A Distributed Microservice Architecture Pattern for the Automated Generation of Information Extraction Pipelines
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Published:2023-10-28
Issue:6
Volume:4
Page:
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ISSN:2661-8907
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Container-title:SN Computer Science
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language:en
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Short-container-title:SN COMPUT. SCI.
Author:
Sildatke MichaelORCID,
Karwanni Hendrik,
Kraft Bodo,
Zündorf Albert
Abstract
AbstractCompanies often build their businesses based on product information and therefore try to automate the process of information extraction (IE). Since the information source is usually heterogeneous and non-standardized, classic extract, transform, load techniques reach their limits. Hence, companies must implement the newest findings from research to tackle the challenges of process automation. They require a flexible and robust system that is extendable and ensures the optimal processing of the different document types. This paper provides a distributed microservice architecture pattern that enables the automated generation of IE pipelines. Since their optimal design is individual for each input document, the system ensures the ad-hoc generation of pipelines depending on specific document characteristics at runtime. Furthermore, it introduces the automated quality determination of each available pipeline and controls the integration of new microservices based on their impact on the business value. The introduced system enables fast prototyping of the newest approaches from research and supports companies in automating their IE processes. Based on the automated quality determination, it ensures that the generated pipelines always meet defined business requirements when they come into productive use.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science
Reference29 articles.
1. Cardie C. Empirical Methods in Information Extraction. AI Mag. 1997;18(4):5. https://doi.org/10.1609/aimag.v18i4.1322.
2. Hashmi KA, et al. Current status and performance analysis of table recognition in document images with deep neural networks. http://arxiv.org/abs/2104.14272. arXiv:2104.14272 (2021).
3. Hanson C, Sussman GJ. Software design for flexibility—how to avoid programming yourself into a corner. Cambridge: MIT Press; 2021.
4. Jamshidi P, Pahl C, Mendonça NC, Lewis J, Tilkov S. Microservices: the journey so far and challenges ahead. IEEE Softw. 2018;35(3):24–35.
5. Newman S. Building microservices: designing fine-grained systems. 1st ed. Sebastopol: O’Reilly Media; 2015.