Faculty Profiles - ISHIKAWA, Yoshiharu

Papers - ISHIKAWA, Yoshiharu

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Novelty-based Incremental Document Clustering for On-line Documents Reviewed

Sophoin Khy, Yoshiharu Ishikawa, and Hiroyuki Kitagawa

Proceedings of International Workshop on Challenges in Web Information Retrieval and Integration (WIRI 2006) page： 40 2006.4

Language：English

Document clustering has been used as a core technique in managing vast amount of data and providing needed information. In on-line environments, generally new information gains more interests than old one. Traditional clustering focuses on grouping similar documents into clusters by treating each document with equal weight. We proposed a novelty-based incremental clustering method for on-line documents that has biases on recent documents. In the clustering method, the notion of `novelty' is incorporated into a similarity function and a clustering method, a variant of the K-means method, is proposed. We examine the efficiency and behaviors of the method by experiments.

Dynamic Mobility Histogram Construction Based on Markov Chains Reviewed

Yoji Machida, Yoshiharu Ishikawa, Hiroyuki Kitagawa

Vol. 5 ( 1 ) page： 89-92 2006.6

Language：Japanese Publishing type：Research paper (scientific journal)

For the accumulation and analysis of a large collection
of moving object trajectories, our group focuses
on the research on a mobility histogram to summarize
moving object trajectories. The histogram is based on
a mobility statistics model called the Markov chain
model. We provide a mobility histogram datacubelike
logical representation and support an OLAP-style
analysis. As its physical structure, we introduce a tree
structure that efficiently works in a limited memory
space. We describe the details of the method and evaluate
its performance based on experiments.

Incremental Clustering Based on Novelty of On-line Documents Reviewed

Sophoin Khy, Yoshiharu Ishikawa, and Hiroyuki Kitagawa

DBSJ Letters Vol. 5 ( 1 ) page： 57-60 2006.6

Language：English Publishing type：Research paper (scientific journal)

Clustering has been widely used as a fundamental
method in many areas such as characterization
and classification. Various clustering researches have
been conducted since decades ago. In previous papers,
we presented a novelty-based incremental document
clustering method which considers novelty of
on-line documents in its similarity measure and performs
clustering based on an extended algorithm of
the K-means method. This paper further examines the
performance of the incremental and non-incremental
processing of the clustering method and effect of parameter
values on the method by showing the experimental
results.

A Dynamic Mobility Histogram Construction Method Based on Markov Chains Reviewed

Yoshiharu Ishikawa, Yoji Machida, and Hiroyuki Kitagawa

Proceedings of the 18th International Conference on Scientific and Statistical Database Management (SSDBM 2006) page： 359-368 2006.7

Authorship：Lead author Language：English

With the recent progress of spatial information technologies and communication technologies, it has become easier to track positions of a large number of moving objects in real-time. Mobility statistics plays an important role in the interactive analysis of a large collection of moving objects trajectories and its use of movement pattern prediction. The development of an effective mobility statistics measure and its efficient computation method are critical issues. Thus, we propose an approach for constructing a mobility histogram to summarize a number of moving object trajectories. The histogram is based on a mobility statistics model called the Markov chain model. To facilitate an interactive analysis performed by a user, we provide a mobility histogram data cube-like logical representation and support an OLAP-style analysis. Since trajectory data is often received continuously as a trajectory stream, we have to support dynamic histogram construction and maintenance. We introduce a tree structure as the physical representation of a histogram and present histogram construction and maintenance methods that work efficiently within the given upperbound size. We evaluate the performance and the precision of the proposed method by means of experiments.

A Dynamic Mobility Histogram Construction Method Based on Markov Chains Reviewed

Yoshiharu Ishikawa, Yoji Machida, Hiroyuki Kitagawa

IEICE Transactions on Information and Systems (Japanese Edition) Vol. J90-D ( 2 ) page： 311-324 2007.2

Authorship：Lead author Language：Japanese Publishing type：Research paper (scientific journal)

Due to the progress of GPS and communication technologies, it has become easier to track positions of moving objects. By monitoring and aggregating the movements of a large number of objects in real-time, we can analyze and estimate their behaviors effectively. For this purpose, we propose an approach to constructing a mobility histogram for the summarization of a large volume of moving object trajectories. The histogram is based on a mobility statistics model called the Markov chain model. We provide a mobility histogram data cube-like logical representation and support an OLAP-style analysis. We introduce a tree structure as its physical representation and present approximated histogram construction methods for the reduction of the storage size. Since trajectory data is often received continuously as a trajectory stream, we enable efficient histogram construction for the real-time processing. We evaluate the performance and the precision of the proposed methods based on the experiments.

