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1,674 results on '"Kitagawa, Hiroyuki"'

12. TraPM: A Framework for Online Pattern Matching Over Trajectory Streams

Author

Trisminingsih, Rina, Shaikh, Salman Ahmed, Amagasa, Toshiyuki, Kitagawa, Hiroyuki, Matono, Akiyoshi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Delir Haghighi, Pari, editor, Pardede, Eric, editor, Dobbie, Gillian, editor, Yogarajan, Vithya, editor, ER, Ngurah Agus Sanjaya, editor, Kotsis, Gabriele, editor, and Khalil, Ismail, editor

Published
2023
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13. Integration of Knowledge Bases and External Information Sources via Magic Properties and Query-Driven Entity Linking

Author

Ohmori, Yuuki, Kitagawa, Hiroyuki, Amagasa, Toshiyuki, Matono, Akiyoshi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Delir Haghighi, Pari, editor, Pardede, Eric, editor, Dobbie, Gillian, editor, Yogarajan, Vithya, editor, ER, Ngurah Agus Sanjaya, editor, Kotsis, Gabriele, editor, and Khalil, Ismail, editor

Published
2023
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16. GeoFlink: A Distributed and Scalable Framework for the Real-time Processing of Spatial Streams

Author

Shaikh, Salman Ahmed, Mariam, Komal, Kitagawa, Hiroyuki, and Kim, Kyoung-Sook

Subjects
Computer Science - Databases, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Apache Flink is an open-source system for scalable processing of batch and streaming data. Flink does not natively support efficient processing of spatial data streams, which is a requirement of many applications dealing with spatial data. Besides Flink, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop, etc. do not support streaming workloads and can only handle static/batch workloads. To fill this gap, we present GeoFlink, which extends Apache Flink to support spatial data types, indexes and continuous queries over spatial data streams. To enable the efficient processing of spatial continuous queries and for the effective data distribution across Flink cluster nodes, a gird-based index is introduced. GeoFlink currently supports spatial range, spatial $k$NN and spatial join queries on point data type. An extensive experimental study on real spatial data streams shows that GeoFlink achieves significantly higher query throughput than ordinary Flink processing., Comment: CIKM 2020 Preprint

Published
2020

17. Inflammatory prognostic factors in advanced or recurrent esophageal squamous cell carcinoma treated with nivolumab

Author

Ikoma, Tatsuki, Shimokawa, Mototsugu, Matsumoto, Toshihiko, Boku, Shogen, Yasuda, Tomoyo, Shibata, Nobuhiro, Kurioka, Yusuke, Takatani, Masahiro, Nobuhisa, Tetsuji, Namikawa, Tsutomu, Kitagawa, Hiroyuki, Hanazaki, Kazuhiro, Doi, Keitaro, Shimada, Takanobu, Tsumura, Takehiko, Marusawa, Hiroyuki, Kanaya, Seichiro, Morita, Shuko, Inokuma, Tetsurou, Nagai, Hiroki, Yasui, Hisateru, and Satake, Hironaga

Published
2023
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20. Mercem: Method Name Recommendation Based on Call Graph Embedding

Author

Yonai, Hiroshi, Hayase, Yasuhiro, and Kitagawa, Hiroyuki

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning, Computer Science - Programming Languages
Abstract

Comprehensibility of source code is strongly affected by identifier names, therefore software developers need to give good (e.g. meaningful but short) names to identifiers. On the other hand, giving a good name is sometimes a difficult and time-consuming task even for experienced developers. To support naming identifiers, several techniques for recommending identifier name candidates have been proposed. These techniques, however, still have challenges on the goodness of suggested candidates and limitations on applicable situations. This paper proposes a new approach to recommending method names by applying graph embedding techniques to the method call graph. The evaluation experiment confirms that the proposed technique can suggest more appropriate method name candidates in difficult situations than the state of the art approach., Comment: 9 pages, 9 figures, 2 tables

Published
2019

21. Scaling Fine-grained Modularity Clustering for Massive Graphs

Author

Shiokawa, Hiroaki, Amagasa, Toshiyuki, and Kitagawa, Hiroyuki

Subjects
Computer Science - Social and Information Networks, Computer Science - Data Structures and Algorithms, Physics - Physics and Society, 62H30
Abstract

