Multi-model query languages: taming the variety of big data

Abstract

A critical issue in Big Data management is to address the variety of data–data are produced by disparate sources, presented in various formats, and hence inherently involves multiple data models. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating multi-model data in a single system and querying across them with a unified query language. This article aims to offer a comprehensive survey of a wide range of multi-model query languages of MMDBs. In particular, we first present the SQL-based extensions toward multi-model data, including the standard SQL extensions such as SQL/XML, SQL/JSON, and GQL, and the non-standard SQL extensions such as SQL++ and SPASQL. We then study the manners in which document-based and graph-based query languages can be extended to support multi-model data. We also investigate the query languages that provide native support on multi-model data. Finally, this article provides insights into the open challenges and problems of multi-model query languages.

Optimal Training Dataset Preparation for AI-Supported Multilanguage Real-Time OCRs Using Visual Methods

“…The formulation of an optimal, vocabulary-based training dataset for multilingual, realtime OCR systems necessitates a strategic approach to various key elements. These include the careful selection of adaptive vocabulary, implementation of cross-lingual pretraining [22], deployment of dynamic dataset augmentation strategies, ensuring domain-specific adaptation, addressing data imbalance issues, applying multimodal learning, and establishing appropriate evaluation metrics and benchmarks. [23].…”