Multimedia Correlation Analysis in Unstructured Peer-to-Peer Network

Recent years saw the rapid development of peer-topeer (P2P) networks in a great variety of applications. However, similarity-based k-nearest-neighbor retrieval (k-NN) is still a challenging task in P2P networks due to the multiple constraints such as the dynamic topologies and the unpredictable data updates. Caching is an attractive solution that reduces network traffic and hence could remedy the technological constraints of P2P networks. However, traditional caching techniques have some major shortcomings that make them unsuitable for similarity search, such as the lack of semantic locality representation and the rigidness of exact matching on data objects. To facilitate the efficient similarity search, we propose semantic-aware caching scheme (SAC) in this paper. The proposed scheme is hierarchy-free, fully dynamic, non-flooding, and do not add much system overhead. By exploring the content distribution, SAC drastically reduces the cost of similarity-based k-NN retrieval in P2P networks. The performance of SAC is evaluated through simulation study and compared against several search schemes as advanced in the literature

〈一般講演〉能動的電子図書館システムの構築に向けて：その機能と要素技術

本稿では、能動的電子図書館システムに求められる機能と、その機能を実現するために利用可能と考えられる要素技術について概説する。一般の「利用者が求めた情報のみを提供する」受動的な電子図書館システムに対して、積極的に「利用者が必要とするであろう情報」を発見・構築して提供するシステムを能動的電子図書館システムという。本稿では、そのような電子図書館システムに必要となる機能である検索結果の仲介機構や適応的情報提供の機能などについて述べるとともに、その実現を支える技術に関する研究成果を紹介する。筑波大学・図書館情報大学統合記念公開シンポジウム 日程：2013年1月24日 会場：筑波大学大学会館国際会議

A New Method for Similarity Indexing of Market Basket Data

In recent years, many data mining methods have been proposed for finding useful and structured information from market basket data. The association rule model was recently proposed in order to discover useful patterns and dependencies in such data. This paper discusses a method for indexing market basket data efficiently for similarity search. The technique is likely to be very useful in applications which utilize the similarity in customer buying behavior in order to make peer recommendations. We propose an index called the signature table, which is very flexible in supporting a wide range of similarity functions. The construction of the index structure is independent of the similarity function, which can be specified at query time. The resulting similarity search algorithm shows excellent scalability with increasing memory availability and database size

Abstract A New Method for Similarity Indexing of Market Basket Data

In recent years, many data mining methods have been proposed for finding useful and structured information from market basket data. The association rule model was recently proposed in order to discover useful patterns and dependencies in such data. This paper discusses a method for indexing market basket data efficiently for similarity search. The technique is likely to be very useful in applications which utilize the similarity in customer buying behavior in order to make peer recommendations. We propose an index called the signature table, whichisvery flexible in supporting a wide range of similarity functions. The construction of the index structure is independent of the similarity function, which can be specified at query time. The resulting similarity search algorithm shows excellent scalability with increasing memory availability and database size.