In order to study the informationised teaching model of track and field education, this paper uses the finite element higher-order fractional differential equation to simulate, to provide high-quality, strong resistance to pressure, physical and mental health talents for the development of the country, through the study of the higher-order fraction of the finite element and track and field course intended to strengthen the application in the innovation of track and field course. The results show that based on the finite element theory of higher-order fractional differential equation, a new teaching model is constructed to solve the current difficulties faced by track and field. Starting from the teaching end, pay attention to the quality of output so as to achieve the purpose of training qualified personnel. Conclusion: In the course of track and field classroom practice based on finite element high-order fractional differential equation, students’ enthusiasm is mobilised, and they take the initiative to learn knowledge and master skills.

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

Because of the huge number of users, Facebook becomes the largest social network on earth. Although, Facebook is continuously developing its features but doesn't provide yet a suitable mechanism to protect the content copyright in an automatic way. This paper suggests a framework proposal to protect the copyright of photos in Facebook. This will be done by adding a watermark to photo that will be uploaded. The watermark contains the name of the profile that uploaded photo and date of posting it. This profile will be considered as the owner of the photo on Facebook. If the uploaded photo already contains a Facebook watermark, the profile that already owned the photo will be notified by Facebook that there is another profile trying to upload the same photo. The original owner will decide to give the permission for the photo to be posted again on Facebook or not. If the photo's owner allows the photo to be posted the watermark information will be displayed in the post. The system doesn't allow the new user to put his/her water mark on the photo. In this way, even if a profile posted a watermarked photo it will be clear to everyone who is the original owner and when the profile posted it.

No abstract

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.