A social networking template with macro data in the health environment

Today, the amount of information available to humans is so amazing and high that analysis and processing of them to find the concept we are looking for are hard or even overwhelming. In the information and communication age, the explosion of the production and exchange of structured and non-structured data from scientific, industrial, and labor resources, especially social networks, has created a new concept called the macro data. Large data of a concept with a feature Validity, oscillation, visualization, credibility and value is the goal of collecting, storing, processing information and converting a large amount of valuable data into a combination of new strategies and technologies. By analyzing more volumes of data, better and more advanced analyzes can be done for various purposes, such as business, medical and security purposes, and receive better results. The medical industry can use social calculation to identify the symptoms of disease and the communication of diseases, and then conclude. On the other hand, the social network analysis and prediction calculations in social networks are useful for dealing with changes so that if the user of the network changes over time, the use of predicted calculations to predict behavior or provide preferable decision strategies will be. A social network is defined as a set of individuals or other entities, social institutions are a set of meaningful social relationships such as friendship, collaboration, or the exchange of information and interactions to achieve desired results by sharing expertise, resources, and information, etc. This analysis can be used to develop a variety of applications such as marketing and business strategies or disease/symptom analyzes in health care. This research contributes to social computing and the disclosure of smart patterns in the social network. Computational prediction is intrinsically reactive because the decision is based on the current state of the environment, which uses multiple sensors to conclude, calculate prediction processing. Machine learning algorithms provide appropriate decision making for users. Today, the complexity of learning a car in large data can affect the health care industry. It has been observed that many authors have used car learning and analysis of large data in diagnosis that did not care about weight and privacy and data security. In this research, various techniques such as encryption and monitoring of activity, gravity access control, data encryption and validation are used to protect the confidentiality of information. Large-scale data analysis gives a lot of insight into different organizations, especially in health care.

کلیدواژه ها:

Sicial networking ، Big data ، health information technology ، Large-scale data analysis