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David Bowden

dblp:236/7431 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—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.

Computer networks
1 paper
Edge and fog computing · 100%
Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

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

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments › pervasive computing infrastructure
distributed camera networks
0.212023
Semantic Programming for Device-Edge-Cloud Continuum · ICNP 2023
Distributed systems › middleware
distributed runtime
0.212023
Semantic Programming for Device-Edge-Cloud Continuum · ICNP 2023

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

probabilistic weights · 2.0neural-symbolic stream fusion · 2.0declarative rules · 2.0
YearPublicationVenuePosition
2023 Semantic Programming for Device-Edge-Cloud Continuum
abstract
This position paper presents ThothSP, a Semantic Programming framework with the aim of lowering the coding effort in building smart applications on the Device-Edge-Cloud continuum by leveraging semantic knowledge. It introduces a novel neural-symbolic stream fusion mechanism, which enables the specification of data fusion pipelines via declarative rules, with degrees of learnable probabilistic weights. Moreover, it includes an adaptive federator that allows the Thoth⊳runtime to be distributed across multiple compute nodes in a network, and to coordinate their resources to collaboratively process tasks by delegating partial workloads to their peers. To demonstrate ThothSP's capability, we report a case study on a distributed camera network to show ThothSP's behaviour against a traditional edge-cloud setup.
Anh Le-Tuan, David Bowden, Danh Le Phuoc
ICNP2