@article{M6D5C6A5B,
title = "Dual Storage Engines toward Multi-Model Analytic Workloads",
journal = "Journal of KIISE, JOK",
year = "2026",
issn = "2383-630X",
doi = "10.5626/JOK.2026.53.2.158",
author = "Kyoseung Koo, Yoojin Choi, Bogyeong Kim, Bongki Moon",
keywords = "multi-model, analytic workloads, storage engine, join algorithm, database system",
abstract = "Modern data analytic workloads increasingly require handling multiple data models
simultaneously. Two primary approaches meet this need: polyglot persistence and multi-model
database systems. However, they are limited by high communication costs due to the physical
disaggregation of the system or inefficient query processing stemming from reliance on a single
engine. To address these limitations, we present DSE, a multi-model analytic system with integrated
storage engines optimized for each model. DSE treats all data models as first-class entities, composing
query plans that incorporate operations across models. To effectively combine data from different
models, the system introduces a specialized inter-model join algorithm called multi-stage hash join.
Our evaluation demonstrates that DSE outperforms existing approaches on multi-model analytics,
confirming the effectiveness of our proposed techniques."
}