The digital transformation leads to massive amounts of heterogeneous data challenging
traditional data warehouse solutions in enterprises. In order to exploit these complex data for …

Over the last decade, a range of new database solutions and technologies have emerged, in
line with the new types of applications and requirements that they facilitate. Consequently …

Abstract Context: Big Data systems are a class of software systems that ingest, store,
process and serve massive amounts of heterogeneous data, from multiple sources. Despite …

It is widely accepted today that relational systems are not appropriate to handle Big Data.
This has led to a new category of databases commonly known as NoSQL databases that …

In the last decade, several NoSQL systems have emerged as a response to the scalability
problems manifested by classical relational databases when used in Big Data contexts …

The discipline of data science is steering analysts away from traditional data warehousing
and towards a more flexible and lightweight approach to data analysis. The idea is to …

In recent years, new data characteristics led to the development of new database
management systems named NoSQL. As opposed to the mature Relational databases …

Self-tuning is a feature of autonomic databases that includes the problem of automatic
schema design. It aims at providing an optimized schema that increases the overall …

Graph databases provide better support for highly interconnected datasets than relational
databases. However, labeled property graph databases, which have become increasingly …

Volume, Variety and Velocity are the three dimensions that have definitely changed the tools
we need to store and process Big Data effectively, giving rise to NoSQL systems for faster …