Toward a General Framework for Multimodal Big Data Analysis

Autor:	Valerio Bellandi, Paolo Ceravolo, Samira Maghool, Stefano Siccardi
Rok vydání:	2022
Předmět:	Big Data Data Analysis Machine Learning data fusion Electronic Data Processing multimodal analysis Information Systems and Management Settore INF/01 - Informatica Information Storage and Retrieval big graph Computer Science Applications Information Systems
Zdroj:	Big Data. 10:408-424
ISSN:	2167-647X 2167-6461
Popis:	Multimodal Analytics in Big Data architectures implies compounded configurations of the data processing tasks. Each modality in data requires specific analytics that triggers specific data processing tasks. Scalability can be reached at the cost of an attentive calibration of the resources shared by the different tasks searching for a trade-off with the multiple requirements they impose. We propose a methodology to address multimodal analytics within the same data processing approach to get a simplified architecture that can fully exploit the potential of the parallel processing of Big Data infrastructures. Multiple data sources are first integrated into a unified knowledge graph (KG). Different modalities of data are addressed by specifying
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88655be65280d10c6d38bb5c4757ada9 https://doi.org/10.1089/big.2021.0326 Zobrazit plný text záznamu