Accompany with fast development of location technology, more and more trajectories datasets are collected on the real applications. So it is something of value in the theory and applied research to mine the clusters from these datasets. In this paper, a trajectory clustering algorithm, called Density-Based Spatial Clustering of Application with noise (Tra-DBSCAN for short), based on DBSCAN that is a classic clustering algorithm. In this framework, each trajectory firstly partitions into sub-trajectories as clustering object, and then line hausdorff distance is used to measure the distance between two sub-trajectories. Next, DBSCAN is introduced to cluster sub-trajectory to form cluster area, and then connecting different moments of clustering area is regarded as trajectory movement patterns. Finally, the experimental results show our framework’s effective.

Since some security flaws exist in the arp protocol, some network attacks may arise such as arp overflow, arp spoofing and so on. By analyzing the modes of arp attack on campus network, the paper puts forward a defense algorithm against the arp attack. In the algorithm, the arp packets sent and receive by the host computer are to be detected ,and those differing ones are abandoned; In accordance with the policy of “receiving arp response after sending an arp request ”checks the arp response packets, those no-request ones are refused. The above defense algorithm can effectively prevent arp attacks, which can be applied in the campus network with high-safety requirements.

The Semantic Web envisions a World Wide Web in which datasources are encapsulated and described with rich semantics, and demander can issue complex queries. A critical problem in the situation is how to efficiently describe, organize and search these encapsulated datasources. This paper describes the Semantic Web-Based Data service(SWBDS for short) approach, which addresses these challenges. SWBDS introduces an ontology-based approach to, mapping web data sources to data service (DS for short), publishing DS with the shared domain ontology, and answering queries through DS provided interface. We define the domain ontology to illustrate the DS interface. The domain ontology is described in OWL that can be understood and processed by machines. Therefore, SWBDS can provide reasoning functions and facilitate datasources management with little human effort. This new system model makes full use of legacy applications and is flexible for future extensions.