Hybrid AI for data platforms

Vassilev, Vassil, Sowinski-Mydlarz, Wiktor and Ilieva, Sylvia (2021) Hybrid AI for data platforms. In: Data Platforms: Foundations, Design Space, and Deployments. Springer. (Submitted)

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Abstract / Description

The current digital transformation of many businesses and the exponential growth of digital data are two of the key factors of the digital revolution. For successful meeting of the high expectations the data platforms for processing digital data need to employ the recent theoretical, technological and methodological advances in contemporary computer and communication science and engineering. This chapter presents an approach to address these challenges by combining logical methods for knowledge processing and machine learning methods for data analysis into an integrated hybrid AI framework for intelligent data processing. This framework can be applied to a wide range of problems which involve both synchronous operations and asynchronous events in different domains. The implementation of this framework will utilize several recent distributed technologies such as Internet-of-Things, Cloud and Edge computing and will integrate them into a multi-level service-oriented architecture that supports multiple services along the entire data value chain. The workflow orchestration of data services on the platform enables high degree of interoperability, reusability and automation. The framework is a foundation for building the GATE Data Platform, which aims at application of Big Data technologies in civil and government services, industry and healthcare. The platform is designed using mainly open components and tools, which makes it additionally attractive for SMEs, although its extensible architecture will provide the possibility to mix them with commercial solutions.

Item Type: Book Section
Uncontrolled Keywords: hybrid AI; explainable AI; ontologies; multi-layer architecture; service-oriented architecture; data platform
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Vassil Vassilev
Date Deposited: 31 Mar 2021 08:53
Last Modified: 31 Mar 2021 08:53
URI: http://repository.londonmet.ac.uk/id/eprint/6475

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