Faculty Profiles - ISHIKAWA, Yoshiharu

Papers - ISHIKAWA, Yoshiharu

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LBSNのための汎用的なオントロジフレームワーク構築

簗井美咲, 高橋正和, 佐々木勇和, 石川佳治

第7回データ工学と情報マネジメントに関するフォーラム（DEIM 2015）論文集 page： F6-2 2015.3

KL情報量に基づいたガウス分布の類似検索

董テイテイ, 石川佳治, 肖川

第7回データ工学と情報マネジメントに関するフォーラム（DEIM 2015）論文集 page： A6-3 2015.3

多階層のカテゴリ分類を用いたスカイライン経路検索について

佐々木勇和, 石川佳治

第7回データ工学と情報マネジメントに関するフォーラム（DEIM 2015）論文集 page： A6-4 2015.3

参加型センシングのためのタスク割当て手法

趙菁, 姜仁河, 董テイテイ, 佐々木勇和, 石川佳治

第7回データ工学と情報マネジメントに関するフォーラム（DEIM 2015）論文集 page： C6-5 2015.3

密度に基づく意味的な軌跡パターンの発見

姜仁河, 趙菁, 董テイテイ, 佐々木勇和, 石川佳治

第7回データ工学と情報マネジメントに関するフォーラム（DEIM 2015）論文集 page： C8-4 2015.3

オントロジとデータベース技術を活用した複合イベント処理システム

高橋正和, 簗井美咲, 佐々木勇和, 石川佳治

第7回データ工学と情報マネジメントに関するフォーラム（DEIM 2015）論文集 page： C8-4 2015.3

Twitterにおけるユーザごとの意見変化抽出手法

近藤弘隆, 鈴木優, 石川佳治

情報処理学会第77回全国大会講演論文集 page： 2M-04 2015.3

時空間データ分析のためのSpatialHadoopの拡張

瀧本祥章, 杉浦健人, 佐々木勇和, 石川佳治

情報処理学会第77回全国大会講演論文集 page： 4N-01 2015.3

Content Reuse Detection in Text Documents

Pei Wang, Chuan Xiao, Yoshiharu Ishikawa

情報処理学会第77回全国大会講演論文集 page： 5N-04 2015.3

AEDSMS: Automotive Embedded Data Stream Management System Reviewed

Akihiro Yamaguchi, Yukikazu Nakamoto, Kenya Sato, Yoshiharu Ishikawa, Yousuke Watanabe, Shinya Honda, Hiroaki Takada

Proceedings of the 31st International Conference on Data Engineering (ICDE 2015), page： 1292-1303 2015.4

Language：English

Data stream management systems (DSMSs) are useful for the management and processing of continuous data at a high input rate with low latency. In the automotive domain, embedded systems use a variety of sensor data and communications from outside the vehicle to promote autonomous and safe driving. Thus, the software developed for these systems must be capable of handling large volumes of data and complex processing. At present, we are developing a platform for the integration and management of data in an automotive embedded system using a DSMS. However, compared with conventional DSMS fields, we have encountered new challenges such as precompiling queries when designing automotive systems (which demands time predictability), distributed stream processing in in-vehicle networks, and real-time scheduling and sensor data fusion by stream processing. Therefore, we developed an automotive embedded DSMS (AEDSMS) to address these challenges. The main contributions of the present study are: (1) a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) the development of AEDSMS to tackle these challenges; and (3) an evaluation of AEDSMS during runtime using a driving assistance application.

DOI： 10.1109/ICDE.2015.7113377

Grouping Methods for Pattern Matching in Probabilistic Data Streams Reviewed

Kento Sugiura, Yoshiharu Ishikawa, Yuya Sasaki

Proceedings of the 20th International Conference on Database Systems for Advanced Applications (DASFAA 2015) 2015.4

Language：English

In recent years, complex event processing has attracted considerable interest in research and industry.Pattern matching is used to find complex events in data streams. In probabilistic data streams, however, the system may find multiple matches in a given time interval. This may result in inappropriate matches, because multiple matches may correspond to a single event. We therefore propose grouping methods of matches for probabilistic data streams, and call such merged matches a group. We describe the definitions and generation methods of groups, propose an efficient approach for calculating an occurrence probability of a group, and compare the proposed approach with a naive one by experiment. The results demonstrate the properties and effectiveness of the proposed method.

