Faculty Profiles - ISHIKAWA, Yoshiharu

Papers - ISHIKAWA, Yoshiharu

Division display 161 - 180 of about 334 ／ All the affair displays >>

オントロジを用いた行動イベント分析

中村亮, 石川佳治, 杉浦健人, 脇田佑希子, 佐々木勇和

第15回情報科学技術フォーラム (FIT 2016) page： 6C-2 2016.9

不完全な道路ネットワークを用いたマップマッチングおよび道路ネットワークの補間手法の提案

余家豪, 佐々木勇和, 石川佳治

第15回情報科学技術フォーラム (FIT 2016) page： 7C-1 2016.9

ジオタグ付き写真を用いた意味的な移動軌跡の分析

瀧本祥章, 石川佳治, 杉浦健人, 脇田佑希子

第15回情報科学技術フォーラム (FIT 2016) page： 7C-3 2016.9

確率的データストリームにおける情報利得を用いたパターン照合手法

杉浦健人, 石川佳治

情報処理学会データベースシステム・情報基礎とアクセス技術合同研究会 page： 2016-DBS-163(5) 2016.9

k-Expected Nearest Neighbor Search over Gaussian Objects Reviewed

Tingting Dong, Yoshiharu Ishikawa, Chuan Xiao, Jing Zhao

Journal of Computers (JCP) Vol. 12 ( 2 ) page： 105-115 2017.3

時空間データ分析のための差分ヒストグラム構築手法

趙セイ, 石川佳治, 杉浦健人, 脇田佑希子

第9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 2017) page： G1-2 2017.3

確率的データストリームにおける情報利得を用いたTop-kパターン照合手法

杉浦健人, 石川佳治

第9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 2017) page： G3-3 2016.3

略記問合せに対する効率的な問合せ自動補完

胡晟, 肖川, 石川佳治

第9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 2017) page： G4-4 2017.3

ライフログサービスのためのオントロジに基づく行動イベント処理

中村亮, 石川佳治, 杉浦健人, 脇田佑希子

第9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 2017) page： I5-1 2016.3

不完全な道路ネットワークにおけるマップマッチングとクラスタリング手法を用いた道路セグメントの補間手法の提案

余家豪, 佐々木勇和, 石川佳治

第9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 2017) page： A5-5 2017.3

ジオタグ付き写真の被写体を考慮した意味的な移動軌跡の分析

瀧本祥章, 杉浦健人, 石川佳治

第9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 2017) page： H7-5 2017.3

配列指向DBMSを用いた避難シミュレーションデータの格納と分析 Open Access

河井悠佑, 杉浦健人, 趙セイ, 石川佳治

情報処理学会第79回全国大会 page： 1L-03 2017.3

Event Calculusに基づく複合イベント処理について Open Access

金山貴紀, 杉浦健人, 石川佳治

情報処理学会第79回全国大会 page： 1L-05 2017.3

オントロジに基づく移動軌跡の意味的な拡張と検索 Open Access

勝田健斗, 中村亮, 瀧本祥章, 石川佳治

情報処理学会第79回全国大会 page： 2K-09 2017.3

都市・国土環境分析のためのレジリエンス・サステナビリティ評価ワークベンチの開発

石川佳治，脇田佑希子, 杉浦健人, 杉本賢二, 加藤博和

電子情報通信学会総合大会 page： D-4-2 2017.3

Grouping Methods for Pattern Matching over Probabilistic Data Streams Reviewed Open Access

Kento Sugiura, Yoshiharu Ishikawa, Yuya Sasaki

IEICE Transactions on Information and Systems Vol. E100-D ( 4 ) page： 718-729 2017.4

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

As the development of sensor and machine learning technologies has progressed, it has become increasingly important to detect patterns from probabilistic data streams. In this paper, we focus on complex event processing based on pattern matching. When we apply pattern matching to probabilistic data streams, numerous matches may be detected at the same time interval because of the uncertainty of data. Although existing methods distinguish between such matches, they may derive inappropriate results when some of the matches correspond to the real-world event that has occurred during the time interval. Thus, we propose two grouping methods for matches. Our methods output groups that indicate the occurrence of complex events during the given time intervals. In this paper, first we describe the definition of groups based on temporal overlap, and propose two grouping algorithms, introducing the notions of complete overlap and single overlap. Then, we propose an efficient approach for calculating the occurrence probabilities of groups by using deterministic finite automata that are generated from the query patterns. Finally, we empirically evaluate the effectiveness of our methods by applying them to real and synthetic datasets.

DOI： 10.1587/transinf.2016DAP0014

Open Access

Time-Aware Personalized Destination Prediction Reviewed

Yoshiaki Takimoto, Kyosuke Nishida, Yuki Endo, Hiroyuki Toda, Hiroshi Sawada, Yoshiharu Ishikawa

IEICE Transactions on Information and Systems (Japanese Edition) Vol. J100-D ( 4 ) page： 472-484 2017.4

Top-k Pattern Matching Using an Information-theoretic Criterion over Probabilistic Data Streams Reviewed

Kento Sugiura, Yoshiharu Ishikawa

Proceedings of APWeb-WAIM Joint Conference on Web and Big Data 2017 page： 511-526 2017.7

Language：English

As the development of data mining technologies for sensor data streams, more sophisticated methods for complex event processing are demanded. In the case of event recognition, since event recognition results may contain errors, we need to deal with the uncertainty of events. We therefore consider probabilistic event data streams with occurrence probabilities of events, and develop a pattern matching method based on regular expressions. In this paper, we first analyze the semantics of pattern matching over non-probabilistic data streams, and then propose the problem of top-k pattern matching over probabilistic data streams. We introduce the use of an information-theoretic criterion to select appropriate matches as the result of pattern matching. Then, we present an efficient algorithm to detect top-k matches, and evaluate the effectiveness of our approach using real and synthetic datasets.

DOI： 10.1007/978-3-319-63579-8_39

Extraction of Frequent Patterns Based on Users' Interests from Semantic Trajectories with Photographs Reviewed

Yoshiaki Takimoto, Kento Sugiura, Yoshiharu Ishikawa

Proceedings of the 21st International Database Engineering & Applications Symposium (IDEAS 2017) page： 219-227 2017.7

Language：English

Along with the popularization of location-based social networking (LBSN), semantic trajectories, which are trajectories with additional information such as photographs and texts, are increasing, and their utilization is required. We consider frequent pattern extraction as applicable to analysis of semantic trajectories and extraction of regions of interest (ROIs). In this research, we propose SimDBSCAN, which considers both spatial density and similarity of points, by extending DBSCAN, which uses density-based clustering, in order to capture users' interests. Since SimDBSCAN identifies points that are interested in the same object in the neighborhood as ROIs, it is possible to detect not only known ROIs such as tourist sites but also unknown ROIs. In this paper, we explain the algorithm of SimDBSCAN and present the experimental results using photographs collected from Flickr. The experiments show that useful ROIs and patterns can be extracted by the proposed method.

DOI： 10.1145/3105831.3105870

Reverse Direction-based Surrounder Queries for Mobile Recommendations Reviewed

Xi Guo, Yoshiharu Ishikawa, Yonghong Xie, Aziguli Wulamu

World Wide Web Journal Vol. 20 ( 5 ) page： 885-913 2017.9

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

This paper proposes a new spatial query called a 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 theta. If a POI cannot be dominated by another POI, it is a DBS of the user. We also propose an extended query called 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/s11280-016-0422-0

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Papers - ISHIKAWA, Yoshiharu