T-Scroll: A Visualization System for Temporally Changing Topics Reviewed

Mikine Hasegawa, Yoshiharu Ishikawa

DBSJ Letters Vol. 6 ( 1 ) page： 149-152 2007.6

Language：Japanese Publishing type：Research paper (scientific journal)

On the Internet, delivery of a large amount of documents such as news articles is continually performed everyday. In this paper, we describe an information visualization system T-Scroll to show the transition of topics contaned in such documents to the user and to provide an overview of their trends. The system is built on a clustering system for time-sereis of documents and presents relationships between clusters like a scroll. This paper describes the idea, the functions, and the implementation of the system.

Record Extraction Based on User Feedback and Document Selection Reviewed

Jianwei Zhang, Yoshiharu Ishikawa, Hiroyuki Kitagawa

Proceedings of the Joint Conference of the 9th Asia-Pacific Web Conference and the 8th International Conference on Web-Age Information Management (APWeb/WAIM07) page： 574-585 2007.6

Language：English

In recent years, the research of record extraction from large document data is becoming popular. However there still exist some problems in record extraction. 1) when large document data is used for the target of information extraction, the process usually becomes very expensive. 2) it is also likely that extracted records may not pertain to the user's interest on the aspect of the topic. To address these problems, in this paper we propose a method to efficiently extract those records whose topics agree with the user's interest. To improve the efficiency of the information extraction system, our method identifies documents from which useful records are probably extracted. We make use of user feedback on extraction results to find topic-related documents and records. Our experiments show that our system achieves high extraction accuracy across different extraction targets.

T-Scroll: Visualizing Trend in a Time-series of Documents for Interactive User Exploration Reviewed

Yoshiharu Ishikawa, Mikine Hasegawa

Proceedings of 11th European Conference on Research and Advanced Technology for Digital Libraries (ECDL 2007) page： 235-246 2007.9

Authorship：Lead author Language：English

On the Internet, a large number of documents such as news articles and online journals are delivered everyday. We often have to review major topics and topic transitions from a large time-series of documents, but it requires much time and effort to browse and analyze the target documents. We have therefore developed an information visualization system called T-Scroll (Trend/Topic-Scroll) to visualize the transition of topics extracted from those documents. The system takes periodical outputs of the underlying clustering system for a time-series of documents then visualizes the relationships between clusters as a scroll. Using its interaction facility, users can grasp the topic transitions and the details of topics for the target time period. This paper describes the idea, the functions, the implementation, and the evaluation of the T-Scroll system.

Record Extraction from Large-scale Text Resources Considering Topics Reviewed

Jianwei Zhang, Yoshiharu Ishikawa, and Hiroyuki Kitagawa

IPSJ Transactions on Databases Vol. 48 ( SIG 14(TOD 35) ) page： 107-123 2007.9

Language：Japanese Publishing type：Research paper (scientific journal)

In recent years, the research on record extraction from a large number of text documents is becoming popular. However, there still exist some problems in record extraction. 1) When a large number of documents are used for the target of information extraction, the process usually becomes very time-consuming. 2) It is also likely that extracted records may not pertain to the user's interest on the aspect of the topic. To address these problems, in this paper we propose a method for efficiently extracting those records whose topics are relevant to the user's interest. To improve the efficiency of the information extraction system, our method identifies documents from which useful records are probably extracted. Those selected documents are first processed in order to reduce processing cost. Moreover, from these documents user-desired records are apt to be extracted so that high extraction accuracy is obtained. Our experiments show that our system reduces the processing cost with achieving high extraction accuracy.

Processing Spatial Queries Based on Imprecise Location Information Reviewed

Yoshiharu Ishikawa

DBSJ Letters Vol. 6 ( 2 ) page： 49-52 2007.9

Authorship：Lead author Language：Japanese Publishing type：Research paper (scientific journal)

In sensor environments and mobile robot applications, we often find the situation in which the location of an object is imprecise due to measurement errors and/or object movements. In this paper, we present an approach for processing spatial queries when the location of a query object is specified by a probabilistic density function based on the Gaussian distribution.

