Publicacoes - INESC TEC

Publicações

Publicações por LIAAD

2019

Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part II

Autores
Li, G; Yang, J; Gama, J; Natwichai, J; Tong, Y;

Publicação
DASFAA (2)

Abstract

2019

Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part III, and DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Chiang Mai, Thailand, April 22-25, 2019, Proceedings

Autores
Li, G; Yang, J; Gama, J; Natwichai, J; Tong, Y;

Publicação
DASFAA Workshops

Abstract

2019

Detecting Bursts of Activity in Telecommunications

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

Publicação
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.

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 a solution for a real problem called Interconnect Bypass Fraud. This problem is one of the most expressive in the telecommunication domain and can be detected by the occurrence of burst of calls from specific numbers. Based on this assumption, we propose the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm. Our goal is to detect as soon as possible items with abnormal behaviours, e.g. bursts of calls, repetitions and mirror behaviours. The results shows that our technique not only complements the techniques used by the telecom company but also improves the performance of the Lossy Counting algorithm in terms of runtime, memory used and sensibility to detect the abnormal behaviours. Copyright © by the paper's authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

FecharLer Abstract

2019

Machine learning for streaming data: state of the art, challenges, and opportunities

Autores
Gomes, HM; Read, J; Bifet, A; Barddal, JP; Gama, J;

Publicação
SIGKDD Explorations

Abstract

2019

Novelty Detection for Multi-Label Stream Classification

Autores
Costa Júnior, JD; de Faria, ER; Andrade Silva, Jd; Gama, J; Cerri, R;

Publicação
8th Brazilian Conference on Intelligent Systems, BRACIS 2019, Salvador, Brazil, October 15-18, 2019

Abstract

2019

Special Issue of DASFAA 2019

Autores
Li, G; Gama, J; Yang, J;

Publicação
Data Science and Engineering

Abstract