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An Adaptive Elastic Multi-model Big Data Analysis and Information Extraction System
- Source :
- Data Science and Engineering. 7:328-338
- Publication Year :
- 2022
- Publisher :
- Springer Science and Business Media LLC, 2022.
-
Abstract
- With the diverse applications to industry and domain-specific context, multi-source information extraction on semi-structured and unstructured data, as well as across data models, is becoming more common. However, multi-model information extraction often requires the deployment of multiple data model management, storage, and analysis subsystems on the cloud, many subsystems are not high-resource utilization at the same time, and the resource waste phenomenon is often serious. Therefore, an adaptive scalable multi-model big data analysis and information extraction system is designed and implemented in this paper, which can support data maintenance and cross-model query of relational, graph, document, key and other data models, and can provide efficient cross-model information extraction. On this basis, we can achieve the system resource allocation on demand and fast scaling mechanism, according to the real-time requirements of multi-model big data analysis, and dynamic adjustment of each subsystem resource allocation. Therefore, our solution not only guarantees multi-model query and information extraction performance and quality of service, but also significantly reduces the total consumption of system resources and cost.
- Subjects :
- Computational Mechanics
Computer Science Applications
Subjects
Details
- ISSN :
- 23641541 and 23641185
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Data Science and Engineering
- Accession number :
- edsair.doi...........418a709786640ba8a425a670b6827d6c