Faculty Profiles - ISHIKAWA, Yoshiharu

Papers - ISHIKAWA, Yoshiharu

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逆最近傍問合せに基づくデマンドヒートマップの連続的な更新手法

李セイ, 石川佳治, 趙セイ, 杉浦健人

第16回情報科学技術フォーラム (FIT 2017) 論文集 page： D-012 2017.9

Event Calculusに基づく複合イベント処理について

金山貴紀, 石川佳治, 杉浦健人

第16回情報科学技術フォーラム (FIT 2017) 論文集 page： D-019 2017.9

ビッグデータへの取り組みと周辺領域との融合

小口正人, 中野美由紀, 石川佳治, 木俵豊

電子情報通信学会誌 Vol. 100 ( 10 ) page： 1059 2017.10

An Efficient Algorithm for Location-Aware Query Autocompletion Reviewed

Sheng Hu, Chuan Xiao, Yoshiharu Ishikawa

IEICE Transactions on Information and Systems Vol. E101-D ( 1 ) page： 181-192 2018.1

RDBと連携したイベント計算による複合イベント処理

金山貴紀, 石川佳治, 杉浦健人

第10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 2018) 論文集 page： E1-4 2018.3

大量な映像における高速な動的場面の分析と検索

胡晟, 劉健全, 西村祥治, 石川佳治

第10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 2018) 論文集 page： A3-3 2018.3

ネットワーク上の軌跡データに対する時間制約付き二点間経路の列挙

小出智士, 吉村貴克, 肖川, 石川佳治

第10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 2018) 論文集 page： H5-1 2018.3

少数ユーザの移動履歴を考慮した大規模な集計データからの人流推定

河井悠佑, 田中佑典, 戸田浩之, 石川佳治

第10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 2018) page： C5-3 2018.3

配列DBMSにおける時空間データの差分分析について

趙セイ, 石川佳治, 河井悠佑, 杉浦健人

第10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 2018) 論文集 page： C6-3 2018.3

配列DBMSにおける空間スキャン統計量の計算手法

安田健人，河井悠佑, 趙セイ，杉浦健人，石川佳治

情報処理学会全国大会講演論文集 page： 4L-5 2018.3

Context-Sensitive Query Auto-Completion with Knowledge Base

Yaobin Hu, Chuan Xiao, Yoshiharu Ishikawa

page： P5-2 2018.3

RDBの構造を考慮したデータベースからの学習手法について

志村薫，石川佳治, 杉浦健人

情報処理学会全国大会講演論文集 page： 6L-6 2018.3

大規模データ分析のための可視化手法に関する検討

野田昌太郎，河井悠佑, 趙セイ，杉浦健人，石川佳治

情報処理学会全国大会講演論文集 page： 6L-7 2018.3

An Analysis Technique of Evacuation Simulation Using an Array DBMS Reviewed

Yusuke Kawai, Jing Zhao, Kento Sugiura, Yoshiharu Ishikawa, Yukiko Wakita

Journal of Disaster Research Vol. 13 ( 2 ) page： 338-346 2018.3

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

Today, large-scale simulations are thriving because of the increase of computating performance and storage capacity. Understanding the results of these simulations is not easy, and hence, support for interactive and exploratory analysis is becoming more important. This study focuses on spatio-temporal simulations and attempts to develop an analysis technology to support them. It uses a database system for supporting interactive analysis of large-scale data.
Since the data gained via spatio-temporal simulations is not suitable for management in a relational DBMS (RDBMS), this study uses an array DBMS, a type of DBMS that has been garnering increased attention in recent years. An array DBMS is designed for the management of large-scale array data; it provides a logical model for array data, yet it also supports efficient query processing. SciDB is used as our specific array DBMS in this paper.
This study targets disaster evacuation simulation data and demonstrates via experimentation that the query-processing functions offered by an array DBMS provide effective analysis support.

