Conjunctive queries are the most basic and central class of database queries. With the continued growth of demands to manage and process the massive volume of different types of data, there is little research to study the conjunctive queries between relation and tree data. In this paper, we study of Cross-Model Conjunctive Queries (CMCQs) over relation and tree-structured data (XML and JSON). To efficiently process CMCQs with bounded intermediate results, we first encode tree nodes with position information. With tree node original label values and encoded position values, it allows our proposed algorithm CMJoin to join relations and tree data simultaneously, avoiding massive intermediate results. CMJoin achieves worst-case optimality in terms of the total result of label values and encoded position values. Experimental results demonstrate the efficiency and scalability of the proposed techniques to answer a CMCQ in terms of running time and intermediate result size.
翻译：连接查询是数据库查询的最基本和核心类别。 随着管理和处理大量不同类型数据的需求不断增加,几乎没有研究研究关系和树木数据之间的混合查询。在本文中,我们研究了关于关系和树木结构数据(XML和JSON)的跨模式聚合查询(CMCQs),为了有效地处理具有封闭中间结果的CMCQs,我们首先用位置信息编码树节点。有了树节原始标签值和编码位置值,我们拟议的CMJoin算法能够同时结合关系和树数据,避免大规模中间结果。CMJoin在标签值和编码位置值的总结果方面实现了最差的情况最佳性。实验结果表明,在运行时间和中间结果大小方面,拟议技术在回答CMCQ方面的效率和可扩展性。
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