In Codd's relational data model, several dependencies have been introduced to specify the intensional properties of a relation. Fagin and independently, Zaniolo introduced the notion of a multivalued dependency (MVD). The definition of MVD's refers to an underlying set of attributes of a relation. The embedded multivalued dependency (EMVD), which is also introduced by Fagin, is an MVD that holds for a projection of an original relation on the subset of attributes of the relation. The properties of EMVD's are not well known although EMVD's play an important role in designing relation schemata by Fagin's decomposition approach. Our study in this paper is motivated from the following problems: (a) Since the validity of an MVD depends on an underlying set of attributes, it is not so easy to specify valid" MVD's for a relation with many attributes. (b) There has not been a useful tool to analyze whether or not a set of relation schemata obtained by Fagin's decomposition approach can represent the same data and the same dependencies of an initial relation schema. Our standpoint is to handle these problems by studying the properties of EMVD's. The main results of this paper are the following: (1) A basic theorem about the interaction between MVD's and EMVD's is provided. Several useful inference rules for MVD's and EMVD's are derived from this theorem. (2) A marked Hasse diagram called a dependency diagram is introduced to investigate the interections between MVD's and EMVD's. (3) We provide some conditions for an MVD or a set of MVD's to be invariant under the addition or deletion of attributes. (4) Some useful equivalence relationships between two dependency sets including MVD's and EMVD's are provided. We also provide some conditions to represent a given dependency set in a reduced form.

This paper focuses on the problems how to organize and retrieve video data in an effective manner. First we identify several issues to be solved for the problems. Next, we overview our current research results together with a brief survey in the research area of video databases. We especially describe the following research results obtained by the the Japanese Ministry of Education under Grant-in-Aid for Scientific Research on Priority Area: "Advanced Databases" concerned with organization and retrieval of video data: Instance-Based Video Annotation Models, Self-Organization of Video Data, and A Query Model for Fragmentally Indexed Video.

Due to the increased preservation efforts, large amounts of past Web data have been stored in Web archives and other archival repositories. Utilizing this data can offer certain benefits to users, for example, it can facilitate page understanding. In this paper, we propose a system for interactive exploration of page histories. We demonstrate an application called Page History Explorer (PHE) for summarizing and visualizing histories of Web pages. PHE portrays the overview of page evolution, characterizes its typical content over time and lets users observe page histories from different viewpoints. In addition, it enables flexible comparison of histories of different pages.

In order to make the hyperling generation mechanism not only dynamic but also systematic, we will mainly describe the use of the querying and abstraction facilities in hypertext database systems. Our work focuses on a dynamic, abstract and systematic link generation mechanism. The prototype system named TextLink/Gem is implemented by taking an object-oriented approach, and the notions of anchor objects, query-pair links, polymorphic links and inheritable links, on which the system is based, and their implementations are described in detail.

The use of photo summarization technology to summarize a photo collection is often oriented to users who own the photo collection. However, people's interest in sharing photos with others highlights the importance of cognition-aware summarization of photos by which viewers can easily recognize the exact event those photos represent. In this research, we address the problem of cognition-aware summarization of photos representing events, and propose to solve this problem and to improve the perceptual quality of a photo set by proactively preventing misrecognization that a photo set might bring. Three types of neighbor events that can possibly cause misrecognizations are discussed in this paper, namely sub-events, super-events and sibling-events. We analyze the reasons for these misrecognitions and then propose three criteria to prevent from them. A combination of the criteria is used to generate summarization of photos that can represent an event with several photos. Our approach was empirically demonstrated with photos from Flickr by utilizing their visual features and related tags. The results indicated the effectiveness of our proposed methods in comparison with a baseline method.

This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.

This paper proposes methods of finding a ranked list of entities for a given query (e.g. “Kennin-ji”, “Tenryu-ji”, or “Kinkaku-ji” for the query “ancient zen buddhist temples in kyoto”) by leveraging different types of modifiers in the query through identifying corresponding properties (e.g. established date and location for the modifiers “ancient” and “kyoto”, respectively). While most major search engines provide the entity search functionality that returns a list of entities based on users' queries, entities are neither presented for a wide variety of search queries, nor in the order that users expect. To enhance the effectiveness of entity search, we propose two entity ranking methods. Our first proposed method is a Web-based entity ranking that directly finds relevant entities from Web search results returned in response to the query as a whole, and propagates the estimated relevance to the other entities. The second proposed method is a property-based entity ranking that ranks entities based on properties corresponding to modifiers in the query. To this end, we propose a novel property identification method that identifies a set of relevant properties based on a Support Vector Machine (SVM) using our seven criteria that are effective for different types of modifiers. The experimental results showed that our proposed property identification method could predict more relevant properties than using each of the criteria separately. Moreover, we achieved the best performance for returning a ranked list of relevant entities when using the combination of the Web-based and property-based entity ranking methods.

As the amount of recorded TV content is increasing rapidly, people need active and interactive browsing methods. In this paper, we use both text information from closed captions and visual information from video frames to generate links to enable users to easily explore not only the original video content but also augmented information from the Web. This solution especially shows its superiority when the video content cannot be fully represented by closed captions. A prototype system was implemented and some experiments were carried out to prove its effectiveness and efficiency.

We address the problem of entity identification on a microblog with special attention to indirect reference cases in which entities are not referred to by their names. Most studies on identifying entities referred to them by their full/partial name or abbreviation, while there are many indirectly mentioned entities in microblogs, which are difficult to identify in short text such as microblogs. We therefore tackled indirect reference cases by developing features that are particularly important for certain types of indirect references and modeling dependency among referred entities by a Conditional Random Field (CRF) model. In addition, we model non-sequential order dependency while keeping the inference tractable by dynamically building dependency among entities. The experimental results suggest that our features were effective for indirect references, and our CRF model with adaptive dependency was robust even when there were multiple mentions in a microblog and achieved the same high performance as that with the fully connected CRF model.