DAPD journal paper published
One journal paper from the UDBMS group at the Department of Computer Science has been published in Distributed and Parallel Databases (DAPD). Please see the details as follows:

Chao Zhang and Jiaheng Lu. "Holistic evaluation in multi-model databases benchmarking." Distributed and Parallel Databases (2019): 1-33.

Abstract A multi-model database (MMDB) is designed to support multiple data models against a single, integrated back-end. Examples of data models include document, graph, relational, and key-value. As more and more platforms are developed to deal with multi-model data, it has become crucial to establish a benchmark for evaluating the performance and usability of MMDBs. In this paper, we propose UniBench, a generic multi-model benchmark for a holistic evaluation of state-of-the-art MMDBs. UniBench consists of a set of mixed data models that mimics a social commerce application, which covers data models including JSON, XML, key-value, tabular, and graph. We propose a three-phase framework to simulate the real-life distributions and develop a multi-model data generator to produce the benchmarking data. Furthermore, in order to generate a comprehensive and unbiased query set, we develop an efficient algorithm to solve a new problem called multi-model parameter curation to judiciously control the query selectivity on diverse models. Finally, the extensive experiments based on the proposed benchmark were performed on four representatives of MMDBs: ArangoDB, OrientDB, AgensGraph and Spark SQL. We provide a comprehensive analysis with respect to internal data representations, multi-model query and transaction processing, and performance results for distributed execution.

Online open access version: https://link.springer.com/article/10.1007/s10619-019-07279-6

UniBench project website: https://www.helsinki.fi/en/researchgroups/unified-database-management-systems-udbms/unibench-towards-benchmarking-multi-model-dbms

UniBench benchmark at Github: https://github.com/HY-UDBMS/UniBench