SINGLE vs. MapReduce vs. Relational: Predicting Query Execution Time

  • Maryam AbbasiEmail author
  • Pedro MartinsEmail author
  • José CecílioEmail author
  • João CostaEmail author
  • Pedro FurtadoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)


Over the past decade’s several new concepts emerged to organize and query data over large Data Warehouse (DW) system with the same primary objective, that is, optimize processing speed. More recently, with the rise of BigData concept, storage cost lowered significantly, and performance (random accesses) increased, particularly with modern SSD disks. This paper introduces and tested a storage alternative which goes against current data normalization premises, where storage space is no longer a concern. By de-normalizing the entire data schema (transparent to the user) it is proposed a new concept system where query execution time must be entirely predictable, independently of its complexity, called, SINGLE. The proposed data model also allows easy partitioning and distributed processing to enable execution parallelism, boosting performance, as happens in MapReduce. TPC-H benchmark is used to evaluate storage space and query performance. Results show predictable performance when comparing with approaches based on a normalized relational schema, and MapReduce oriented.


Predictable Query execution Data warehouse MapReduce Normalization De-normalization Distributed Relational 



This work is financed by national funds through FCT - Fundação para a Ciência e Tecnologia, I.P., under the project UID/Multi/04016/2016. Furthermore, we would like to thank the Instituto Politécnico de Viseu and CI&DETS for their support.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of CoimbraCoimbraPortugal
  2. 2.Polytechnic Institute of ViseuViseuPortugal
  3. 3.Polytechnic Institute of CoimbraCoimbraPortugal

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