Modularity clustering is an essential tool to understand complicated graphs. However, existing methods are not applicable to massive graphs due to two serious weaknesses. (1) It is difficult to fully reproduce ground-truth clusters due to the resolution limit problem. (2) They are computationally expensive because all nodes and edges must be computed iteratively. This paper proposes gScarf, which outputs fine-grained clusters within a short running time. To overcome the aforementioned weaknesses, gScarf dynamically prunes unnecessary nodes and edges, ensuring that it captures fine-grained clusters. Experiments show that gScarf outperforms existing methods in terms of running time while finding clusters with high accuracy., Comment: Accepted by IJCAI 2019

Published
2019

25. PR-MVI: Efficient Missing Value Imputation over Data Streams by Distance Likelihood

Author

Bou, Savong, Amagasa, Toshiyuki, Kitagawa, Hiroyuki, Shaikh, Salman Ahmed, Matono, Akiyoshi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pardede, Eric, editor, Delir Haghighi, Pari, editor, Khalil, Ismail, editor, and Kotsis, Gabriele, editor

Published
2022
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26. Streaming Augmented Lineage: Traceability of Complex Stream Data Analysis

Author

Yamada, Masaya, Kitagawa, Hiroyuki, Shaikh, Salman Ahmed, Amagasa, Toshiyuki, Matono, Akiyoshi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pardede, Eric, editor, Delir Haghighi, Pari, editor, Khalil, Ismail, editor, and Kotsis, Gabriele, editor

Published
2022
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27. InTrans: Fast Incremental Transformer for Time Series Data Prediction

Author

Bou, Savong, Amagasa, Toshiyuki, Kitagawa, Hiroyuki, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Cuzzocrea, Alfredo, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2022
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28. BPF: An Effective Cluster Boundary Points Detection Technique

Author

Khalique, Vijdan, Kitagawa, Hiroyuki, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Cuzzocrea, Alfredo, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2022
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30. Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.

Author

Tsunoda, Ryoya, Kume, Keitaro, Kagawa, Rina, Sanuki, Masaru, Kitagawa, Hiroyuki, Mase, Kaori, and Yamagata, Kunihiro

Abstract

The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy, diagnosis codes aimed at billing should not be directly used for clinical research because of the risk of misdiagnosis. To solve this problem, we aimed to develop a novel method for identifying patients with immunoglobulin A nephropathy from billing data using machine learning. The medical records and bills of 3,743 patients who consulted nephrologists at a single center were extracted. Patients were labeled to have been diagnosed with immunoglobulin A nephropathy through a review of medical records. A manual analysis of the diagnostic accuracy and machine learning was performed. For machine learning, the datasets were preprocessed in three patterns and assigned to the XGBoost program using five-fold cross-validation. Of all the participants, 437 were labeled as having been diagnosed with immunoglobulin A nephropathy. Bill codes for immunoglobulin A nephropathy were provided to approximately half of them. The manually created criteria consisting of the recommended examinations and treatments in the Japanese guidelines for immunoglobulin A nephropathy showed both specificity and sensitivity < 0.8. In contrast, with the receiver operating characteristic curve analysis, the machine learning process yielded area under the curve values over 0.9 with preprocessing from the clinical viewpoint. Applying machine learning technology to a dataset preprocessed from a clinical viewpoint achieved a high performance in detecting patients with immunoglobulin A nephropathy. This methodology contributes to the construction of a disease-specific cohort using big bill data. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Usefulness of an interprofessional work manual for perioperative glucose control of an artificial pancreas.

Author

Mibu, Kiyo, Kitagawa, Hiroyuki, Namikawa, Tsutomu, Maeda, Hiromichi, Fujisawa, Kazune, Munekage, Masaya, Yamasaki, Fumiyasu, Yamamoto, Nao, Seo, Satoru, and Hanazaki, Kazuhiro

Subjects
*ARTIFICIAL pancreases, *MANUAL labor, *BLOOD sugar, *BLOOD sampling, *GLUCOSE
Abstract

