Takao Miura

Topic Detection and Tracking for News Web Pages

This paper proposes a new approach to observe, summarize and track events from a collection of news Web pages. Given a set of temporal Web pages, we obtain valid times-tamp from Web pages and detect events by means of clustering. Then we track events by using KeyGraph based on the clusters and abstract the clusters by using SuffixTree. We examine some experimental results and show the usefulness of our approach

Mining temporal classes from time series data

In this investigation, we discuss how to mine Temporal Class Schemes to model a collection of time series data. From the viewpoint of temporal data mining, this problem can be seen as discretizing time series data or aggregating them. Also this can be considered as screening (or noise filtering). From the viewpoint of temporal databases, the issue is how we represent the data and how we can obtain intensional aspects as temporal schemes. In other words, we discuss scheme discovery for temporal data. Given a collection of temporal objects along with time axis (called log), we examine the data and we introduce a notion of temporal frequent classes to describe them. As the main results of this investigation, we can show that there exists one and only one interval decomposition and the temporal classes related to them. Also we give experimental results that prove the feasibility to time series data.

Mining Type Schemes in Databases

We propose a heuristic method to mine type scheme semiautomatically from initial database scheme and the instances. Unlike conventional database design methods, the proposed one starts from examining database entities.

Knowledge acquisition for classification systems

We propose a new method to mine a type scheme semi-automatically from an initial database scheme and the instances. Our data model assumes that one entity may have more than one type and classification (or type scheme). It might be appropriate when each entity is classified into at most k (least general) classes with respect to the ISA hierarchy, to keep database processing efficient. Our method differs from others in evolving ISA hierarchy by introducing a semantical metric. We propose a sophisticated algorithm to simplify, evolve and generate type schemes.

Improving text categorization by resolving semantic ambiguity

In this investigation, we propose a new method for text categorization (TC) based on Bayesian approach by resolving ambiguity. The TC assumes weights to words of which meanings are ambiguous in a sense of synonymy and polysemy. We give weights to articles by examining dictionaries of thesaurus and of dimensionality reduction to improve the quality of TC. Also we show some experiments to illustrate how well our approach goes.

Similarity among melodies for music information retrieval

Here we discuss how to look for similar melody in music databases by giving monophonic melody in sheet. In this work, we utilize text expression (or sheet music) to describe music and introduce pitch spectrum of melodies. By this feature, we concisely distinguish music from tempo, transposition or other arbitrary expressions. We show the usefulness by experimental results.

Meta model approach for mediation

In this work, we discuss how to interpret traditional Data Flow Diagram (DFD) by Unified Modeling Language (UML) for the purpose of integrating legacy systems with modern systems. To do that we introduce DFD meta model by using UML class diagrams and Object Constraint Language (OCL). We utilize the meta model to capture DFD semantics and establish close correspondence between DFD expressions and the expressions that can be derived by instantiating the meta framework. At the same time we describe procedures to generate UML descriptions from DFD based on the correspondence.

Giving temporal order to news corpus

We propose a new mechanism to give temporal order to a news article in a form of times-tamps. Here we learn temporal data in advance to extract ordering by means of incremental clustering and then we estimate most likely order to news text. In this work, we examine TDT2 corpus and we show how well our approach works by some experiments.

Reordering B-tree files

In this investigation, we address a reordering technique that improves sequential processing to B-tree files dramatically. We show conventional reorganization is not fully helpful, and propose reordering of blocks reflecting logical order. First we obtain all the data in a logical order, and then we put them into pre-order. Here we show some experimental results that says how this technique works well.

On domain independence of author identification

Latent Dirichlet Allocation (LDA) is a probabilistic framework by which we may assume each word carries probability distribution to each topic and a topic carries a distribution to each document. By putting all the documents together into one collection by each author, it is possible to identify authors. Here we show that author identification is fully reliable within a framework of LDA independent of documents domains by learning incomplete and massive documents.