One of the key issues for providing users user-customized or context-aware services is to automatically detect latent topics, users’ interests, and their changing patterns from large-scale social network information. Most of the current methods are devoted either to discovering static latent topics and users’ interests or to analyzing topic evolution only from intrafeatures of documents, namely, text content, without considering directly extrafeatures of documents such as authors. Moreover, they are applicable only to the case of single processor. To resolve these problems, we propose a dynamic users’ interest discovery model with distributed inference algorithm, named as Distributed Author-Topic over Time (D-AToT) model. The collapsed Gibbs sampling method following the main idea of MapReduce is also utilized for inferring model parameters. The proposed model can discover latent topics and users’ interests, and mine their changing patterns over time. Extensive experimental results on NIPS (Neural Information Processing Systems) dataset show that our D-AToT model is feasible and efficient.

Sensor Networks can perceive the extensive area by applying many sensor nodes because the size of sensor nodes is relatively small and cheap. Sensor nodes can transfer multi-hop data to sink nodes which are far away than sending and receiving distance. Generally sensor nodes are composed of a large size so it is difficult that much capacity is loaded on nodes. Many routing methods are proposed in order to raise energy efficiency in sensor networks field. There is a routing method based on structure as a composing method of network by cluster. Cluster technology consisting and maintaining network topology based on cluster is mostly studied in routing protocol. There are demerits that LEACH, EACHS shall be rest energy of all nodes and HEED does not guarantee the number of cluster head. Proposed energy efficiency of selected cluster head guarantees the number of cluster head which is a demerit of HEED and minimizes the node of DEAD.