Demo Data from EDBT 2026 / reviewers in the wild / expert
Longhao Yi
dblp:413/8240
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 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.
| Theoretical computer science
1 paper |
Approximation and online algorithms · 67% Algorithmic game theory and mechanism design · 33% | |
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Approximation and online algorithms › online algorithms
competitive analysis |
0.9 | 1 | 2025 | Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks · IEEE Trans. Mob. Comput. 2025 |
Algorithmic game theory and mechanism design › mechanism design
crowdsourcing |
0.9 | 1 | 2025 | Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks · IEEE Trans. Mob. Comput. 2025 |
Approximation and online algorithms › online algorithms
online scheduling |
0.9 | 1 | 2025 | Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks · IEEE Trans. Mob. Comput. 2025 |
Ubiquitous computing and smart environments
mobile crowdsourcing |
0.3 | 1 | 2025 | Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks · IEEE Trans. Mob. Comput. 2025 |
Methods — techniques the papers use, named apart from their topics
competitive analysis · 1.7approximation algorithms · 0.9approximation algorithm · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Task Scheduling Mechanism for Crowdsourcing in Mobile Social NetworksabstractWith the popularization of smart phones, mobile crowdsourcing emerged and gained growing attention in the recent years. Mobile users are now able to conduct complex tasks with the communication between each other. In this paper, we study the task scheduling problem in the mobile crowdsourcing systems based on the spontaneously formed mobile social networks (MSNs). We introduce two crowdsourcing task scheduling problems under this system model, with one problem aiming to minimize the total cost of some crowdsourcing tasks and the other focusing on minimizing the final completion time of the tasks belonging to the same project. We introduce a unified framework to solve the problems and propose two approximation algorithms for these two problems in the offline versions respectively and prove Their approximation ratios accordingly. Based on the two algorithms, we further design two online algorithms to deal with the workers' dynamism and also analyze their competitive ratios. Finally, we verify the effectiveness and efficiency of the proposed methods through numerical experiments on real and synthetic datasets. Longhao Yi, Xiaofeng Gao 0001, Shahzad Sarwar Bhatti, Guihai Chen |
IEEE Trans. Mob. Comput. | 2 |