Kalyan Moy Gupta · EDBT 2026 Demo

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

Artificial intelligence and machine learning · 12 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 4 · 1 first-authorHuman-computer interaction and ubiquitous computing · 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.

Artificial intelligence 1 paper		Graph learning · 50% Probabilistic and Bayesian machine learning · 50%
Databases, data mining, and information retrieval 1 paper		Data integration and cleaning · 100%
Software engineering, system software, and programming languages 1 paper		Software maintenance and evolution · 100%
Interdisciplinary, comprehensive, and emerging computing 1 paper		Computing education · 100%
Human-computer interaction and pervasive computing 1 paper		Human-AI interaction · 100%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Distributed systems · 100%

Topics — the 3 heaviest of 7, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Data integration and cleaning data mapping	0.1	1	2008	IMT: A Mixed-Initiative Data Mapping and Search Toolkit · AAAI 2008
Machine learning › Graph learning › graph neural network › node classification collective classification	0.1	1	2007	Cautious Inference in Collective Classification · AAAI 2007
Human-AI interaction mixed-initiative interaction	0.0	1	2008	IMT: A Mixed-Initiative Data Mapping and Search Toolkit · AAAI 2008

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

mixed-initiative interaction · 0.2

Year	Publication	Venue	Position
2019	NOD-CC: A Hybrid CBR-CNN Architecture for Novel Object Discovery J. T. Turner, Michael W. Floyd, Kalyan Moy Gupta, Tim Oates 0001	ICCBR	3
2018	Novel Object Discovery Using Case-Based Reasoning and Convolutional Neural Networks J. T. Turner, Michael W. Floyd, Kalyan Moy Gupta, David W. Aha	ICCBR	3
2014	Case-Based Object Placement Planning Kellen Gillespie, Kalyan Moy Gupta, Michael Drinkwater	ICCBR	2
2009	Case-Based Collective Inference for Maritime Object Classification Kalyan Moy Gupta, David W. Aha, Philip Moore 0002	ICCBR	1
2009	Cautious Collective Classification Luke K. McDowell, Kalyan Moy Gupta, David W. Aha	J. Mach. Learn. Res.	2
2008	Enabling the Interoperability of Large-Scale Legacy Systems Kalyan Moy Gupta, Michael Zang, Adam Gray, David W. Aha, Joe Kriege	AAAI	1
2008	IMT: A Mixed-Initiative Data Mapping and Search Toolkit Michael Zang, Adam Gray, Joe Kriege, Kalyan Moy Gupta, David W. Aha	AAAI	4
2008	Soft computing techniques for web services brokering Roy Ladner, Fred Petry, Kalyan Moy Gupta, Elizabeth Warner, Philip Moore 0002, David W. Aha	Soft Comput.	3
2007	Cautious Inference in Collective Classification Luke K. McDowell, Kalyan Moy Gupta, David W. Aha	AAAI	2
2001	Taxonomic Conversational Case-Based Reasoning Kalyan Moy Gupta	ICCBR	1
2001	Bridging the Lesson Distribution Gap David W. Aha, Rosina O. Weber, Hector Muñoz-Avila, Len Breslow, Kalyan Moy Gupta	IJCAI	5
1997	A connectionist approach for similarity assessment in case-based reasoning systems Kalyan Moy Gupta, Ali R. Montazemi	Decis. Support Syst.	1
1997	Empirical evaluation of retrieval in case-based reasoning systems using modified cosine matching function abstract Case-based reasoning (CBR) supports ill-structured decision making by retrieving previous cases that are useful toward the solution of a new decision problem. The usefulness of previous cases is determined by assessing the similarity of a new case with the previous cases. In this paper, we present a modified form of the cosine matching function that makes it possible to contrast the two cases being matched and to include differences in the importance of features in the new case and the importance of features in the previous case. Our empirical evaluation of a CBR application to a diagnosis and repair task in an electromechanical domain shows that the proposed modified cosine matching function has a superior retrieval performance when compared to the performance of nearest-neighbor and the Tversky's contrast matching functions. Kalyan Moy Gupta, Ali R. Montazemi	IEEE Trans. Syst. Man Cybern. Part A	1