Faculty Profiles - ISHIKAWA, Yoshiharu

Papers - ISHIKAWA, Yoshiharu

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同時実行B⁺木のマルチバージョン化の検討

桑村真生, 杉浦健人, 平野匠真, 石川佳治, 陸可鏡

第16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 2024） 2024.2

永続メモリ向けロックフリー索引Bz木に関する研究

中山宗, 杉浦健人, 石川佳治, 陸可鏡

第16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 2024） 2024.2

来歴情報を活用したデータベースからの因果推論

大岩和樹, 石川佳治, 杉浦健人, 陸可鏡

第16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 2024） 2024.2

因果推論に基づくデータベースからの仮説問合せについて

大岩和樹, 石川佳治, 杉浦健人, 陸可鏡

情報処理学会第86回全国大会 2024.2

確率的イベントストリームにおける最小記述長に基づく代表系列パターンの検出

中村航規, 杉浦健人, 石川佳治, 陸可鏡

情報処理学会第86回全国大会 2024.2

Universal Adaptive Radix Treeにおける空間分割戦略の改善

杉江祐介, 杉浦健人, 石川佳治, 陸可鏡, 井戸佑

情報処理学会第86回全国大会 2024.2

Watermark Management for Edge Computing System Based on Approximate Fault Tolerance Reviewed

TAKAO Daiki, SUGIURA Kento, ISHIKAWA Yoshiharu, LU Kejing

Vol. J107-D ( 5 ) page： 335 - 347 2024.5

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers

Data loss due to failure is an important issue in data stream processing for edge computing. In particular, the detection of data loss is a big problem, and the conventional method accumulates delays. In this paper, we propose a watermark management method based on the cooperation of edges and a gateway. Our method estimates lost data at a later stage and generates watermarks based on user's error requirements. We utilize an error guarantee mechanism to calculate the error requirements for edges in each failure situation. We show that our method can recover lost data precisely and reduce delays according to the user's requirements through experiments.

DOI： 10.14923/transinfj.2023dep0007

CiNii Research

Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search Reviewed

Kejing Lu, Chuan Xiao, Yoshiharu Ishikawa

Proceedings of Machine Learning Research Vol. 235 page： 33177 - 33195 2024.5

Language：English Publishing type：Research paper (scientific journal) Publisher：Proceedings of Machine Learning Research

Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal challenge in the field of machine learning. In recent years, graph-based methods have emerged as the superior approach to ANNS, establishing a new state of the art. Although various optimizations for graph-based ANNS have been introduced, they predominantly rely on heuristic methods that lack formal theoretical backing. This paper aims to enhance routing within graph-based ANNS by introducing a method that offers a probabilistic guarantee when exploring a node's neighbors in the graph. We formulate the problem as probabilistic routing and develop two baseline strategies by incorporating locality-sensitive techniques. Subsequently, we introduce PEOs, a novel approach that efficiently identifies which neighbors in the graph should be considered for exact distance calculation, thus significantly improving efficiency in practice. Our experiments demonstrate that equipping PEOs can increase throughput on commonly utilized graph indexes (HNSW and NSSG) by a factor of 1.6 to 2.5, and its efficiency consistently outperforms the leading-edge routing technique by 1.1 to 1.4 times. The code and datasets used for our evaluations are publicly accessible at https://github.com/ICML2024-code/PEOs.

Scopus

Acceleration of Synopsis Construction for Bounded Approximate Query Processing Reviewed

Tianjia Ni, Kento Sugiura, Yoshiharu Ishikawa, Kejing Lu

The DASFAA 2024 Workshop on Emerging Results in Data Science and Engineering (ERDSE 2024) Vol. 14667 LNCS page： 236 - 251 2025.1

データベースにおける仮説推論問合せについて

大岩和樹, 石川佳治, 杉浦健人, 陸可鏡

情報処理学会研究報告 Vol. 2024-DBS-179 2024.9

ロックフリー索引のトライ木化による高速化に関する研究

井戸佑, 杉浦健人, 石川佳治, 陸可鏡

情報処理学会研究報告 Vol. 2024-DBS-179 2024.9

Guaranteeing an Exact Error Bound for Bounded Approximate Query Processing Reviewed

