real e fluxos de dados. É membro do conselho editorial de revistas internacionais ML, DMKD, TKDE, IDA, NGC e KAIS. Supervisionou mais de 15 estudantes de doutoramento e 50 alunos de mestrado.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Gama
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 abril 2009
013
Publicações

2019

The search of conditional outliers

Autores
Portel, E; Ribeire, RP; Gama, J;

Publicação
INTELLIGENT DATA ANALYSIS

Abstract
There is no standard definition of outliers, but most authors agree that outliers are points far from other data points. Several outlier detection techniques have been developed mainly for two different purposes. On one hand, outliers are considered error measurement observations that should be removed from the analysis, e.g. robust statistics. On the other hand, outliers are the interesting observations, like in fraud detection, and should be modelled by some learning method. In this work, we start from the observation that outliers are affected by the so-called simpson paradox: a trend that appears in different groups of data but disappears or reverses when these groups are combined. Given a data set, we learn a regression tree. The tree grows by partitioning the data into groups more and more homogeneous of the target variable. At each partition defined by the tree, we apply a box plot on the target variable to detect outliers. We would expect that the deeper nodes of the tree would contain less and less outliers. We observe that some points previously signalled as outliers are no more signalled as such, but new outliers appear.

2019

Development and Field Demonstration of a Gamified Residential Demand Management Platform Compatible with Smart Meters and Building Automation Systems

Autores
Zehir, MA; Ortac, KB; Gul, H; Batman, A; Aydin, Z; Portela, JC; Soares, FJ; Bagriyanik, M; Kucuk, U; Ozdemir, A;

Publicação
ENERGIES

Abstract
Demand management is becoming an indispensable part of grid operation with its potential to aid supply/demand balancing, reduce peaks, mitigate congestions and improve voltage profiles in the grid. Effective deployments require a huge number of reliable participators who are aware of the flexibilities of their devices and who continuously seek to achieve savings and earnings. In such applications, smart meters can ease consumption behavior visibility, while building automation systems can enable the remote and automated control of flexible loads. Moreover, gamification techniques can be used to motivate and direct customers, evaluate their performance, and improve their awareness and knowledge in the long term. This study focuses on the design and field demonstration of a flexible device-oriented, smart meter and building automation system (BAS) compatible with a gamified load management (LM) platform for residential customers. The system is designed, based on exploratory surveys and systematic gamification approaches, to motivate the customers to reduce their peak period consumption and overall energy consumption through competing or collaborating with others, and improving upon their past performance. This paper presents the design, development and implementation stages, together with the result analysis of an eight month field demonstration in four houses with different user types in Istanbul, Turkey.

2019

Gait stride-to-stride variability and foot clearance pattern analysis in Idiopathic Parkinson's Disease and Vascular Parkinsonism

Autores
Ferreira, F; Gago, MF; Bicho, E; Carvalho, C; Mollaei, N; Rodrigues, L; Sousa, N; Rodrigues, PP; Ferreira, C; Gama, J;

Publicação
Journal of Biomechanics

Abstract
The literature on gait analysis in Vascular Parkinsonism (VaP), addressing issues such as variability, foot clearance patterns, and the effect of levodopa, is scarce. This study investigates whether spatiotemporal, foot clearance and stride-to-stride variability analysis can discriminate VaP, and responsiveness to levodopa. Fifteen healthy subjects, 15 Idiopathic Parkinson's Disease (IPD) patients and 15 VaP patients, were assessed in two phases: before (Off-state), and one hour after (On-state) the acute administration of a suprathreshold (1.5 times the usual) levodopa dose. Participants were asked to walk a 30-meter continuous course at a self-selected walking speed while wearing foot-worn inertial sensors. For each gait variable, mean, coefficient of variation (CV), and standard deviations SD1 and SD2 obtained by Poincaré analysis were calculated. General linear models (GLMs) were used to identify group differences. Patients were subject to neuropsychological evaluation (MoCA test) and Brain MRI. VaP patients presented lower mean stride velocity, stride length, lift-off and strike angle, and height of maximum toe (later swing) (p < .05), and higher %gait cycle in double support, with only the latter unresponsive to levodopa. VaP patients also presented higher CV, significantly reduced after levodopa. Yet, all VaP versus IPD differences lost significance when accounting for mean stride length as a covariate. In conclusion, VaP patients presented a unique gait with reduced degrees of foot clearance, probably correlated to vascular lesioning in dopaminergic/non-dopaminergic cortical and subcortical non-dopaminergic networks, still amenable to benefit from levodopa. The dependency of gait and foot clearance and variability deficits from stride length deserves future clarification. © 2019 Elsevier Ltd

2019

Special track on data streams

Autores
Bifet, A; Carvalho, A; Ferreira, C; Gama, J;

Publicação
Proceedings of the ACM Symposium on Applied Computing

Abstract

2019

BRIGHT - Drift-aware demand predictions for taxi networks (Extended Abstract)

Autores
Saadallah, A; Moreira Matias, L; Sousa, R; Khiari, J; Jenelius, E; Gama, J;

Publicação
Proceedings - International Conference on Data Engineering

Abstract
The dynamic behavior of urban mobility patterns makes matching taxi supply with demand as one of the biggest challenges in this industry. Recently, the increasing availability of massive broadcast GPS data has encouraged the exploration of this issue under different perspectives. One possible solution is to build a data-driven real-time taxi-dispatching recommender system. However, existing systems are based on strong assumptions such as stationary demand distributions and finite training sets, which make them inadequate for modeling the dynamic nature of the network. In this paper, we propose BRIGHT: a drift-aware supervised learning framework which aims to provide accurate predictions for short-term horizon taxi demand quantities through a creative ensemble of time series analysis methods that handle distinct types of concept drift. A large experimental set-up which includes three real-world transportation networks and a synthetic test-bed with artificially inserted concept drifts, was employed to illustrate the advantages of BRIGHT when compared to S.o.A methods for this problem. © 2019 IEEE.

Teses
supervisionadas

2018

Optimal Participation of an Aggregator of Prosumers in the Electricity Markets

Autor
José Pedro Barreira Iria

Instituição
UP-FEUP

2017

Image visual similarity with deep learning: application to a fashion ecommerce company

Autor
Rui Pedro da Silva Rodrigues Machado

Instituição
UP-FEP

2017

Regras de Associação – Market Basket Analysis - Items Frequentes e Itens Raros

Autor
Filomena Clara Gouveia Anselmo

Instituição
UP-FEP

2017

Credit Scoring for micro-finance on emerging markets with non-traditional data

Autor
Saulo Neftali Carpio Ruiz

Instituição
UP-FEP

2017

Computer Vision: Object recognition with deep learning applied to fashion items detection in images

Autor
Hélder Filipe de Sousa Russa

Instituição
UP-FEP