DOI： 10.20965/jdr.2018.p0338

Sequenced Route Query with Semantic Hierarchy Reviewed

Yuya Sasaki, Yoshiharu Ishikawa, Yasuhiro Fujiwara, Makoto Onizuka

Proceedings of 21st International Conference on Extending Database Technology (EDBT 2018) page： 37-48 2018.3

GPH: Similarity Search in Hamming Space

Jianbin Qin, Yaoshu Wang, Chuan Xiao, Wei Wang, Xuemin Lin, Yoshiharu Ishikawa

Proceedings of 34th International Conference on Data Engineering (ICDE 2018) page： (not fixed) 2018.4

CiNCT: Compression and Retrieval for Massive Vehicular Trajectories via Relative Movement Labeling Reviewed

Satoshi Koide, Yukihiro Tadokoro, Chuan Xiao, Yoshiharu Ishikawa

Proceedings of 34th International Conference on Data Engineering (ICDE 2018) page： (not fixed) 2018.4

Histogram Construction for Difference Analysis of Spatio-Temporal Data on Array DBMS Reviewed

Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

2018 Australasian Database Conference (ADC 2018) page： 41-52 2018.5

Language：English

To analyze scientific data, there are frequent demands for comparing multiple datasets on the same subject to detect any differences between them. For instance, comparison of observation datasets in a certain spatial area at different times or comparison of spatial simulation datasets with different parameters are considered to be important. Therefore, this paper proposes a difference operator in spatio-temporal data warehouses, based on the notion of histograms in the database research area. We propose a difference histogram construction method and they are used for effective and efficient data visualization in difference analysis. In addition, we implement the proposed algorithms on an array DBMSs SciDB, which is appropriate to process and manage scientific data. Experiments are conducted using mass evacuation simulation data in tsunami disasters, and the effectiveness and efficiency of our methods are verified.

DOI： 10.1007/978-3-319-92013-9_4

地域のサステナビリティとレジリエンスを同時に考慮できる評価システムの開発

朴秀日, 加藤博和, 石川佳治, 山中英生, 奥嶋政嗣, 渡辺公次郎

第57回土木計画学研究発表会・講演集 page： 36-01 2018.6

Enhanced Indexing and Querying of Trajectories in Road Networks via String Algorithms Reviewed

Satoshi Koide, Yukihiro Tadokoro, Takayoshi Yoshimura, Chuan Xiao, Yoshiharu Ishikawa

ACM Transactions on Spatial Algorithms and Systems Vol. 4 ( 1 ) page： 3 2018.6

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

In this article, we propose a novel indexing and querying method for trajectories constrained in a road network. We aim to provide efficient algorithms for various types of spatiotemporal queries that involve routing in road networks, such as (1) finding moving objects that have traveled along a given path during a given time interval, (2) extracting all paths traveled after a given spatiotemporal context, and (3) enumerating all paths between two locations traveled during a certain time interval. Unlike the existing methods in spatial database research, we employ indexing techniques and algorithms from string processing. This idea is based on the fact that we can represent spatial paths as strings, because trajectories in a network are represented as sequences of road segment IDs. The proposed SNT-index (suffix-array-based network-constrained trajectory index) introduces two novel concepts to trajectory indexing. The first is FM-index, which is a compact in-memory data structure for pattern matching. The second is an inverse suffix array, which allows the FM-index to be integrated with the temporal information stored in a forest of B+-trees. Thanks to these concepts, we can reduce the number of B+-tree accesses required by the query processing algorithms to a constant number, something that cannot be achieved with existing methods. Although an FM-index is essentially a static index, we also propose a practical method of appending new data to the index. Finally, experiments show that our method can process the target queries for more than 1 million trajectories in a few tens of milliseconds, which is significantly faster than what the baseline algorithms can achieve without string algorithms.

DOI： 10.1145/3200200

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Papers - ISHIKAWA, Yoshiharu