• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.485-494
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• 2008

Huge amounts of various web items such as keywords, images, and web pages are being made widely available on the Web. The popularities of such web items continuously change over time, and mining temporal patterns in popularities of web items is an important problem that is useful for several web applications. For example, the temporal patterns in popularities of search keywords help web search enterprises predict future popular keywords, enabling them to make price decisions when marketing search keywords to advertisers. However, presence of millions of web items makes it difficult to scale up previous techniques for this problem. This paper proposes an efficient method for mining temporal patterns in popularities of web items. We treat the popularities of web items as time-series, and propose gapmeasure to quantify the similarity between the popularities of two web items. To reduce the computation overhead for this measure, an efficient method using the Fast Fourier Transform (FFT) is presented. We assume that the popularities of web items are not necessarily following any probabilistic distribution or periodic. For finding clusters of web items with similar popularity trends, we propose to use a density-based clustering algorithm based on the gap measure. Our experiments using the popularity trends of search keywords obtained from the Google Trends web site illustrate the scalability and usefulness of the proposed approach in real-world applications.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.7 s.349
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• pp.1-13
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• 2006

In this paper, we introduce a novel key management scheme that is based on the key pre-distribution but provides the key re-distribution method, in order to manage keys for message encryption and authentication of lower-layer sensor nodes on hierarchical mobile sensor networks. The characteristics of our key management are as follows: First, the role of key management is distributed to aggregator nodes as well as a sink node, to overcome the weakness of centralized management. Second, a sink node generates keys using regression model, thus it stores only the information for calculating the keys using the key information received from nodes, but does not store the relationship between a node and a key, and the keys themselves. As the disadvantage of existing key pre-distributions, they do not support the key re-distribution after the deployment of nodes, and it is hard to extend the key information in the case that sensor nodes in the network enlarge. Thirdly, our mechanism provides the resilience to node capture(${\lambda}$-security), also provided by the existing key pre-distributions, and fourth offers the key freshness through key re-distribution, key distribution to mobile nodes, and scalability to make up for the weak points in the existing key pre-distributions. Fifth, our mechanism does not fix the relationship between a node and a key, thus supports the anonymity and untraceability of mobile nodes. Lastly, we compare ours with existing mechanisms, and verify our performance through the overhead analysis of communication, computation, and memory.