Management of streaming multimedia content using mobile agent technology on pure P2P-based distributed e-learning system

Abstract

Nowadays, a lot of e-Learning systems are widely deployed in educational schools. Typical e-Learning systems are implemented as client-server model. In the client-server model, the number of clients affects on the load of the server. In order to reduce the load on the server, we developed a P2P-based distributed e-Learning system. The proposed system consists of a lot of mobile agents which manage study contents and some functions such as scoring, showing questions, and correct answers. When a learner requests content, a mobile agent who has its content comes to the learner's computer, and then he/she can start the study. Here, a mobile agent has to manage multimedia data, which may be a huge size of data. Thus, a mobile agent has to migrates to the learner's node with a huge size of data. Then, the learner cannot start the study until the mobile agent finished to migrate. In order to solve this problem, we divide multimedia data into fragments and prepare mobile agents which manages each fragments. Since each mobile agents become small, a learner can start the study soon without waiting for the migration of a mobile agent which has a huge size of multimedia data. We, however, have to search a mobile agent which manages their fragments. Therefore, a mobile agent which manages nth fragments informs its location to the agent which manages (n+1)-th fragments, and vice versa. Since each agent knows the location of a mobile agent which manages next and previous fragment; and fragments are cached into learner's node, a learner can play multimedia data smoothly without finding the location of mobile agent which manages holding next fragment. Experiment results show the effectiveness of our method.

Social media marketing through time‐aware collaborative filtering

“…Shared information along the time can be differently aggregated in order to understand topics' appeal and its periodical occurring in time intervals. Due to the amount of data, this problem is shifted, from data mining communities into a problem of stream processing preferring privacy‐preserving extraction methods . In fact, large volumes of data provided by social networks and blogs can be seen as sensors about people's needs, preferences, inclinations, and so on, that have to be acquired live and filtered in order to remove noisy and fake content .…”