The work presented in this paper is motivated by the acknowledgement that a complete and
updated systematic literature review (SLR) that consolidates all the research efforts for Big …

Multi-model DBMSs, which support different data models with a fully integrated backend,
have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can …

This paper describes the process of design and construction of a data warehouse (“DW”) for
an online learning platform using three prominent technologies, Microsoft SQL Server …

Abstract Multi-model DBMSs (MMDBMSs) have been recently introduced to store and
seamlessly query heterogeneous data (structured, semi-structured, graph-based, etc.) in …

The growing importance of data analytics is leading to a shift in data management strategy
at many companies, moving away from simple data storage towards adopting Online …

Nowadays, the data used for decision-making come from a wide variety of sources which
are difficult to manage using relational databases. To address this problem, many …

Hive has long been one of the industry-leading systems for Data Warehousing in Big Data
contexts, mainly organizing data into databases, tables, partitions and buckets, stored on top …

OLAP is a technology that allows analysts to perform multidimensional data analysis, and
view business data from different angles. It enables users to analyze informations from …

OLAP (Online Analytical Processing) is a tried-and-tested technology and a core concept in
Business Intelligence. With data flowing from different and countless sources, exploring data …

Geospatial Data Warehouse (GDW) is a repository of historical and geospatial data used in
the decision-making process. This kind of system manages large volumes of data and …