TY - GEN
T1 - PADL
T2 - 5th International Conference on Smart and Sustainable Technologies, SpliTech 2020
AU - Díaz-De-Arcaya, Josu
AU - Miñón, Raúl
AU - Torre-Bastida, Ana I.
AU - Del Ser, Javier
AU - Almeida, Aitor
N1 - Publisher Copyright:
© 2020 University of Split, FESB.
PY - 2020/9/23
Y1 - 2020/9/23
N2 - In this paper we introduce PADL, a language for modeling and deploying data-based analytical pipelines. The novelty of this language relies on its independence from both the infrastructure and the technologies used on it. Specifically, this descriptive language aims at embracing all the particularities and constraints of high-demanding deployment models, such as critical restrictions regarding latency, privacy and performance, by providing fully-compliant schemas for implementing data analytical workloads. The adoption of PADL provides means for the operationalization of these pipelines in a reproducible and resilient fashion. In addition, PADL is able to fully utilize the benefits of Edge and Fog computing layers. The feasibility of the language has been validated with an analytical pipeline deployed over an Edge computing environment to solve an Industry 4.0 use case. The promising results obtained therefrom pave the way towards the widespread adoption of our proposed language when deploying data analytical pipelines over real application scenarios.
AB - In this paper we introduce PADL, a language for modeling and deploying data-based analytical pipelines. The novelty of this language relies on its independence from both the infrastructure and the technologies used on it. Specifically, this descriptive language aims at embracing all the particularities and constraints of high-demanding deployment models, such as critical restrictions regarding latency, privacy and performance, by providing fully-compliant schemas for implementing data analytical workloads. The adoption of PADL provides means for the operationalization of these pipelines in a reproducible and resilient fashion. In addition, PADL is able to fully utilize the benefits of Edge and Fog computing layers. The feasibility of the language has been validated with an analytical pipeline deployed over an Edge computing environment to solve an Industry 4.0 use case. The promising results obtained therefrom pave the way towards the widespread adoption of our proposed language when deploying data analytical pipelines over real application scenarios.
KW - AI domain specific language
KW - analytical pipelines
KW - edge computing
KW - machine learning life cycle
UR - http://www.scopus.com/inward/record.url?scp=85096562659&partnerID=8YFLogxK
U2 - 10.23919/SpliTech49282.2020.9243735
DO - 10.23919/SpliTech49282.2020.9243735
M3 - Conference contribution
AN - SCOPUS:85096562659
T3 - 2020 5th International Conference on Smart and Sustainable Technologies, SpliTech 2020
BT - 2020 5th International Conference on Smart and Sustainable Technologies, SpliTech 2020
A2 - Solic, Petar
A2 - Nizetic, Sandro
A2 - Rodrigues, Joel J. P. C.
A2 - Rodrigues, Joel J. P.C.
A2 - Lopez-de-Ipina Gonzalez-de-Artaza, Diego
A2 - Perkovic, Toni
A2 - Catarinucci, Luca
A2 - Patrono, Luigi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 September 2020 through 26 September 2020
ER -