T-Scroll: A Trend Visualization System Based on Clustering of a Time-series of Documents Reviewed

Mikine Hasegawa, Yoshiharu Ishikawa

IPSJ Transactions on Databases Vol. SIG 20 ( TOD 36 ) page： 61-78 2007.12

Language：Japanese Publishing type：Research paper (scientific journal)

On the Internet, a large number of documents such as news articles and online journals are delivered everyday. Documents continually delivered with timestamps such as issue dates are called a time-series of documents. We often need to review major topics and trends from a large time-series of documents, but it requires much time and effort to browse and analyze the target documents. We have therefore developed an information visualization system called T-Scroll (Trend/Topic-Scroll) to display the overall trends extracted from those documents. The system takes periodical outputs of the underlying clustering system for a time-series of documents then visualizes the relationships between clusters as a scroll. Using its interaction facility, users can grasp the trends and the details of the topics contained in the documents. This paper describes the idea, the functions, the implementation, and the evaluation of the T-Scroll system.

A Novelty-based Clustering Method for On-line Documents Reviewed

Sophoin Khy, Yoshiharu Ishikawa, and Hiroyuki Kitagawa

World Wide Web Journal Vol. 11 ( 1 ) page： 1-37 2008.3

Language：English Publishing type：Research paper (scientific journal)

In this paper, we describe a document clustering method called novelty-based document clustering. This method clusters documents based on similarity and novelty. The method assigns higher weights to recent documents than old ones and generates clusters with the focus on recent topics. The similarity function is derived probabilistically, extending the conventional cosine measure of the vector space model by incorporating a document forgetting model to produce novelty-based clusters. The clustering procedure is a variation of the K-means method. An additional feature of our clustering method is an incremental update facility, which is applied when new documents are incorporated into a document repository. Performance of the clustering method is examined through experiments. Experimental results show the efficiency and effectiveness of our method.

Traceable P2P Record Exchange Based on Database Technologies Reviewed

Fengrong Li and Yoshiharu Ishikawa

Proceedings of the 10th Asia Pacific Web Conference (APWeb 2008) page： 475-486 2008.4

Language：English

Information exchanges in P2P networks have become very popular in recent years. However, tracing how data circulates between peers and how data modifications are performed during the circulation before reaching the destination are not easy because data replications and modifications are performed independently by peers. This creates a lack of reliability among the records exchanged. To provide reliable and flexible information exchange facilities in P2P networks, we propose a framework for a record exchange system based on database technologies. The system consists of three layers: a user layer, a logical layer and a physical layer. Its tracing operations are executed as distributed recursive queries among cooperating peers in a P2P network. This paper describes the concept and overviews the framework.

Monitoring Aggregate k-NN Objects in Road Networks Reviewed

Lu Qin, Jeffrey Xu Yu, Bolin Ding, and Yoshiharu Ishikawa

Proceedings of 20th International Conference on Scientific and Statistical Database Management (SSDBM 2008) page： 168-186 2008.7

Language：English Publishing type：Research paper (scientific journal)

In recent years, there is an increasing need to monitor k nearest neighbor (k-NN) in a road network. There are existing solutions on either monitoring k-NN objects from a single query point over a road network, or computing the snapshot k-NN objects over a road network to minimize an aggregate distance function with respect to multiple query points. In this paper, we study a new problem that is to monitor k-NN objects over a road network from multiple query points to minimize an aggregate distance function with respect to the multiple query points. We call it a continuous aggregate k-NN (CANN) query. We propose a new approach that can significantly reduce the cost of computing network distances when monitoring aggregate k-NN objects on road networks. We conducted extensive experimental studies and confirmed the fficiency of our algorithms.

Traceable P2P Record Exchange: A Database-Oriented Approach Reviewed

Fengrong Li, Takuya Iida, and Yoshiharu Ishikawa

Frontiers of Computer Science in China Vol. 2 ( 3 ) page： 257-267 2008.9

Language：English Publishing type：Research paper (scientific journal)

In recent years, peer-to-peer (P2P) technologies are used for flexible and scalable information exchange in the Internet, but there exist problems to be solved for reliable information exchange. It is important to trace how data circulates between peers and how data modifications are performed during the circulation before reaching the destination for enhancing the reliability of exchanged information. However, such lineage tracing is not easy in current P2P networks, since data replications and modifications are performed independently by autonomous peers---this creates a lack of reliability among the records exchanged. In this paper, we propose a framework for traceable record exchange in a P2P network. By managing historical information in distributed peers, we make the modification and exchange histories of records traceable. One of the features of our work is that the database technologies are utilized for realizing the framework. Histories are maintained in a relational database in each peer, and tracing queries are written in the datalog query language and executed in a P2P network by cooperating peers. This paper describes the concept of the framework and overviews the approach to query processing.

