Image Classification with Knowledge-Based Systems on the Edge for Real-Time Danger Avoidance in Robots

Hegemier, Henri; Kelley, Jaimie

doi:10.1109/aiiot52608.2021.9454183

Cited by 3 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the components of these systems act as independent entities that interact toward a goal. They can act as sensors that transmit data to cloud servers [16,48,55,58,62,86,101] or systems' subcomponents that hide their own data model [47,67,98,99,111].…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

“…For example, systems can use API calls to collect data from data sources [10,81] or to receive requests from and send responses to end users [49] reviewed papers (i.e., 56.5%) do not adopt data coupling as data is hidden by each systems' components and these components interact using traditional process calls. They can use API calls [14,31,48,55,62,67,75,86,99,102,109] or remote procedure calls (RPC) [8,15,16,29,43,47,57,58,98,101,103,111,123,126] for communication. We found that not all the systems where components share a common data model necessarily follow the data coupling sub-principle.…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

“…They have distributed devices acting as sensors, which can store some data but mainly transmit it to cloud nodes where the main processing takes place. Among these devices we found IoT sensors [9,14,16,36,48,55,75,86,102,112], smartphones [29], robots [58], and UAVs [101]. We found that 30.4% of the reviewed systems prioritise local processing before requesting centralised entities (e.g., cloud servers).…”

Section: Prioritise Decentralisationmentioning

confidence: 99%

“…Workflow orchestration tools, such as Apache Airflow, can also be used for similar purposes [22]. Fifeteen percent of the reviewed systems partially adopt the local first principle [8,29,58,101,102]. Different nodes are in charge of different stages of learning models' life-cycle in these systems.…”

Section: Prioritise Decentralisationmentioning

confidence: 99%

“…The rest of the papers (i.e., 87%) do not follow this principle. Either they are implemented as centralised architectures [2,10,14,23,43,47,49,56,57,67,81,98,105,106,112,116], or federated architectures where there are no interactions between nodes at the same layer (e.g., servers at the edge layer) [8,9,16,29,31,36,48,55,58,62,66,75,83,86,99,[101][102][103].…”

Section: Prioritise Decentralisationmentioning

confidence: 99%

See 4 more Smart Citations

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

View full text Add to dashboard Cite

With the upsurge of interest in artificial intelligence machine learning (ML) algorithms, originally developed in academic environments, are now being deployed as parts of real-life systems that deal with large amounts of heterogeneous, dynamic, and high-dimensional data. Deployment of ML methods in real life is prone to challenges across the whole system life-cycle from data management to systems deployment, monitoring, and maintenance. Data-Oriented Architecture (DOA) is an emerging software engineering paradigm that has the potential to mitigate these challenges by proposing a set of principles to create data-driven, loosely coupled, decentralised, and open systems. However DOA as a concept is not widespread yet, and there is no common understanding of how it can be realised in practice. This review addresses that problem by contextualising the principles that underpin the DOA paradigm through the ML system challenges. We explore the extent to which current architectures of ML-based real-world systems have implemented the DOA principles. We also formulate open research challenges and directions for further development of the DOA paradigm.

show abstract