Vijay Khatri

1.8k total citations · 1 hit paper
31 papers, 1.1k citations indexed

About

Vijay Khatri is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Vijay Khatri has authored 31 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 8 papers in Computer Networks and Communications and 8 papers in Signal Processing. Recurrent topics in Vijay Khatri's work include Semantic Web and Ontologies (9 papers), Data Management and Algorithms (8 papers) and Advanced Database Systems and Queries (7 papers). Vijay Khatri is often cited by papers focused on Semantic Web and Ontologies (9 papers), Data Management and Algorithms (8 papers) and Advanced Database Systems and Queries (7 papers). Vijay Khatri collaborates with scholars based in United States, Australia and India. Vijay Khatri's co-authors include Carol V. Brown, V. Ramesh, Anne Massey, Mitzi M. Montoya‐Weiss, Iris Vessey, Frank Acito, Sudha Ram, Paul Clay, Sungjin Park and Andrew Massey and has published in prestigious journals such as Communications of the ACM, MIS Quarterly and IEEE Transactions on Software Engineering.

In The Last Decade

Vijay Khatri

30 papers receiving 1.0k citations

Hit Papers

Designing data governance 2009 2026 2014 2020 2009 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Designing data governance
    2009 388 citations Vijay Khatri et al. Communications of the ACM profile →

Peers

Vijay Khatri
Mark Lycett United Kingdom
Minder Chen United States
InduShobha Chengalur‐Smith United States
William H. Money United States
Jöerg Evermann Canada
Amit Basu United States
Mathias Klier Germany
Andrew Gemino Canada
Christian Janiesch Germany
Niv Ahituv Israel
Mark Lycett United Kingdom
Vijay Khatri
Citations per year, relative to Vijay Khatri Vijay Khatri (= 1×) peers Mark Lycett

Countries citing papers authored by Vijay Khatri

Since Specialization
Citations

This map shows the geographic impact of Vijay Khatri'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 Vijay Khatri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vijay Khatri more than expected).

Fields of papers citing papers by Vijay Khatri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vijay Khatri. 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 Vijay Khatri. The network helps show where Vijay Khatri may publish in the future.

Co-authorship network of co-authors of Vijay Khatri

This figure shows the co-authorship network connecting the top 25 collaborators of Vijay Khatri. A scholar is included among the top collaborators of Vijay Khatri based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vijay Khatri. Vijay Khatri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Khatri, Vijay, et al.. (2025). Biological AIE Molecules: Innovations in Synthetic Design and AI‐Driven Discovery. Advanced Biology. 9(8). e2400792–e2400792. 5 indexed citations
2.
Khatri, Vijay, et al.. (2025). Assembly of Branched Chain Amino Acids to Toxic Fibrils may be Related to Pathogenesis of Maple Syrup Urine Disease. ChemBioChem. 26(13). e202500092–e202500092.
3.
Khatri, Vijay, et al.. (2019). BODY DONATION FOR LEARNING AND RESEARCH IN MEDICAL SCIENCE: A REVIEW OF PREVALENT TRENDS AND PERCEPTION OF DONORS. International journal of scientific research. 8(11). 1 indexed citations
4.
Khatri, Vijay, et al.. (2019). Analytics for managerial work. Communications of the ACM. 62(4). 100–100. 14 indexed citations
5.
Khatri, Vijay, et al.. (2018). Exploring the Effects of Extensional Versus Intensional Representations on Domain Understanding1. MIS Quarterly. 42(4). 1187–1209. 14 indexed citations
6.
Khatri, Vijay & Iris Vessey. (2016). Understanding the Role of IS and Application Domain Knowledge on Conceptual Schema Problem Solving: A Verbal Protocol Study. Journal of the Association for Information Systems. 17(12). 759–803. 10 indexed citations
7.
Khatri, Vijay, et al.. (2014). Customizing the Representation Capabilities of Process Models: Understanding the Effects of Perceived Modeling Impediments. IEEE Transactions on Software Engineering. 41(1). 19–39. 11 indexed citations
8.
Acito, Frank & Vijay Khatri. (2014). Business analytics: Why now and what next?. Business Horizons. 57(5). 565–570. 84 indexed citations
9.
Massey, Anne, Vijay Khatri, & Randall K. Minas. (2013). The Influence of Psychographic Beliefs on Website Usability Requirements. AIS Transactions on Human-Computer Interaction. 5(4). 157–174. 7 indexed citations
10.
Khatri, Vijay, Sudha Ram, Richard T. Snodgrass, & Paolo Terenziani. (2012). Capturing Telic/Atelic Temporal Data Semantics: Generalizing Conventional Conceptual Models. IEEE Transactions on Knowledge and Data Engineering. 26(3). 528–548. 7 indexed citations
11.
Khatri, Vijay, et al.. (2008). Understanding Use Cases: Harnessing the Power of Narratives to Comprehend Application Domains. Journal of the Association for Information Systems. 203. 1 indexed citations
12.
Khatri, Vijay & Iris Vessey. (2008). Information search process for a well-structured IS problem: The role of IS and application domain knowledge. Journal of the Association for Information Systems. 14. 206. 1 indexed citations
13.
Massey, Anne, Vijay Khatri, & Mitzi M. Montoya‐Weiss. (2008). Online Services, Customer Characteristics and Usability Requirements. 18. 33–33. 6 indexed citations
14.
Khatri, Vijay, Iris Vessey, Sudha Ram, & V. Ramesh. (2006). Cognitive Fit Between Conceptual Schemas and Internal Problem Representations: The Case of Geospatio–Temporal Conceptual Schema Comprehension. IEEE Transactions on Professional Communication. 49(2). 109–127. 35 indexed citations
15.
Khatri, Vijay, Iris Vessey, V. Ramesh, Paul Clay, & Sungjin Park. (2006). Understanding Conceptual Schemas: Exploring the Role of Application and IS Domain Knowledge. Information Systems Research. 17(1). 81–99. 137 indexed citations
16.
Bajaj, Akhilesh, et al.. (2006). Advances in Data Modeling Research. Communications of the Association for Information Systems. 17. 2 indexed citations
17.
Ram, Sudha & Vijay Khatri. (2003). A comprehensive framework for modeling set-based business rules during conceptual database design. Information Systems. 30(2). 89–118. 23 indexed citations
18.
Khatri, Vijay. (2002). Bridging the spatio-temporal semantic gap : a theoretical framework, evaluation and a prototype system. UA Campus Repository (The University of Arizona). 4 indexed citations
19.
Khatri, Vijay, et al.. (2002). Supporting User-Defined Granularities in a Spatiotemporal Conceptual Model. Annals of Mathematics and Artificial Intelligence. 36(1-2). 195–232. 14 indexed citations
20.
Khatri, Vijay, Sudha Ram, Richard T. Snodgrass, et al.. (2001). ST USM: Bridging the Semantic Gap with a Spatio-Temporal Conceptual Model. 5 indexed citations

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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