A Query Language and Its Processing for Time-Series Document Clusters Reviewed

Sophoin Khy, Yoshiharu Ishikawa, and Hiroyuki Kitagawa

Proceedings of the 11th International Conference on Asia-Pacific Digital Libraries (ICADL 2008) page： 82-92 2008.12

Language：English

Document clustering methods for time-series documents produce a sequence of snapshots of clustering over time. Analyzing the contents (topics) and trends in a long sequence of clustering snapshots is hard and requires efforts since there are too many number of clusters; a user may need to access every cluster or read every document contained in each cluster. In this paper, we propose a framework to find clusters of user interest and change patterns called transition patterns involving the clusters. A cluster in a clustering result may persist in another cluster, branch into more than one cluster, merge with other clusters to form one cluster, or disappear in the adjacent clustering result. This research aims at providing users facilities to retrieve specific transition patterns in the clustering results. For this purpose, we propose a query language for time-series document clustering results and an approach to query processing. The first experimental results on TDT2 corpus clustering results are presented.

Range Query Processing for Imprecise Objects with Gaussian Distributions

Yoshiharu Ishikawa

The 4th Korea-Japan Workshop (KJDB 2008) page：（招待講演） 2008.9

Querying Topic Evolution in Time Series Document Clusters Reviewed

Sophoin Khy, Yoshiharu Ishikawa, and Hiroyuki Kitagawa

Vol. 7 ( 3 ) page： 7-12 2008.12

Language：English Publishing type：Research paper (scientific journal)

A document clusteringmethod for time series documents produces a sequence of clustering results over time. Analyzing the contents and trends in a long sequence of clustering results is a hard and tedious task since there are too many number of clusters. In this paper, we propose a framework to find clusters of users' topics of interest and evolution patterns called transition patterns involving
the topics. A cluster in a clustering result may continue to appear in or move to another cluster, branch into more than one cluster, merge with other clusters to form one cluster, or disappear in the adjacent clustering result. This research aims at providing users facilities to retrieve specific transition patterns in the clustering results. For this purpose, we propose a query language for time series document clustering results and an approach to query processing. The first experimental results on TDT2 corpus clustering results are presented.

Spatial Range Querying for Gaussian-Based Imprecise Query Objects Reviewed

Yoshiharu Ishikawa, Yuichi Iijima, and Jeffrey Xu Yu

Proceedings of the 25th International Conference on Data Engineering (ICDE 2009) page： 676-687 2009.4

Authorship：Lead author Language：English

In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In this paper, we present query processing methods for spatial databases in which the position of the query object is imprecisely specified by a probability density function based on a Gaussian distribution. We define the notion of a {\em probabilistic range query\/} by extending the traditional notion of a spatial range query, then present three strategies for query processing. Since the qualification probability evaluation of target objects requires numerical integration by a method such as the Monte Carlo method, reduction of the number of candidate objects that should be evaluated has a large impact on query performance. We compare three strategies and their combinations in terms of the experiments and evaluate their effectiveness.

`Pay-as-you-go' Processing for Tracing Queries in a P2P Record Exchange System Reviewed

Fengrong Li, Takuya Iida, and Yoshiharu Ishikawa

Proceedings of the 14th International Conference on Database Systems for Advanced Applications (DASFAA 2009) page： 323-327 2009.4

Language：English

In recent years, data provenance or lineage tracing has become an acute issue in the database research. Our target is the data provenance issue in peer-to-peer (P2P) networks where duplicates and modifications of data occur independently in autonomous peers. To ensure reliability among the exchanged data in P2P networks, we have proposed a reliable record exchange framework with tracing facilities based on database technologies in [5,6]. The framework is based on the "pay-as-you-go" approach in which the system maintains the minimum amount of information for tracing with low maintenance cost and a user pays the cost when he or she issues a tracing query to the system. This paper focuses on its two alternative query processing strategies and compare their characteristics according to the performance.

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Papers - ISHIKAWA, Yoshiharu