Demo Data from VLDB 2026 / reviewers in the wild / expert
Kejun Bi
dblp:360/5171
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2025
0009-0004-2233-3591ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Emerging computing paradigms · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Emerging computing paradigms
quantum computer architecture |
0.8 | 1 | 2024 | A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping · IEEE Trans. Computers 2024 |
Emerging computing paradigms › quantum computer architecture
qubit mapping |
0.8 | 1 | 2024 | A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping · IEEE Trans. Computers 2024 |
Emerging computing paradigms › quantum computing
NISQ |
0.2 | 1 | 2024 | A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping · IEEE Trans. Computers 2024 |
Methods — techniques the papers use, named apart from their topics
mutual-influence-aware heuristic · 0.8iterated local search · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Knowledge Graph Reasoning with Hierarchical Attention-Based Temporal Aggregation for Industrial Chain Risk Prediction
Yongjiao Sun, Anrui Han, Xin Bi 0001, Kejun Bi, Hangxu Ji |
ADMA (4) | 5 |
| 2025 | Enterprise risk assessment model based on graph attention networks
Kejun Bi, Chuanjie Liu |
Appl. Intell. | 1 |
| 2024 | A Mutual-Influence-Aware Heuristic Method for Quantum Circuit MappingabstractQuantum circuit mapping (QCM) is a crucial preprocessing step for executing a logical circuit (LC) on noisy intermediate-scale quantum (NISQ) devices. Balancing the introduction of extra gates and the efficiency of preprocessing poses a significant challenge for the mapping process. To address this challenge, we propose the mutual-influence-aware (MIA) heuristic method by integrating an initial mapping search framework, an initial mapping generator, and a heuristic circuit mapper. Initially, the framework utilizes the generator to obtain a favorable starting point for the initial mapping search. With this starting point, the search process can efficiently discover a promising initial mapping within a few bidirectional iterations. The circuit mapper considers mutual influences of SWAP gates and is invoked once per iteration. Ultimately, the best result from all iterations is considered the QCM outcome. The experimental results on extensive benchmark circuits demonstrate that, compared to the iterated local search (ILS) method, which represents the current state-of-the-art, our MIA method introduces a similar number of extra gates while achieving nearly 95 times faster execution. Kui Ye, Shengxin Dai, Bing Guo 0003, Yan Shen 0001, Chuanjie Liu, Kejun Bi, Yuchuan Hu, Mingjie Zhao 0002 |
IEEE Trans. Computers | 6 |