## 2 Background and Related Work  
With the advent of Future Internet applications, there is an increasing need to pass from SQL-Like to NoSQL DBMS(s) able to efficiently manage a huge volume of heterogeneous data in different domains including smart cities [3], industry 4.0 [4], marketing [5], robotics [6], transportation [7], healthcare [8; 9], genomics [10; 11], and so on. Therefore, many scientific and technical works have been proposed in literature so far focusing on a comparative analysis between SQL-like and NoSQL database solutions. In [12], a comparison is proposed between both types of databases (SQL and NoSQL) to test the various CRUD operations i.e., Create, Read, Update and Delete varying the amount of used data. The authors therefore thanks to their analysis give an evaluation index in order to select which database to use for a given data set. All of the above works give their own evaluation about the performance of each DBMS. They have been tested all basic operations (CRUD) but none of these works made a comparative analysis between the Join (SQL) and Join (NoSQL) operations, because everyone agrees that this operation does not exist in the non-relational databases. In [13], in order to run comparative tests between different kind of databases, a bank application that uses different types of databases is designed and implemented. The experimental results measure the timing of the basic operations carried out on each of them demonstrating that the NoSQL databases with SQL features such _Foundationdb_ turn out to be better than SQL. In [14] the authors made a conversational careful analysis on NoSQL databases, comparing the differences between the different types of NoSQL databases, highlighting in the end their advantages and disadvantages. Authors in [8; 15] present the Open Archival Information System (OAIS), which exploits the well-known NoSQL column-oriented Database (DB) Cassandra. They performed some tests on the proposed solutions in a real use case scenario, comparing performance with a traditional MySQL solution. They noticed that in a not distributed infrastructure Cassandra in not very performing compared to MySQL, because benefits of column family management can not be exhibited. However, advantages in using Cassandra are evident when Cassandra is Coupled with Apache Spark. In the healthcare application domain, ref. [16] presents a protocol to assess the computational complexity of querying relational and non-relational standardized electronic health record (EHR). This protocol shows that SQL systems are not practical for single-patient queries since response times are slower. NoSQL databases show a linear slope, and MongoDB performs considerably faster than eXist DBMS. In concurrency, MongoDB also behaves much better than relational MySQL. The [17] presents a comparative analysis between NoSQL databases such as _HBase_, _MongoDB_, _BigTable_, _SimpleDB_ and relational databases such as _MySQL_ specifying their limits when applied to real world. The authors specifically tested the above databases analysing both simple and more complex queries. A set-similarity join solution in NoSQL using MapReduce is discussed in [18].The set-similarity join algorithm can avoid redundant comparisons between join attribute values in the MapReduce framework. In particular, the number of comparisons to find all similar pairs was reduced by extending the prefix filtering technique for the MapReduce Framework. This solution resulted up to an order of magnitude improvement in performance over the most efficient existing solutions. In [19], an index-based solution that adapts a centralized rank-join algorithm is discussed. In particular, authors provide (i) MapReduce algorithms showing how to build indices and statistical structures, (ii) algorithms to allow for online updates to these indices, and (iii) query processing algorithms utilizing them. All algorithms are implemented and tested in Hadoop (HDFS) and HBase, utilizing different queries on tables of various sizes and different score-attribute distributions.