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Link prediction is one of the significant research problems in social networks analysis. Most previous works in this area neglect attribute similarity of the node pair which can easily obtain from real world dataset. Traditional supervised learning methods study the link prediction problem as a binary classification problem, where features are extracted from topology of the network. In this paper, we propose a similarity index called Attribute Proximity. The set of features are similarity index we proposed and four others well-known neighbourhood based features. We then apply a supervised learning based temporal link prediction framework on DBLP dataset and examine whether attribute similarity feature can improve the performance of the link prediction. In our experiments, the AUC performance is better when attribute similarity feature is considered.

An increasing number of applications in environmental monitoring and location-based services make use of large-scale distributed sensing provided by wireless sensor networks. In such applications, a large number of sensor devices are deployed to collect useful information such as temperature readings and vehicle positions. However, these distributed sensors usually have limited computational and communication power and thus the amount of sensor queries should be reduced to conserve system resources. At the same time, data captured by such sensors is inherently imprecise due to sensor limitations. We propose an efficient probabilistic filter-based protocol for answering continuous nearest neighbor queries over uncertain sensor data. Experimental evaluation on real-world temperature sensing data and synthetic location data showed a significant reduction in the number of update messages.

The chief challenge in identifying similar individuals across multiple ontologies is the high computational cost of evaluating similarity between every pair of entities. We present an approach to querying for similar individuals across multiple ontologies that makes use of the correspondences discovered during ontology alignment in order to reduce this cost. The query algorithm is designed using the framework of fuzzy logic and extends fuzzy ontology alignment. The algorithm identifies entities that are related to the given entity directly from a single alignment link or by following multiple alignment links. We evaluate the approach using both publicly available ontologies and from an enterprise-scale dataset. Experiments show that it is possible to trade-off a small decrease in precision of the query results with a large savings in execution time.