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Top1. Introduction
The time we are facing could be considered as the era of big data. The data are everywhere around us. The penetration of the technology in our everyday life has given rise to the creation of billions of data web journals, mobile devices, sensors, instruments and transactions. The term “Big Data” is used as one of the newest technologies in the IT industry, to describe the collection and analysis of very large amounts of data and to draw correlations and conclusions. A typical example is the event which took place, when Instagram launched at their public version for the Android application and in less than 24 hours a million users has installed the application. The architecture used behind this point of view was the fragmentation of data between the various nodes forming a distributed database. Also, an architecture that is used by the large internet companies such as Facebook and Twitter and many others who collect data of millions of users and their needs for management to lead in the creation and the development of new systems. To this effect, the latter could not be used effectively with the systems of traditional databases. No-doubt that people store and share their files, including large files such as audio and video, on the internet. There has been a lot of work improving specific file sharing systems but improving the performance of file sharing on a distributed storage system still remains a challenge.
Intelligent manufacturing is a new trend that is a deep integration of information and industrialization. As to the information, database is very important. For the past 20 years, relational databases have been widely used in a lot of fields because of their rich feature, such as query capabilities and transaction management and so on. However, they do not have the capacity of storing and processing a large amount of data effectively, and at the same time, they are not very efficient to make transactions and join operations. In order to adapt to the new demand, some of new databases have been invented which are not in accordance with relational model. These databases are known as NoSQL. The underlying data and transaction model of the NoSQL are different from relational database.
Some of organizations have shown much interest in NoSQL and adopted this new technology, which promote further research on the NoSQL. This work is in limited areas of the new technologies for Big data management. The broader storage environment has become heterogeneous, multi-tiered, and aligned with cloud applications and infrastructure technologies. The Robots are the big elected systems for the use of these technologies. They are virtual or physical tools for repeating and automating jobs. But knowing when and where to use a specific job is often rules based on storing those rules in some sort of database. This will solve a lot of problems and design patterns for producing an AI robotics project. Big Data is a cluster of many technologies and tools that are used in various setups and fulfilling any of skills sets and types. The NoSQL embedded systems that will be integrated on the robot’s environment provides instant access to electronic health records (as a health field), critical applications and clinical workstations.
There were non-continuous tests in this axis of research which remains a vital nerve for many researchers among others. In which, they focused on children’s imaginaries of human-robot interaction in healthcare. However, the use of social robots has opened up new controversies (or has revived old ones, though in new forms). The evocative potential of robots, which make us reflect about our own humanity, has led to the emergence of new social and ethical debates concerning their adequacy or their desirability, especially when they are used with children and other vulnerable collectives.
This flexibility has a not insignificant impact on the productivity and responsiveness of the medical profession. Thanks to an optimized management of patient profiles, the tracing of the care and the experience of the patients are greatly improved. Medical teams can access individualized profiles where data has been shared and synchronized between teams. Real-time access to workstations, electronic health records and key communication tools maximizes the time and quality of patient experience. For the authors knowledge, the first proposed work is an approach which allows a performance gain in memory and time.
Big data as the main problem under the birth of NoSQL solutions, and all its technologies are becoming an industry standard in finance, commerce, insurance, healthcare and distribution. Companies that embrace data solutions establish a self-service business-driven paradigm while improving management and operational processes, creating a competitive advantage that will withstand an ever-evolving marketplace.