A
Athena Vakali
Researcher at Aristotle University of Thessaloniki
Publications - 278
Citations - 7836
Athena Vakali is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Computer science & The Internet. The author has an hindex of 36, co-authored 248 publications receiving 6740 citations. Previous affiliations of Athena Vakali include Purdue University.
Papers
More filters
Journal ArticleDOI
Cloud Computing: Distributed Internet Computing for IT and Scientific Research
TL;DR: This issue's articles tackle topics including architecture and management of cloud computing infrastructures, SaaS and IaaS applications, discovery of services and data in cloud computing infrastructure, and cross-platform interoperability.
Journal ArticleDOI
Community detection in Social Media
TL;DR: This survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles, placing special emphasis on the performance of existing methods in terms of computational complexity and memory requirements.
Journal ArticleDOI
Insight and perspectives for content delivery networks
George Pallis,Athena Vakali +1 more
TL;DR: Striking a balance between the costs for Web content providers and the quality of service for Web customers is a challenge.
Journal ArticleDOI
Content delivery networks: status and trends
Athena Vakali,George Pallis +1 more
TL;DR: An overview of the CDN architecture and popular CDN service providers can be found in this paper, where the authors offer an overview of some of the most popular service providers and their architecture.
Proceedings ArticleDOI
Large scale crowdsourcing and characterization of twitter abusive behavior
Antigoni Maria Founta,Constantinos Djouvas,Despoina Chatzakou,Ilias Leontiadis,Jeremy Blackburn,Gianluca Stringhini,Athena Vakali,Michael Sirivianos,Nicolas Kourtellis +8 more
TL;DR: The authors proposed an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels and identified a reduced but robust set of labels to characterize abusive-related tweets.