Background: A closed‐loop bedside‐type artificial pancreas for perioperative glucose control has previously been introduced. However, artificial pancreas therapy was often interrupted due to continuous blood sampling failure. We developed an interprofessional work manual to reduce the interruption time of artificial pancreatic therapy for perioperative blood glucose control due to continuous blood sampling failure. This study aimed to investigate the usefulness of this manual. Methods: The manual consisted of the following sections: (1) the roles of the professionals in the preparation and management of the artificial pancreas, (2) how to address continuous blood sampling failure, and (3) checkpoints for interprofessional transfer of the artificial pancreas. We compared the results before the introduction of the manual and 2 years after the introduction of the manual. Results: There were 35 and 37 patients in the Before and After groups, respectively. There were no significant differences in patient backgrounds between the two groups, although there was significantly less blood loss in the After group (1164 vs. 366 mL; p < 0.001). The mean artificial pancreas therapy and artificial pancreas therapy interruption times were 847 min and 20 min, respectively. Artificial pancreas therapy interruption time (34 vs. 8 min; p = 0.078) and time per interruption (24 vs. 4 min; p < 0.001) were significantly shorter in the After group than in the Before group. Conclusions: The interprofessional working manual was useful in reducing the artificial pancreatic therapy interruption time for perioperative glucose control. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Ultralow‐Temperature Sintering of Titanium Powder by Spark Plasma Sintering Under Cyclic Pressure.

Author

Manabe, Nao, Suzuki, Ayako S., Ninagawa, Mei, Wakabayashi, Hideki, Hirayama, Naomi, Niinobe, Kouichi, Tang, Yuanbo T., Utada, Satoshi, McCartney, David Graham, Reed, Roger C., and Kitagawa, Hiroyuki

Subjects
BEND testing, TENSILE tests, TENSILE strength, HIGH temperatures, SINTERING, TITANIUM powder
Abstract

Spark plasma sintering (SPS) is a promising method for producing titanium components from powder but a limitation is that high sintering temperatures (>900 °C) are normally required to eliminate porosity. Herein, the SPS of commercially pure titanium powder is reported using both cyclic and constant uniaxial pressure and compare densification, microstructure, and mechanical behavior. The following parameters are varied: sintering temperature, TS, 400 to 900 °C; cyclic and constant pressures, 100 to 500 MPa; with and without an isothermal dwell of 60 min at TS. The mechanical behavior is determined by bend and tensile testing. It is demonstrated that the application of cyclic pressure (cyclic‐SPS) gives superior densification over the range of parameters investigated compared with a constant pressure. Bend testing reveals improved ductility after cyclic‐SPS compared with a constant pressure. The dwell at TS further improves mechanical properties, giving excellent tensile ductility and strength. Consequently, at the ultralow temperature of 500 °C, nearly fully dense, ductile, titanium is achieved. It is shown that cyclic pressure enhances the degree of powder compaction at room temperature, and mechanisms are proposed to rationalize the effect of cyclic‐SPS on enhancing the rates of densification and sintering as the temperature increases during processing. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Unifying Spatial Keyword Indexing in Continuous Search on Dynamic Objects

Author

Kato, Yasuyuki, Chen, Hanxiong, Furuse, Kazutaka, Kitagawa, Hiroyuki, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2021
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34. Aggregate Nearest Neighborhood Queries

Author

Takagi, Hayata, Chen, Hanxiong, Furuse, Kazutaka, Kitagawa, Hiroyuki, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2021
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35. Augmented Lineage: Traceability of Data Analysis Including Complex UDFs

Author

Yamada, Masaya, Kitagawa, Hiroyuki, Amagasa, Toshiyuki, Matono, Akiyoshi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2021
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36. VOA*: Fast Angle-Based Outlier Detection over High-Dimensional Data Streams

Author

Khalique, Vijdan, Kitagawa, Hiroyuki, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlapalem, Kamal, editor, Cheng, Hong, editor, Ramakrishnan, Naren, editor, Agrawal, R. K., editor, Reddy, P. Krishna, editor, Srivastava, Jaideep, editor, and Chakraborty, Tanmoy, editor

Published
2021
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40. 胃癌術後における血清亜鉛値の評価

Author

Namikawa, Tsutomu, primary, Tanaka, Tomoki, additional, Utsuomiya, Masato, additional, Yokota, Keiichiro, additional, Kawanishi, Yasuhiro, additional, Fujisawa, Kazune, additional, Munekage, Masaya, additional, Maeda, Hiromichi, additional, Kitagawa, Hiroyuki, additional, Kobayashi, Michiya, additional, Hanazaki, Kazuhiro, additional, and Seo, Satoru, additional

Published
2024
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41. Fast and Accurate Community Search Algorithm for Attributed Graphs

