Demo Data from EDBT 2026 / reviewers in the wild / expert
Mani Abrol
dblp:77/736
· DBLP profile ↗
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
1 paper |
Data models and query languages · 77% Information retrieval · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data models and query languages
semistructured data |
0.0 | 1 | 2001 | Navigating large-scale semi-structured data in business portals · VLDB 2001 |
Information retrieval › document retrieval › structured document retrieval
semi-structured retrieval |
0.0 | 1 | 2001 | Navigating large-scale semi-structured data in business portals · VLDB 2001 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Intelligent Taxonomy Management Tools for Enterprise ContentabstractThe taxonomy is the most popular way for organizing a large volume of content. A taxonomy, which is typically a hierarchical representation of categories, provides a navigation structure for exploring and understanding the underlying corpus without sifting through a huge volume of documents. Creating and maintaining taxonomies with a large volume of documents remains a daunting task facing many enterprises. Content organization process typically involves four basic steps: (i) creating taxonomies; (ii) building classification models; (iii) populating taxonomies with documents; and (iv) deploying populated taxonomies in enterprise portals. Each step in the process may have unique requirements that determine what techniques and tools are suitable for the tasks. In this paper, we present a comprehensive suite of tools developed by Verity Inc. for accurately, collaboratively, and efficiently organizing enterprise content. Mani Abrol, Bhavin Doshi 0002, Jim Kanihan, Jianchang Mao |
Web Intelligence | 1 |
| 2001 | Navigating large-scale semi-structured data in business portals
Mani Abrol, Neil Latarche, Uma Mahadevan, Jianchang Mao, Rajat Mukherjee, Prabhakar Raghavan, Michel Tourn, Grace Zhang |
VLDB | 1 |