Cross-Model Conjunctive Queries over Relation and Tree-structured Data (Extended)

TL;DR

This paper introduces a novel algorithm, CMJoin, for efficiently processing cross-model conjunctive queries over relation and tree-structured data like XML and JSON, reducing intermediate results and improving scalability.

Contribution

The paper proposes a new encoding scheme and the CMJoin algorithm for efficient, worst-case optimal cross-model conjunctive query processing over heterogeneous data structures.

Findings

CMJoin reduces intermediate result sizes significantly.

The approach achieves worst-case optimality in query result size.

Experimental results show improved running time and scalability.

Abstract

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.