Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

María-Del-Pilar Villamil

dblp:95/6334 · DBLP profile ↗
← Back
4ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-authorArtificial intelligence and machine learning · 2Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
anomaly detection		 0.312018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018
Data mining › anomaly detection
fraud detection		 0.312018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018
Data mining
clustering		 0.112018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018
Data mining › clustering
unsupervised learning		 0.112018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018

Methods — techniques the papers use, named apart from their topics

unsupervised machine learning · 0.7
YearPublicationVenuePosition
2019 A Survey on Big Data Analytics Solutions Deployment
Camilo Castellanos, Boris Perez, Carlos A. Varela, María-Del-Pilar Villamil, Darío Correal
 ECSA4
2018 Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach
abstract
Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment.
Daniel de Roux, Boris Perez, Andrés Moreno, María-Del-Pilar Villamil, César Figueroa
 KDD4
2010 Source Selection in Large Scale Data Contexts: An Optimization Approach
Alexandra Pomares-Quimbaya, Claudia Roncancio, Van-Dat Cung, José Abásolo, María-Del-Pilar Villamil
 DEXA (1)5
2004 PinS: Peer-to-Peer Interrogation and Indexing System
María-Del-Pilar Villamil, Claudia Roncancio, Cyril Labbé
 IDEAS1