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About

About

João Gama is a Full Professor at 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 a Senior member of IEEE.

He has worked on 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 the author of several books on 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
  • Role

    Research Coordinator
  • Since

    01st April 2009
017
Publications

2024

SWINN: Efficient nearest neighbor search in sliding windows using graphs

Authors
Mastelini, SM; Veloso, B; Halford, M; de Carvalho, ACPDF; Gama, J;

Publication
INFORMATION FUSION

Abstract
Nearest neighbor search (NNS) is one of the main concerns in data stream applications since similarity queries can be used in multiple scenarios. Online NNS is usually performed on a sliding window by lazily scanning every element currently stored in the window. This paper proposes Sliding Window-based Incremental Nearest Neighbors (SWINN), a graph-based online search index algorithm for speeding up NNS in potentially never-ending and dynamic data stream tasks. Our proposal broadens the application of online NNS-based solutions, as even moderately large data buffers become impractical to handle when a naive NNS strategy is selected. SWINN enables efficient handling of large data buffers by using an incremental strategy to build and update a search graph supporting any distance metric. Vertices can be added and removed from the search graph. To keep the graph reliable for search queries, lightweight graph maintenance routines are run. According to experimental results, SWINN is significantly faster than performing a naive complete scan of the data buffer while keeping competitive search recall values. We also apply SWINN to online classification and regression tasks and show that our proposal is effective against popular online machine learning algorithms.

2023

Online Anomaly Explanation: A Case Study on Predictive Maintenance

Authors
Ribeiro, RP; Mastelini, SM; Davari, N; Aminian, E; Veloso, B; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches due to their predictive accuracy and are based on deep-learning techniques. This paper presents an architecture that uses an online rule learning algorithm to explain when the black-box model predicts rare events. The system can present global explanations that model the black-box model and local explanations that describe why the black-box model predicts a failure. We evaluate the proposed system using four real-world public transport data sets, presenting illustrative examples of explanations.

2023

An Online Data-Driven Predictive Maintenance Approach for Railway Switches

Authors
Tome, ES; Ribeiro, RP; Veloso, B; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Abstract
An online data-driven predictive maintenance approach for railway switches using data logs obtained from the interlocking system of the railway infrastructure is proposed in this paper. The proposed approach is detailed described and consists of a two-phase process: anomaly detection and remaining useful life prediction. The approach is applied to and validated in a real case study, the Metro do Porto, from which seven months of data is available. The approach has been revealed to be satisfactory in detecting anomalies. The results open the possibilities for further studies and validation with a more extensive dataset on the remaining useful life prediction.

2023

Ethical and Technological AI Risks Classification: A Human Vs Machine Approach

Authors
Teixeira, S; Veloso, B; Rodrigues, JC; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsibleAI. Themain goal of thiswork is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.

2023

Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set

Authors
Davari, N; Veloso, B; Ribeiro, RP; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Abstract
The demand for high-performance solutions for anomaly detection and forecasting fault events is increasing in the industrial area. The detection and forecasting faults from time-series data are one critical mission in the Internet of Things (IoT) data mining. The classical fault detection approaches based on physical modelling are limited to some measurable output variables. Accurate physical modelling of vehicle dynamics requires substantial prior information about the system. On the other hand, data-driven modelling techniques accurately represent the system's dynamic from data collection. Experimental results on large-scale data sets from Metro do Porto subsystems verify that our method performs high-quality fault detection and forecasting solutions. Also, health indicator obtained from the principal component analysis of the forecasting solution is applied to predict the remaining useful life.

Supervised
thesis

2022

Intelligent Intrusion Detection System in IoT

Author
Maria Inês Pinto Bastos Martins

Institution
UP-FCUP

2022

Predicting Portuguese Local Elections Outcome: A Machine Learning approach

Author
Diogo João Teixeira Pinto da Costa

Institution
UP-FEP

2022

Data-driven Predictive Maintenance for Industry 4.0

Author
Emanuel Rodrigues de Sousa Tomé

Institution
UP-FCUP

2022

Evaluating Fairness, Explainability and Robustness of AI Systems

Author
Sérgio Gabriel Pontes de Jesus

Institution
UP-FCUP

2022

Social network changes among older adults caring for their older relatives

Author
Gabija Kisieliute

Institution
UP-FEP