EDBT 2026 Demo / reviewers in the wild / expert
James Kelly
dblp:32/2715
· DBLP profile ↗
4ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 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
2 papers |
Data mining · 100% | |
| Artificial intelligence
2 papers |
Probabilistic and Bayesian machine learning · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
data mining system |
0.0 | 1 | 1997 | MineSet: An Integrated System for Data Mining · KDD 1997 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian classification |
0.0 | 2 | 1988 | AutoClass: A Bayesian Classification System · ML 1988 Bayesian Classification · AAAI 1988 |
Visualization and visual analytics
data visualization |
0.0 | 1 | 1997 | MineSet: An Integrated System for Data Mining · KDD 1997 |
Machine learning › Probabilistic and Bayesian machine learning
clustering |
0.0 | 1 | 1988 | AutoClass: A Bayesian Classification System · ML 1988 |
Data mining
clustering |
0.0 | 1 | 1988 | AutoClass: A Bayesian Classification System · ML 1988 |
Data mining › clustering › model-based clustering
mixture model clustering |
0.0 | 1 | 1988 | AutoClass: A Bayesian Classification System · ML 1988 |
Methods — techniques the papers use, named apart from their topics
expectation-maximization · 0.0bayesian inference · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Developing a Concept Inventory for Computer Science 2: What should it focus on and what makes it challenging?abstractThis Work-In-Progress Research Paper reports on an international study that is being undertaken in order to develop a validated concept inventory for the second introductory computer science course (CS2).A concept inventory is a research-based multiple-choice test that measures a student's knowledge of a set of concepts while also capturing conceptions and misconceptions they may have about the topic under consideration. Development of a concept inventory for a course requires identifying course topics that are both difficult and important. This paper details how the Delphi method is being used to develop a concept inventory for CS2; in particular, it focuses on the initial process of identifying the set of topics that should be covered by a concept inventory for CS2. Lea Wittie, Anastasia Kurdia, Judy Peng, James Kelly, Meriel Huggard |
FIE | 4 |
| 1997 | MineSet: An Integrated System for Data Mining
Clifford Brunk, James Kelly, Ron Kohavi |
KDD | 2 |
| 1988 | Bayesian Classification
Peter C. Cheeseman, Matthew Self, James Kelly, Will Taylor, Don Freeman, John C. Stutz |
AAAI | 3 |
| 1988 | AutoClass: A Bayesian Classification System
Peter C. Cheeseman, James Kelly, Matthew Self, John C. Stutz, Will Taylor, Don Freeman |
ML | 2 |