Online Social Network (OSN) is one of the most hottest services in the past years. It preserves the life of users and provides great potential for journalists, sociologists and business analysts. Crawling data from social network is a basic step for social network information analysis and processing. As the net- work becomes huge and information on the network updates faster than web pages, crawling is more dicult because of the limitations of band-width, po- liteness etiquette and computation power. To extract fresh information from social network eciently and eectively, this paper presents a novel crawling method and discusses parallelization architecture of social network. To dis- cover the feature of social network, we gather data from real social network, analyze them and build a model to describe the discipline of users' behavior. With the modeled behavior, we propose methods to predict users' behavior. According to the prediction, we schedule our crawler more reasonably and ex- tract more fresh information with parallelization technologies. Experimental results demonstrate that our strategies could obtain information from OSN eciently and eectively.
Mining and analyzing data from social networks can be difficult because of the large amounts of data involved. Such activities are usually very expensive, as they require a lot of computational resources. With the recent success of cloud computing, data analysis is going to be more accessible due to easier access to less expensive computational resources. In this work we propose to use cloud computing services as a possible solution for analysis of large amounts of data. As a source for a large data set, we propose to use Twitter, yielding a graph with 50 million nodes and 1.8 billion edges. In this paper, we use computation of PageRank on Twitter’s social graph to investigate whether or not cloud computing, and Amazon cloud services1 in particular, can make these tasks more feasible and, as a side effect, whether or not PageRank provides a good ranking of Twitter users. This paper describes the functions of a system proposed for the music tube recommendation from social network data base. Such a system enables the automatic collection, evaluation and rating of music critics, the possibility to rate music tube by auditors and the recommendation of tubes depended from auditor's profiles in form of regional internet radio. First, the system searches and retrieves probable music reviews from the Internet. Subsequently, the system carries out an evaluation and rating of those reviews. From this list of music tubes the system directly allows notation from our application. Finally the system automatically create the record list diffused each day depended form the region, the year season, day hours and age of listeners. Our system uses linguistics and statistic methods for classifying music opinions and data mining techniques for recommendation part needed for recorded list creation. The principal task is the creation of popular intelligent radio adaptive on auditor's age and region - IA-Regional-Radio.