To allow further automatic big data management, and to reduce data resource allocation problems, we propose an approach for embedded systems reconfiguration. The reconfiguration of embedded systems consists of adding new servers and clusters for exchanging the big data. For that, we propose a methodology based on an automatic programming approach to deploy new data servers based on NoSQL performances. These servers will be interlinked to get generated data from an embedded system. This will increase the number of satisfied transactions and queries with an optimal data access latency. Moreover, to evaluate the performance of our model, we propose a new definition based on committing full queries and the ability of all the servers to manage and process the generated big data with the constraint of time. To test and validate the proposed approach, a software tool named SFRBDM (Software for Real-time Big Data Management) has been developed. This software tool allows users to simulate the functioning of embedded systems, inject, generate data, and ensure recoveries between data servers and clusters. SFRBDM also allows calculating a better resource allocation of the simulated embedded databases with a higher performance based on NoSQL. The experimental study performed over multiple servers, with different structures and sizes, validates the efficiency of our proposed approach.

In today’s fast-changing business environment, organizations need to be adaptable and responsive to change. Effective decision-making enables companies to cope with uncertainty and adjust their strategies and operations promptly. In this context, the emergence of intelligent decision support systems (DSS) shows great promise. These systems enable companies to analyze data, identify patterns, and offer valuable insights to facilitate informed decision-making. An intelligent DSS for updating control plans is presented in this paper. The proposed system incorporates two methods to assist the decision-maker in choosing the optimal control scenario. The first involves making a manual decision based on several criteria. The second uses the case-based reasoning (CBR) technique to make an automatic recommendation. Moreover, the suggested recommender system (RS) enables the control plans to be updated regularly depending on the current state of the process quality. To increase the quality of decisions, CBR is used to acquire the necessary knowledge. The viability and applicability of the suggested RS are demonstrated through a practical case study.

Today NoSQL databases have become unavoidable that more and more companies are turning to these solutions. The variety of non-relational databases resides in their data storage structure to face the big amount of data and their modeling as well. Hence, our paper presents a unified schema modeling for NoSQL solutions based on a mapping approach. Indeed, NoSQL refers to a family of database management systems (DBMS) that departs from the traditional paradigm of relational databases. These systems remain efficient in a continuous scalability case. Among the NoSQL solutions our paper deals with Columns, Document and Graph oriented databases to test the feasibility of our proposed approach.

In nowadays, robotics database management systems are increasing. These systems ensure good storage of data and with big data analytic, a new approach demands new structures and methods for collecting, recording, and analyzing enterprise data. This paper work deals with the NoSQL databases which are the secret of the continual progression data that new data management solutions have been emerged. They crossed several areas as personalization, profile management, big data in real-time, content management, catalogue, view of customers, mobile applications, internet of things, digital communication and fraud detection. Machine learning, for example, thrives on more data, so smart machines can learn more and faster, the Robotics are our use of case to focus on our Test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of Robotics we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.