Author

Matsugu, Shohei, Shiokawa, Hiroaki, Kitagawa, Hiroyuki, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hartmann, Sven, editor, Küng, Josef, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2020
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42. Accelerating All 5-Vertex Subgraphs Counting Using GPUs

Author

Suganami, Shuya, Amagasa, Toshiyuki, Kitagawa, Hiroyuki, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hartmann, Sven, editor, Küng, Josef, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2020
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43. Correction to: Integration of Knowledge Bases and External Information Sources via Magic Properties and Query-Driven Entity Linking

Author

Ohmori, Yuuki, Kitagawa, Hiroyuki, Amagasa, Toshiyuki, Matono, Akiyoshi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Delir Haghighi, Pari, editor, Pardede, Eric, editor, Dobbie, Gillian, editor, Yogarajan, Vithya, editor, ER, Ngurah Agus Sanjaya, editor, Kotsis, Gabriele, editor, and Khalil, Ismail, editor

Published
2023
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46. Group Rotation Type Crowdsourcing

Author

Kumai, Katsumi, Shiraishi, Yuhki, Zhang, Jianwei, Kitagawa, Hiroyuki, and Morishima, Atsuyuki

Subjects
Computer Science - Databases
Abstract

A common workflow to perform a continuous human task stream is to divide workers into groups, have one group perform the newly-arrived task, and rotate the groups. We call this type of workflow the group rotation. This paper addresses the problem of how to manage Group Rotation Type Crowdsourcing, the group rotation in a crowdsourcing setting. In the group-rotation type crowdsourcing, we must change the group structure dynamically because workers come in and leave frequently. This paper proposes an approach to explore a design space of methods for group restructuring in the group rotation type crowdsourcing., Comment: 2 non-reference pages + reference-only page, HCOMP2016, WiP paper

Published
2016

47. The spatio-temporal structure of the Lateglacial to early Holocene transition reconstructed from the pollen record of Lake Suigetsu and its precise correlation with other key global archives: Implications for palaeoclimatology and archaeology

Author

Nakagawa, Takeshi, Tarasov, Pavel, Staff, Richard, Bronk Ramsey, Christopher, Marshall, Michael, Schlolaut, Gordon, Bryant, Charlotte, Brauer, Achim, Lamb, Henry, Haraguchi, Tsuyoshi, Gotanda, Katsuya, Kitaba, Ikuko, Kitagawa, Hiroyuki, van der Plicht, Johannes, Yonenobu, Hitoshi, Omori, Takayuki, Yokoyama, Yusuke, Tada, Ryuji, and Yasuda, Yoshinori

Published
2021
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50. O(1)-Time Complexity for Fixed Sliding-Window Aggregation Over Out-of-Order Data Streams

Author

Bou, Savong, Amagasa, Toshiyuki, and Kitagawa, Hiroyuki

Abstract

Sliding-window aggregation is one of the core operations in processing and analyzing data streams, but it seriously suffers from the unordered events or elements from data streams. Unordered streams or out-of-order data streams contain events whose order based on their timestamps (called event time) is different from the order based on their arriving times to the system (called ingestion time). Out-of-order data streams typically occur in a distributed environment due to many factors, such as network disruptions and delays. Out-of-order data streams drastically make the processing speed slower and existing works, that can handle out-of-order streams, do not address this problem well and can be further improved. The time complexities of existing approaches are not efficient because they are dependent on $n$n, which is the number of slides in the window. In addition, they ignore the past windows affected by the late-arrival records. In many applications, updating and reporting the results of the past windows affected by the late-arrival records in real time is strongly needed. This paper proposes two solutions: (1) A Maximum-allowed lateness-based IndeXing algorithm with a Constant time complexity (CMiX) for computing the current window, and (2) A Past Window Indexing algorithm (PWiX) for efficient updating the past windows. Experimental results show that CMiX and PWiX can deal with out-of-order data streams significantly better than other existing approaches. CMiX is about 3.21 times faster than the state-of-the-art approach by significantly using less memory. It is important to emphasize that all approaches mentioned in the paper have the following limitations: (1) Aggregation can be both distributive and algebraic, which must be commutative due to the out-of-order of data streams, and (2) The window and slide sizes are assumed to be fixed, and if they are changed, the indices must be reconstructed.

Published
2024
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