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

Mahesh Gupta

dblp:92/9574 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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
Knowledge graphs · 44% Data models and query languages · 44% Graph data management · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge graphs
knowledge graph querying
0.212014
GQBE: Querying knowledge graphs by example entity tuples · ICDE 2014
Data models and query languages › query interface
query by example
0.212014
GQBE: Querying knowledge graphs by example entity tuples · ICDE 2014
Graph data management
graph query processing
0.112014
GQBE: Querying knowledge graphs by example entity tuples · ICDE 2014

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

weighted maximum query graph · 0.2top-k approximate matching · 0.2
YearPublicationVenuePosition
2015 Throughput accounting and performance of a manufacturing company under stochastic demand and scrap rates
Olli-Pekka Hilmola, Mahesh Gupta
Expert Syst. Appl.2
2014 GQBE: Querying knowledge graphs by example entity tuples
abstract
We present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world knowledge graphs are difficult to use due to their sheer size and complexity and the challenging task of writing complex structured graph queries. Toward better usability of query systems over knowledge graphs, GQBE allows users to query knowledge graphs by example entity tuples without writing complex queries. In this demo we present: 1) a detailed description of the various features and user-friendly GUI of GQBE, 2) a brief description of the system architecture, and 3) a demonstration scenario that we intend to show the audience.
Nandish Jayaram, Mahesh Gupta, Arijit Khan 0001, Chengkai Li 0001, Xifeng Yan, Ramez Elmasri
ICDE2