Valentin Tudor

dblp:124/5156 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2020
0000-0001-6616-7266ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-authorSecurity and privacy · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1

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
Edge and fog computing · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › distributed coordination
decentralized orchestration
0.412020
ERAIA - Enabling Intelligence Data Pipelines for IoT-based Application Systems · PerCom 2020

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

migration · 0.9actor model · 0.9
YearPublicationVenuePosition
2020 ERAIA - Enabling Intelligence Data Pipelines for IoT-based Application Systems
abstract
Establishing upon the connectivity layer provided by Internet of Things (IoT) platforms, modern industries are moving towards management and computation solutions which enable Artificial Intelligence (AI) services for data intensive applications. This raises two important challenges: first, the information carried by data should be refined and prepared for the various AI algorithms via data processing pipelines and; second, a distributed orchestration solution for data and AI computation resources featuring with migration capabilities is required to support the refining process. In order to address these challenges, this paper introduces ERAIA, an actor-based framework which provides a novel basis to build intelligence and data pipelines. ERAIA facilitates the deployment and migration of distributed AI computations for heterogeneous and dynamic IoT scenarios. An implementation description is accompanied by relevant performance evaluations to demonstrate the flexibility and scalability of the solution. ERAIA provides an interface to expand the scope of existing IoT systems as Application Enablement Platform (AEP), which hence accelerates the development of AI-based IoT solutions.
Aitor Hernandez Herranz, Valentin Tudor
PerCom3
2020 BES: Differentially private event aggregation for large-scale IoT-based systems
Valentin Tudor, Vincenzo Gulisano, Magnus Almgren, Marina Papatriantafilou
Future Gener. Comput. Syst.1
2018 The influence of dataset characteristics on privacy preserving methods in the advanced metering infrastructure
Valentin Tudor, Magnus Almgren, Marina Papatriantafilou
Comput. Secur.1