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Eng."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1007\/s41019-022-00196-2","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T12:05:28Z","timestamp":1665576328000},"page":"328-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Adaptive Elastic Multi-model Big Data Analysis and Information Extraction System"],"prefix":"10.1007","volume":"7","author":[{"given":"Qiang","family":"Yin","sequence":"first","affiliation":[]},{"given":"Jianhua","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Sheng","family":"Du","sequence":"additional","affiliation":[]},{"given":"Jianquan","family":"Leng","sequence":"additional","affiliation":[]},{"given":"Jintao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yinhao","family":"Hong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1983-7321","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yunpeng","family":"Chai","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaonan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Mengyu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Song","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"196_CR1","unstructured":"HL Chieu and HT Ng (2002) A maximum entropy approach to information extraction from semi-structured and free text. 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