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About

João Gama is Associate Professor of the Faculty of Economy, University of Porto. He is a researcher and vice-director of LIAAD, a group belonging to INESC TEC. He got the PhD degree from the University of Porto, in 2000. He is Senior member of IEEE.

He has worked in several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery, Learning from Massive, and Structured Data, etc. He served as Co-Program chair of ECML'2005, DS'2009, ADMA'2009, IDA' 2011, and ECML/PKDD'2015. He served as track chair on Data Streams with ACM SAC from 2007 till 2016. He organized a series of Workshops on Knowledge Discovery from Data Streams with ECML/PKDD, and Knowledge Discovery from Sensor Data with ACM SIGKDD. He is author of several books in Data Mining (in Portuguese) and authored a monograph on Knowledge Discovery from Data Streams. He authored more than 250 peer-reviewed papers in areas related to machine learning, data mining, and data streams. He is a member of the editorial board of international journals ML, DMKD, TKDE, IDA, NGC, and KAIS. He (co-)supervised more than 12 PhD students and 50 Msc students.

Interest
Topics
Details

Details

  • Name

    João Gama
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st April 2009
015
Publications

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

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

Publication
BigMine@KDD

Abstract

2020

A drift detection method based on dynamic classifier selection

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

Publication
Data Min. Knowl. Discov.

Abstract

2020

Identifying Points of Interest and Similar Individuals from Raw GPS Data

Authors
Andrade, T; Gama, J;

Publication
CoRR

Abstract

2020

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

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

Publication
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

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

Publication
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.

Supervised
thesis

2019

The Impact of Big Data on Financial Auditing Profession

Author
Tânia Marisa Jesus Julião

Institution
UP-FEP

2019

Extracting technological description of companies from text

Author
Filippe Gustavo Correia de Sousa Reis

Institution
UP-FEP

2019

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

Author
Joana Monteiro Lemos

Institution
UP-FEP

2019

Massive Scale Streaming Graphs: Evolving Network Analysis and Mining

Author
Shazia Tabassum

Institution
UP-FEUP

2019

Halo Effect in Promotions – Application to Food Retail

Author
Bruno Daniel de Castro Borges

Institution
UP-FEP