Fusion OLAP : Fusing the Pros of MOLAP and ROLAP Together for In-memory OLAP

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Zhang , Y , Zhang , Y , Wang , S & Lu , J 2019 , ' Fusion OLAP : Fusing the Pros of MOLAP and ROLAP Together for In-memory OLAP ' , IEEE Transactions on Knowledge and Data Engineering , vol. 31 , no. 9 , pp. 1722-1735 . https://doi.org/10.1109/TKDE.2018.2867522

Title: Fusion OLAP : Fusing the Pros of MOLAP and ROLAP Together for In-memory OLAP
Author: Zhang, Yansong; Zhang, Yu; Wang, Shan; Lu, Jiaheng
Contributor organization: Unified DataBase Management System research group / Jiaheng Lu
Department of Computer Science
Date: 2019-09
Language: eng
Number of pages: 14
Belongs to series: IEEE Transactions on Knowledge and Data Engineering
ISSN: 1041-4347
DOI: https://doi.org/10.1109/TKDE.2018.2867522
URI: http://hdl.handle.net/10138/305996
Abstract: OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multidimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. This is achieved by mapping the relation tables into virtual multidimensional model and binding the multidimensional operations into a set of vector indexes to enable multidimensional computing on relation tables. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional surrogate key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35, 365, and 169 percent performance improvements based on the Hyper, Vectorwise, and MonetDB databases, respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
Subject: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Peer reviewed: Yes
Rights: cc_by_nc_sa
Usage restriction: openAccess
Self-archived version: acceptedVersion


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