Demo Data from VLDB 2026 / reviewers in the wild / expert
Daniela Socas Gil
dblp:216/5571
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Smart cities and intelligent transportation · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Smart cities and intelligent transportation
traffic estimation |
0.3 | 1 | 2017 | On the Challenges of Mobile Crowdsensing for Traffic Estimation · SenSys 2017 |
Ubiquitous computing and smart environments › mobile crowdsourcing › crowdsensing
mobile crowdsensing |
0.1 | 1 | 2017 | On the Challenges of Mobile Crowdsensing for Traffic Estimation · SenSys 2017 |
Methods — techniques the papers use, named apart from their topics
exploratory data analysis · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | On the Challenges of Mobile Crowdsensing for Traffic EstimationabstractTraffic congestion adversely impacts our lives. Traffic estimation resorting to mobile (crowdsensing) probes is a challenging task. We present key challenges for accurate and real-time traffic estimation resorting to crowdsensing data, namely data sparsity, user trip diversity, population bias, data quality, among others. We propose solutions to address some of these issues and demonstrate the relevance of others through an exploratory data analysis. Daniela Socas Gil, Pedro M. d'Orey, Ana Aguiar |
SenSys | 1 |