Tianjia Ni, Kento Sugiura, Yoshiharu Ishikawa, Kejing Lu

Journal of Information Processing Vol. 32 ( 0 ) page： 903 - 915 2024.11

Language：English Publishing type：Research paper (scientific journal) Publisher：Information Processing Society of Japan

In recent years, efficient query processing in databases has become more crucial with the sophistication of analysis requirements. Approximate query processing (AQP) is one of the approaches to dealing with database queries on big data. In this research, we focus on synopsis construction on a relational database and the query technology based on it, called bounded approximate query processing (BAQ). This paper points out the limitations of existing research BAQ and solves them by the proposed BAQ±. BAQ± is capable of dealing datasets with a broader range while ensuring the exact error bound. Furthermore, compared to the original BAQ, BAQ± generates a more compact synopsis with various data distributions. We introduce an innovative bucketing approach to construct smaller synopses while keeping the same properties in BAQ. Additionally, we propose novel rewrite methods for answering online queries by deriving error guarantee conditions. We provide extensive experiment assessments using different distribution datasets. Our BAQ± provides a smaller synopsis at 64% the size of BAQ and efficiently executes online queries within the exact error bound.

DOI： 10.2197/ipsjjip.32.903

Scopus

CiNii Research

researchmap

Hierarchical and Efficient Synopsis Construction for Bounded Approximate Query Processing Reviewed Open Access

Tianjia Ni, Kento Sugiura, Yoshiharu Ishikawa, Kejing Lu

Journal of Information Processing Vol. 33 ( 0 ) page： 115 - 127 2025.2

Language：English Publishing type：Research paper (scientific journal) Publisher：Information Processing Society of Japan

Approximate query processing (AQP) has gained traction as an effective technique for executing queries on big data. Bounded approximate query processing (BAQ) is a recently proposed framework that stores a summary of an original table as a synopsis and ensures that its approximation errors remain below a user-specified threshold. Based on the BAQ framework, we have extended it to BAQ± to guarantee strictly bounded errors for more diverse data. However, BAQ and BAQ± still have problems when constructing synopses. They require time-consuming data sorting for each numerical attribute and cannot summarize high-cardinality categorical attributes, such as spatiotemporal data. To overcome these problems, we propose a novel framework called Hierarchical BAQ (HBAQ) and a synopsis construction method in this paper. HBAQ constructs multiple synopses based on the dimension tables of several categorical attributes and uses them to answer OLAP queries efficiently. We also introduce a new bucket definition to summarize numerical attributes effectively and support incremental updates for synopses. We conducted extensive experiments with several datasets. The experimental results show that HBAQ achieved half the construction time of BAQ with lower memory consumption. Furthermore, HBAQ could answer OLAP queries more efficiently than BAQ using hierarchically constructed synopses.

DOI： 10.2197/ipsjjip.33.115

Open Access

Scopus

CiNii Research

データベースにおける来歴情報を考慮した仮説推論問合せのための問合せ言語とその実装

大岩和樹, 石川佳治, 杉浦健人, 陸可鏡

第17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 Forum 2025) 2025.2

同時実行B+木のマルチバージョン化と範囲走査性能の評価

桑村真生, 杉浦健人, 石川佳治, 陸可鏡

第17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 Forum 2025) 2025.2

ロックフリー索引のトライ木化による改善と評価

井戸佑, 杉浦健人, 石川佳治, 陸可鏡

第17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 Forum 2025) 2025.2

部分ステートマシンレプリケーションにおける投機的分散トランザクション処理

白石裕輝、杉浦健人、石川佳治

第17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 Forum 2025) 2025.2

バックオフ戦略によるロックフリーMulti-Word Compare-and-Swap命令の改善

吽野元基, 杉浦健人, 石川佳治, 陸可鏡

情報処理学会第87回全国大会 2025.3

楽観的ロック手法OptiQLの再現実装及び性能評価

阿井星后, 杉浦健人, 石川佳治, 陸可鏡

情報処理学会第87回全国大会 2025.3

多腕バンディットを利用した自動索引作成の再現実装及び性能評価

張智嘉, 郭宏遠, 杉浦健人, 石川佳治, 陸可鏡

情報処理学会第87回全国大会 2025.3

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Papers - ISHIKAWA, Yoshiharu