A large amount of data are generated every moment by connected objects creating Internet of Things (IoT). These data are difficult to handle using traditional databases leading to use NoSQL databases. These latter have achieved a large popularity thanks to their high performance, flexibility in scaling, and high availability. But, which NoSQL database is the most suitable for IoT applications? In this paper, we discuss the main requirements of IoT data management, and we compare five of the most popular NoSQL databases namely, Redis, Cassandra, MongoDb, Couchbase and Neo4j, in accordance with IoT data management requirements, in order to find the most suitable NoSQL database for IoT applications.

Systems composed of many components which interact with each other and lead to unpredictable global behaviour, are considered as complex systems. In a biological context, complex systems represent living systems composed of a large number of interacting elements. In order to study these systems, a precise mathematical modelling was typically used in this context. However, this modelling has limitations in the structural understanding and the behavioural study. In this sense, formal computational modelling is an approach that allows to model and to simulate dynamical properties of these particular systems. In this paper, we use Hybrid Functional Petri Net (HFPN), a Petri net extension dedicated to study and verify biopathways, to model and study the Methionine metabolic pathway. Methionine and its derivatives play significant roles in human bodies. We propose a set of simulations for the purpose of studying and analysing the Methionine pathway’s behaviour. Our simulation results have shown that several important abnormalities in this pathway are related to sever diseases such as Alzheimer’s disease, cardiovascular disease, cancers and others.

In the past few years, Tweets have been widely used to perform Big Data analysis. However, the incredible amount of data captured by Twitter needs to be stored for further processing which may be a challenging task for many database systems. NoSQL is a generation of databases that aim to handle a large volume of data. However there is a large set of NoSQL systems, each has its own characteristics. Consequently choosing the suitable NoSQL system to handle Tweets is challenging. Based on these motivations, this work is carried out to find the suitable NoSQL system to manage Tweets. This paper presents the requirements of managing Tweets and provides a detailed comparison of five NoSQL systems namely, Redis, Cassandra, MongoDB, Couchbase and Neo4j regarding these requirements. The five NoSQL systems are compared in a real scenario where we collect and analyze 1.000.000 Tweets. The chosen scenario enables to evaluate not only the performance of the read and write operations, but also other requirements related to Tweets management such as scalability, analysis tools support and analysis languages support. The obtained results show that Couchbase is the most suitable NoSQL systems for managing Tweets. GRAPHICAL ABSTRACT

NoSQL systems are a new generation of databases that aim to handle a large volume of data. However there is a large set of NoSQL systems, each has its own characteristics. Consequently choosing the suitable NoSQL system to handle Tweets is challenging. Based on these motivations, this work is carried out to find the suitable NoSQL system to store, preprocess and analyze Tweets. This paper presents the requirements of managing Tweets and provides a detailed comparison of five of the most popular NoSQL systems namely, Redis, Cassandra, MongoDB, Couchbase and Neo4j regarding to these requirements. The results of this work show that for Tweets storing, preprocessing and analyzing, MongoDB and Couchbase are the most suitable NoSQL systems. Unlike related works, this work compares five NoSQL systems from different types in a real scenario which is Tweet storing, preprocessing and analyzing. The chosen scenario enables to evaluate not only the performance of read and write operations, but also other requirements related to Tweets management such as scalability, analysis tools support and analysis languages support.