In this paper, we propose new methods for generating playlists with a single graph, which represents multiple types of relations in a playlist. Although current users are familiar with online music services, they have difficulty in deciding which tracks to listen to because there are millions of tracks available on such services. Automated playlist generation is one of the best solutions to solving this costly task of finding interesting tracks from the enormous tracks. Accordingly, one playlist-generation task, namely, hit rate, in which several tracks are given as a user query, is focused on in this study. There are four types of context objects (playlists, tracks, artists, and users) in the basic information on playlists, and three types of relations (playlists contain tracks and artists, users create playlists and artists play and/or sing tracks) in playlists. First, different types of relations in playlists are combined, and a single graph linking different context objects is generated. Next, a random walk is applied to the graph, and the expected values of track nodes are calculated on the basis of the transition probabilities of nodes in the graph. Finally, tracks are recommended in order of the expected values. The results of an experimental evaluation of the proposed methods in comparison with conventional methods revealed that one of the proposed methods (RW-hybrid) improved effectiveness by up to 21%. Moreover, this method reduces execution time as much as the fastest existing methods.

In this study, we propose a readability measure for Web documents and an information retrieval system that considers readability. Previous information retrieval systems aim to identify documents that are relevant to a given query; however, as information requirements of search system users becomes increasingly diverse and complicated, systems that take such new criteria into account are constantly being introduced. In particular, the focus of our present paper is on readability. Given that the population of non-native English speakers exceeds that of native English speakers, incorporating readability into an information retrieval system is crucial. Therefore, we propose (1) a readability measure that considers document simplicity and document structure as new features for readability and (2) a score fusion method that combines relevance and readability scores. In our experimental results, we found that our proposed readability measure outperformed an existing readability measure. Moreover, we found score fusion methods using a statistical framework called a copula improved overall accuracy as compared to such existing methods as linear combination.

A method for fast and incremental indexing, with both effective and efficient query processing, is proposed for XML element retrieval. When frequent document updates occur on the Web, they must be handled to maintain the effectiveness of the search system. When new topics are added and document statistics change drastically, search accuracy is also reduced. We therefore consider a method not only for updating indices efficiently but also for processing queries effectively and efficiently. We construct indices for fast updating and propose a method for computing accurate term weights even under dynamically changing statistics. Experimental results show that our proposed system can handle document updates at low cost and search documents accurately even when their statistics change.

In this paper, we propose a non-blocking buffer management scheme based on a lock-free variant of the GCLOCK page replacement algorithm. Concurrent access to the buffer management module is a major factor that prevents database scalability to processors. Therefore, we propose a non-blocking scheme for bufferfix operations that fix buffer frames for requested pages without locks by combining Nb-GCLOCK and a non-blocking hash table. Our experimental results revealed that our scheme can obtain nearly linear scalability to processors up to 64 processors, although the existing locking-based schemes do not scale beyond 16 processors.

We propose and evaluate a method for obtaining more accurate search results in extensible markup language (XML) fragment search, which is a search that produces only relevant fragments or portions of an XML document. The existing approaches generate a ranked list in descending order of each XML fragment's relevance to a search query; however, these approaches often extract irrelevant XML fragments and overlook more relevant fragments. To address these problems, our approach extracts relevant XML fragments by considering the size of the fragments and the relationships between the fragments. Next, we score the XML fragments to generate a refined ranked list. For scoring, we rank the XML fragments that are informative for user information needs as high in the list. In particular, each XML fragment is scored using the statistics of its descendant and ancestor XML fragments.

The purpose of this study is to propose Context-Aware Support Vector Machine (C-SVM) for application in a context-dependent recommendation system. It is important to consider users’ contexts in information recommendation as users’ preference change with context. However, currently there are few methods which take into account users’ contexts (e.g. time, place, the situation and so on). Thus, we extend the functionality of a Support Vector Machines (SVM), a popular classifier method used between two classes, by adding axes of context to the feature space in order to consider the users’ context. We then applied the Context-Aware SVM (C-SVM) and the Collaborative Filtering System with Context-Aware SVM (C-SVM-CF) to a recommendation system for restaurants and then examined the effectiveness of each approach.

Summary form only given. Starting from the history of document media, especially from the point of the relationship between document media and human bodies, document media can be seen as extension of body. We see how document media have been evolved with human interaction and how human bodies have been extended by document media. But recently, we have lost those historical obtained senses in electronic documents. Some thoughts for recovering those senses are presented. Next, an example of document media in workplace is described. We see how workplace affects the interaction between document media and human being as services. Finally, the future of document media and its services are briefly forecasted.

We propose a parallel directory structure, Fat-Btree, to improve high speed access for parallel database systems in shared nothing environments. The Fat-Btree has a threefold aim: to provide an indexing mechanism for fast retrieval in each processor; to balance the amount of data among distributed disks, and to reduce synchronization costs between processors during update operations. We use a probability based model to compare the throughput and response time of the Fat-Btree with two ordinary parallel Btree structures, with copies of a whole Btree in each processor and storing index nodes in a processor. The comparison results indicate that the Fat-Btree is suitable for actual parallel database systems that accept update operations.