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The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, etc. Considering the fact that the mature relational databases established on relational data model are still predominant in today’s market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases’ capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey could motivate new mapping approaches through drawing lessons from each model’s mapping strategies, as well as a new research topic - mapping multi-model data into relational tables.

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How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant in the current market, it is expensive to replace them with others. Besides, due to the theories and technologies of RDBMSs having been enhanced over decades, it is hard to use few years to develop a multimodel database that can be compared with existing RDBMSs in handling security, query optimization, transaction management, etc. In this paper, we reconsider employing relational databases to store and query multi-model data. Unfortunately, the mismatch between the complexity of multi-model data structure and the simplicity of flat relational tables makes this difficult. Against this challenge, we utilize the reinforcement learning (RL) method to learn a relational schema by interacting with an RDBMS. Instead of using the classic Q-learning algorithm, we propose a variant Q-learning algorithm, called Double Q-tables, to reduce the dimension of the original Qtable and improve learning efficiency. Experimental results show that our approach could learn a relational schema outperforming the existing multi-model storage schema in terms of query time and space consumption. CCS CONCEPTS• Information systems → Relational database model; • Theory of computation → Data structures and algorithms for data management.

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