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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
014
Publicações

2020

Proceedings of the 8th International Workshop on Big Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications co-located with 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August 4-8, 2019

Autores
Bifet, A; Berlingerio, M; Gama, J; Read, J; Nogueira, AR;

Publicação
BigMine@KDD

Abstract

2020

A drift detection method based on dynamic classifier selection

Autores
Pinage, F; dos Santos, EM; Gama, J;

Publicação
Data Min. Knowl. Discov.

Abstract

2020

Identifying Points of Interest and Similar Individuals from Raw GPS Data

Autores
Andrade, T; Gama, J;

Publicação
CoRR

Abstract

2020

On fast and scalable recurring link's prediction in evolving multi-graph streams

Autores
Tabassum, S; Veloso, B; Gama, J;

Publicação
Network Science

Abstract
The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time. © 2020 Cambridge University Press.

2020

Fraud detection using heavy hitters: A case study

Autores
Veloso, B; Martins, C; Espanha, R; Azevedo, R; Gama, J;

Publicação
Proceedings of the ACM Symposium on Applied Computing

Abstract
The high asymmetry of international termination rates, where calls are charged with higher values, are fertile ground for the appearance of frauds in Telecom Companies. In this paper, we present three different and complementary solutions for a real problem called Interconnect Bypass Fraud. This problem is one of the most common in the telecommunication domain and can be detected by the occurrence of abnormal behaviours from specific numbers. Our goal is to detect as soon as possible numbers with abnormal behaviours, e.g. bursts of calls, repetitions and mirror behaviours. Based on this assumption, we propose: (i) the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm; (ii) the proposal of a single pass hierarchical heavy hitter algorithm that also contains a forgetting technique; and (iii) the application of the HyperLogLog sketches for each phone number. We used the heavy hitters to detect abnormal behaviours, e.g. burst of calls, repetition and mirror. The hierarchical heavy hitters algorithm is used to detect the numbers that make calls for a huge set of destinations and destination numbers that receives a huge set of calls to provoke a denial of service. Additionally, to detect the cardinality of destination numbers of each origin number we use the HyperLogLog algorithm. The results shows that these three approaches combined complements the techniques used by the telecom company and make the fraud task more difficult. © 2020 ACM.

Teses
supervisionadas

2019

Market Basket Analysis and High utility itemset Minning in a retail Company

Autor
Joana Monteiro Lemos

Instituição
UP-FEP

2019

Massive Scale Streaming Graphs: Evolving Network Analysis and Mining

Autor
Shazia Tabassum

Instituição
UP-FEUP

2019

Time Series Change Point Identification with Application of the Data: Verification of the Occurrence of Abrupt change points in the time series of the esocial data in front of the caged

Autor
Sérgio Luiz Rodrigues Torres

Instituição
UP-FEP

2019

Trustability in data-driven decision models for Public Policy

Autor
Sónia Alexandra Carvalho Teixeira

Instituição
UP-FEUP

2019

Halo Effect in Promotions – Application to Food Retail

Autor
Bruno Daniel de Castro Borges

Instituição
UP-FEP