Manu Aery

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 VLDB 2026 / reviewers in the wild / expert

dblp:87/3756 · DBLP profile ↗

← Back

2ranked-venue papers

2as first author

0since 2021 · last 2005

—ORCID · none

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

Databases, data management, data science and information retrieval · 2 · 2 first-authorArtificial intelligence and machine learning · 1 · 1 first-author

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

2 papers

Data mining · 83% Graph data management · 13% Information retrieval · 4%

Topics — the 7 heaviest of 7, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Data mining › text mining › text classification email classification	0.1	2	2005	eMailSift: Email Classification Based on Structure and Content · ICDM 2005 eMailSift: mining-based approaches to email classification · SIGIR 2004
Data mining › text mining text classification	0.1	2	2005	eMailSift: Email Classification Based on Structure and Content · ICDM 2005 eMailSift: mining-based approaches to email classification · SIGIR 2004
Graph data management › graph matching approximate graph matching	0.1	1	2005	eMailSift: Email Classification Based on Structure and Content · ICDM 2005
Data mining › structured data mining graph mining	0.1	1	2005	eMailSift: Email Classification Based on Structure and Content · ICDM 2005
Data mining pattern mining	0.0	1	2004	eMailSift: mining-based approaches to email classification · SIGIR 2004
Data mining text mining	0.0	1	2004	eMailSift: mining-based approaches to email classification · SIGIR 2004
Information retrieval › document processing › document analysis document representation	0.0	1	2005	eMailSift: Email Classification Based on Structure and Content · ICDM 2005

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

structure extraction · 0.1graph matching · 0.1

Year	Publication	Venue	Position
2005	eMailSift: Email Classification Based on Structure and Content abstract In this paper we propose a novel approach that uses structure as well as the content of emails in a folder for email classification. Our approach is based on the premise that representative - common and recurring -structures/patterns can be extracted from a pre-classified email folder and the same can be used effectively for classifying incoming emails. A number of factors that influence representative structure extraction and the classification are analyzed conceptually and validated experimentally. In our approach, the notion of inexact graph match is leveraged for deriving structures that provide coverage for characterizing folder contents. Extensive experimentation validate the selection of parameters and the effectiveness of our approach for email classification. Manu Aery, Sharma Chakravarthy	ICDM	1
2004	eMailSift: mining-based approaches to email classification abstract No abstract available. Manu Aery, Sharma Chakravarthy	SIGIR	1