Jon Sneyers

713 citations
30 papers · 319 · h-index 9

Impact in

Papers in

Jon Sneyers

27 papers receiving 285 citations

Peers

Jon Sneyers
Comparison fields: 5 of 34
  • Computer Vision and Pattern Recognition 178
  • Software 27
  • Signal Processing 55
  • Artificial Intelligence 130
  • Computer Graphics and Computer-Aided Design 13
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Vincent Vidal France
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Citations per field
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Citations per year

Countries citing papers authored by Jon Sneyers

Since Specialization
Citations

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

Fields of papers citing papers by Jon Sneyers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jon Sneyers, 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 Jon Sneyers Line = papers co-authored together Jon Sneyers links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201696
2 201970
3 200923
4
Dijkstra's algorithm with Fibonacci heaps: an executable description in CHR
200621
5 200920
6 202015
7 201013
8
Extending CHR with negation as absence
200610
9
The computational power and complexity of Constraint Handling Rules
20058
10
Join ordering for constraint handling rules
20076
11 20255
12 20134
13 20103
14
Generalized CHR Machines
20083
15
To CHR¬ or not to CHR¬ : Extending CHR with Negation as Absence
20063
16
Proceedings of the 9th International Workshop on Constraint Handling Rules (CHR 2012)
20122
17 20222
18
APOPCALEAPS: Automatic Music Generation with CHRiSM
20102
19 20202
20
CHRiSM: CHance Rules induce Statistical Models
20092

About Jon Sneyers

Jon Sneyers is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing and Computational Theory and Mathematics, having authored 30 papers that have together received 319 indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (10 papers), Constraint Satisfaction and Optimization (7 papers), Logic, programming, and type systems (6 papers), Advanced Database Systems and Queries (5 papers), AI-based Problem Solving and Planning (4 papers), Advanced Data Compression Techniques (4 papers), Video Coding and Compression Technologies (3 papers) and Algorithms and Data Compression (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (178 citations), Software (27 citations), Signal Processing (55 citations), Artificial Intelligence (130 citations) and Computer Graphics and Computer-Aided Design (13 citations). Jon Sneyers has collaborated with scholars based in Belgium, Germany and Switzerland. Frequent co-authors include Pieter Wuille, Tom Schrijvers, Bart Demoen, Luca Versari, Thom Frühwirth, Zoltán Szabadka, Iulia M. Comșa, Sebastián Gómez, Krzysztof Potempa and Thomas Fischbacher. Their work appears in journals such as Theory and Practice of Logic Programming, SMPTE Motion Imaging Journal, IEEE Access, ACM Transactions on Programming Languages and Systems and Lirias (KU Leuven).

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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