Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Milo Dreyfus

dblp:359/0241 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0002-8557-9949ORCID · reported

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.

Computer networks
1 paper
Network measurement and analytics · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network measurement and analytics › geolocation
IP geolocation
0.712023
Replication: Towards a Publicly Available Internet Scale IP Geolocation Dataset · IMC 2023
Network measurement and analytics › network measurement methodology
measurement reproducibility
0.712023
Replication: Towards a Publicly Available Internet Scale IP Geolocation Dataset · IMC 2023

Methods — techniques the papers use, named apart from their topics

vantage-point probing · 0.7active measurement · 0.7
YearPublicationVenuePosition
2023 Replication: Towards a Publicly Available Internet Scale IP Geolocation Dataset
abstract
IP geolocation is one of the most widely used forms of metadata for IP addresses, and despite almost twenty years of effort from the research community, the reality is that there is no accurate, complete, up-to-date, and explainable publicly available dataset for IP geolocation. We argue that a central reason for this state of affairs is the impressive results from prior publications, both in terms of accuracy and coverage: up to street level accuracy and locating millions of IP addresses with a few hundred vantage points in months. We believe the community would substantially benefit from a public baseline dataset and code. To encourage future research in IP geolocation, we replicate two geolocation techniques and evaluate their accuracy and coverage. We show that we can neither use the first technique to obtain the previously claimed street level accuracy, nor the second to geolocate millions of IP addresses on today's Internet and with publicly available measurement infrastructure. In addition to this reappraisal, we re-evaluate the fundamental insights that led to these prior results, as well as provide new insights and recommendations to help the design of future geolocation techniques. All of our code and data are publicly available to support reproducibility.
Omar Darwich, Hugo Rimlinger, Milo Dreyfus, Matthieu Gouel, Kevin Vermeulen
IMC3