DOI： 10.1007/978-3-319-18120-2_6

共同執筆コンテンツにおける単語の起源追跡 Reviewed

中村晃，鈴木優，石川佳治

情報処理学会論文誌データベース（TOD） Vol. 8 ( 2 ) page： 43-56 2015.6

確率的データストリームにおけるパターン照合結果の時間的重複に基づくグループ化

杉浦健人, 石川佳治, 佐々木勇和

情報処理学会研究報告 Vol. 2015-DBS-161 ( 7 ) page：（番号なし） 2015.8

空間クラウドソーシングのための多様性を考慮したタスク割り当て手法

趙セイ, 石川佳治, 肖川, 董テイテイ, 佐々木勇和

情報処理学会研究報告 Vol. 2015-DBS-161 ( 8 ) page：（番号なし） 2015.8

Detecting Reused Contents in Text Documents

Pei Wang, Chuan Xiao, Yoshiharu Ishikawa

page： 3C-2 2015.9

シミュレーションデータの分析管理のためのデータウェアハウスについて

石川佳治, 王元元, 董テイテイ, 杉浦健人, 佐々木勇和

第14回情報科学技術フォーラム (FIT 2015) 講演論文集 page： 3D-2 2015.9

複数ドメインのデータストリームにおける意味的なイベント検出について

佐々木勇和, 石川佳治, 杉浦健人

第14回情報科学技術フォーラム (FIT 2015) 講演論文集 page： 3D-3 2015.9

Reverse Direction-Based Surrounder Queries Reviewed

Xi Guo, Yoshiharu Ishikawa, Aziguli Wulamu, Yonghong Xie

Proceedings of the 17th Asia-Pacific Web Conference (APWeb 2015) page： 280-291 2015.9

Language：English

This paper proposes a new spatial query called the reverse direction-based surrounder (RDBS) query, which retrieves a user who is seeing a point of interest (POI) as one of their direction-based surrounders (DBSs). According to a user, one POI can be dominated by a second POI if the POIs are directionally close and the first POI is farther from the user than the second is. Two POIs are directionally close if their included angle with respect to the user is smaller than an angular threshold, θ. If a POI cannot be dominated by another POI, it is a DBS of the user. We also propose an extended query called the competitor RDBS query. POIs that share the same RDBSs with another POI are defined as competitors of that POI. We design algorithms to answer the RDBS queries and competitor queries. The experimental results show that the proposed algorithms can answer the queries efficiently.

DOI： 10.1007/978-3-319-25255-1_23

A Density-based Approach for Mining Movement Patterns from Semantic Trajectories Reviewed

Renhe Jiang, Jing Zhao, Tingting Dong, Yoshiharu Ishikawa, Chuan Xiao, Yuya Sasaki

Proceedings of IEEE TENCON 2015 - IEEE Region 10 Conference page：（なし） 2015.11

Language：English

In this paper, we study the problem of discovering all movement patterns from semantic trajectory databases. We propose a two-step method to solve this problem efficiently. We first retrieve frequent movement patterns of categories from the transformed database of sequential categories, and then cluster dense trajectories in a growth-type way for all movement patterns. Moreover, we define a new metric distance function on trajectories. We also use M-tree to cluster trajectories more efficiently. Our experimental results demonstrate the efficiency of the proposed method.

An Automatic Video Reinforcing System based on Popularity Rating of Scenes and Level of Detail Controlling Reviewed

Yuanyuan Wang, Kazutoshi Sumiya, Yukiko Kawai, Yoshiharu Ishikawa

Proceedings of the 11th IEEE International Workshop on Multimedia Information Processing and Retrieval (IEEE-MIPR 2015) page： 529-534 2015.12

Language：English

With the advance of video-on-demand (VOD) services such as Netfix, users are able to watch many kinds of videos anytime and anywhere. While watching a video, recently, users often search related information about it through the Web by using mobile PC. However, users cannot satisfactorily understand and enjoy it because the video keeps playing when they search about it. It is necessary to detect various questions of the video to supplement their related information about each scene for automatic search. However, only one video includes various topics of each scene, furthermore, viewers have different levels of knowledge. Therefore, we have developed a novel automatic video reinforcing system, called TV-Binder, it generates new video contents from one video stream related to viewers' interests and knowledge by adding other related contents (i.e., YouTube videos, images or maps) and by removing unnecessary original scenes, based on topics of each scene. As a result, viewers can satisfy and joyfully watch modified video contents without searching anything. At first, our system extract topics and detect their scenes of a video stream by using closed captions. The system then searches other necessary contents and determines unwanted original scenes based on popularity rating of each original scene and level of detail (LOD) controlling under time pressure. Through this, TVBinder can automatically generate video contents are classified into four quadrants by two axes; one is digest and detailed videos, the other one is videos for experts with knowledge about particular topics and ordinary viewers without special knowledge. In this paper, we discuss our automatic video reinforcing system and an evaluation of its effectiveness.

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Papers - ISHIKAWA, Yoshiharu