Rihan Hai

20 papers receiving 316 citations

Peers

Rihan Hai
Comparison fields: 5 of 77
  • Management Science and Operations Research 121
  • Information Systems and Management 62
  • Health Informatics 10
  • Industrial and Manufacturing Engineering 44
  • Computer Networks and Communications 101
Replace Raoul-Gabriel Urma with:
Raoul-Gabriel Urma United Kingdom
Antônio Espósito Italy
Alexander Tolstoy Russia
Ianire Taboada Spain
Katherine L. Morse United States
István Dávid Canada
Christian Neureiter Austria
Ridha Khédri Canada
Kareem S. Aggour United States
Per-Olov Östberg Sweden
Rihan Hai relative to Raoul-Gabriel Urma United Kingdom Raoul-Gabriel Urma's profile →
Citations per field
00.5×9.4×
Raoul-Gabriel Urma · 1×
Citations per year

Countries citing papers authored by Rihan Hai

Since Specialization
Citations

This map shows the geographic impact of Rihan Hai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rihan Hai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rihan Hai more than expected).

Fields of papers citing papers by Rihan Hai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rihan Hai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rihan Hai. The network helps show where Rihan Hai may publish in the future.

Co-authors

The 25 scholars most cited alongside Rihan Hai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Rihan Hai Line = papers co-authored together Rihan Hai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016107
2 201966
3 202333
4 202227
5 201627
6
GEMMS: A Generic and Extensible Metadata Management System for Data Lakes.
201619
7 202014
8 20245
9 20244
10 20194
11 20234
12 20233
13 20222
14 20241
15
Ontology matching for patent classification
20171
16 20241
17 20251
18 20241
19 20151
20 20201

About Rihan Hai

Rihan Hai is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 27 papers that have together received 323 indexed citations. Recurring topics across this work include Data Quality and Management (9 papers), Advanced Database Systems and Queries (6 papers), Semantic Web and Ontologies (5 papers), Data Management and Algorithms (4 papers), Scientific Computing and Data Management (4 papers), Graph Theory and Algorithms (4 papers), Machine Learning and Data Classification (4 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Management Science and Operations Research (121 citations), Information Systems and Management (62 citations), Health Informatics (10 citations), Industrial and Manufacturing Engineering (44 citations) and Computer Networks and Communications (101 citations). Rihan Hai has collaborated with scholars based in Netherlands, Germany and Taiwan. Frequent co-authors include Christoph Quix, Sandra Geisler, Matthias Jarke, Jan Pennekamp, Martin Henze, Klaus Wehrle, Andreas Bührig–Polaczek, Philipp Niemietz, Asterios Katsifodimos and Tobias Meisen. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Access, IEEE Transactions on Knowledge and Data Engineering, Distributed and Parallel Databases and Journal of Medical Internet Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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