Data Mapping and Querying in NoSQL Data Warehouses

EasyChair Preprint no. 10735

Abstract

In the era of big data, traditional databases struggle to handle the volume variety and velocity of data. NoSQL systems present a promising alternative providing faster data access enhanced scalability, and increased flexibility.
This research paper introduces a practical approach that involves a set of mapping rules to adapt conceptual multidimensional schemes to NoSQL document-oriented representations enabling efficient online analytical processing (OLAP) analytics.
 Our approach focuses on two models the embedded document model which allows for storing data directly within the document for easy context-based viewing, and the reference document model which offers flexibility by storing data separately and utilizing references as needed.
Consequently, this work presents two notable contributions, one for each model employing embedded document and reference documents. To evaluate the efficiency and effectiveness of both models. A performance comparison was conducted employing query execution time as a key metric for OLAP analytics. The insights gained from this evaluation shed light on the performance and suitability of each model in different kind of queries.

Keyphrases: Big Data, Data Warehouse, Document-oriented, multidimensional database, NoSQL, Snowflake Schema

@Booklet{EasyChair:10735,
  author = {Malika Taouai and Faiza Ghozzi},
  title = {Data Mapping and Querying in NoSQL Data Warehouses},
  howpublished = {EasyChair Preprint no. 10735},

  year = {EasyChair, 2023}}