Doctor of engineering, The University of Tokyo

LifeLog Mining

LigeLog

Data Analysis

Distributed Parallel Processing

Data Engineering

Data mining

データ分析

データ活用

並列分散処理

データマイニング

データ工学

Informatics, Intelligent informatics

Informatics, Database science

03 Apr. 2000 - 31 Dec. 2010
Hitachi Ltd., Researcher, Senior Resercher

01 Apr. 1999 - 31 Mar. 2000
Industrial Institute of Science, The University of Tokyo, Post Doctral Research Fellow

01 Apr. 1999 - 31 Mar. 2000
日本学術振興会, 特別研究員(PD)

01 Apr. 1998 - 31 Mar. 1999
日本学術振興会, 特別研究員(DC2)

Apr. 1996 - Mar. 1999
The University of Tokyo, Graduate School, Division of Engineering, Department of Information Engineering

31 Mar. 1989
東京都立三鷹高等学校, 普通科

Jun. 2024 - Present
副委員長, 電子情報通信学会和文論文誌D編集委員会, Society

Jun. 2022 - May 2024
幹事, 電子情報通信学会和文論文誌D編集委員会, Society

Apr. 2020 - Mar. 2024
専門委員, 情報処理学会データベースシステム研究運営委員会, Society

Mar. 2022 - Feb. 2023
幹事, 電子情報通信学会和文論文誌Dデータ工学と情報マネジメント特集編集委員会, Society

Jun. 2019 - May 2022
編集委員, 電子情報通信学会和文論文誌D編集委員会, Society

01 Apr. 2017 - 31 Mar. 2020
幹事, 情報処理学会データベースシステム研究運営委員会, Society

01 Apr. 2015 - 31 Mar. 2019
SWG委員, 情報処理学会会誌編集委員会, Society

01 Jun. 2016
専門委員, 電子情報通信学会データ工学研究専門委員会, Society

01 Jun. 2014 - 31 May 2016
幹事, 電子情報通信学会データ工学研究専門委員会, Society

01 Jun. 2013 - 31 May 2014
幹事補佐, 電子情報通信学会データ工学研究専門委員会, Society

Mar. 2024
階層的に細分化したマルチレベルイベントタイプからなる長時間マルチレベルエピソード抽出手法
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)優秀プレゼンテーション賞(指導学生),

Mar. 2015
スキップ探索を用いた不確実データからの頻出パターンの抽出
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)優秀プレゼンテーション賞(指導学生)
Japan society

Mar. 2014
マルチ最小サポートを用いて継続時間と時間間隔を考慮した時系列パターンマイニングアルゴリズム
第6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 2014)優秀プレゼンテーション賞(指導学生)
Japan society

Mar. 1997
情報処理学会
情報処理学会第53回全国大会大会奨励賞

平面図と断面図による軌跡可視化のためのLine Simplification手法
村上司; 藤田秀之; 大森匡; 新谷隆彦
電子情報通信学会和文論文誌D分冊, Vol.J107-D, No.5, 290-299, May 2024, Peer-reviwed
Scientific journal, Japanese

ネットワーク可視化における拡大描画に適したエッジバンドリング手法
秋山桂一; 藤田秀之; 大森匡; 新谷隆彦
情報処理学会論文誌, 秋山桂一, 藤田秀之, 大森匡, 新谷隆彦 ネットワーク可視化における拡大描画に適したエッジバンドリング手法 情報処理学会論文誌 Vol.63, No.3 pp.1-14 2022, 63, 3, 817-830, 01 Mar. 2022, Peer-reviwed
Scientific journal, Japanese

Comparison Method of Long-term Daily Life Considering the Manner of Spending a Day
Takahiko Shintani; Tadashi Ohmori; Hideyuki Fujita
Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management(IC3K 2019), SciTePress, Vol.1, KDIR, 347-354, 18 Sep. 2019, Peer-reviwed
International conference proceedings, English

Method for Comparing Long-term Daily Life using Long-duration Episodes
Takahiko Shintani; Tadashi Ohmori; Hideyuki Fujita
Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference, CEUR, 2322, Mar. 2019, Peer-reviwed
International conference proceedings, English

Route Network Construction with Location-Direction-Enabled Photographs
H.Fujita; Shota Sagara; T.Ohmori; T.Shintani
Proc. 28th Int. Cartographic Conference (ICC) 2017, 40, 1-3, Jul. 2017, Peer-reviwed
International conference proceedings, English

スキップ探索を用いた不確実データからの頻出パターンの抽出
建島広翔; 新谷隆彦; 大森匡; 藤田秀之
日本データベース学会和文論文誌, 14, 6, Mar. 2016, Peer-reviwed
Scientific journal, Japanese

Pairwise expansion: A new topdown search for mCK queries problem over spatial web
Yuan Qiu; Tadashi Ohmori; Takahiko Shintani; Hideyuki Fujita
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 9932, 459-463, 2016, Peer-reviwed, This paper focuses on the problem of m-Closest Keywords (mCK) queries over spatial web objects. An mCK query is to find the optimal set of objects (object-set) in the sense that they are the spatiallyclosest records and satisfy m user-given keywords. We propose a new approach called Pairwise Expansion to find an exact solution of mCK queries based on topdown search of an on-the-fly quad-tree. This approach first enumerates object-pairs in a topdown way, then picks up each ‘closer’ object-pair and expands it into candidate object-sets. Experimental results show that this approach is more efficient than existing topdown search strategies and applicable for real spatial web data.
International conference proceedings, English

Skip Search Approach for Mining Probabilistic Frequent Itemsets from Uncertain Data
Takahiko Shintani; Tadashi Ohmori; Hideyuki Fujita
KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1, SCITEPRESS, Vol.1, KDIR, 174-180, 2016, Peer-reviwed, Due to wider applications of data mining, data uncertainty came to be considered. In this paper, we study mining probabilistic frequent itemsets from uncertain data under the Possible World Semantics. For each tuple has existential probability in probabilistic data, the support of an itemset is a probability mass function (pmf). In this paper, we propose skip search approach to reduce evaluating support pmf for redundant itemsets. Our skip search approach starts evaluating support pmf from the average length of candidate itemsets. When an evaluated itemset is not probabilistic frequent, all its superset of itemsets are deleted from candidate itemsets and its subset of itemset is selected as a candidate itemset to evaluate next. When an evaluated itemset is probabilistic frequent, its superset of itemset is selected as a candidate itemset to evaluate next. Furthermore, our approach evaluates the support pmf by difference calculus using evaluated itemsets. Thus, our approach can reduce the number of candidate itemsets to evaluate their support pmf and the cost of evaluating support pmf. Finally, we show the effectiveness of our approach through experiments.
International conference proceedings, English

A New Algorithm for m-Closest Keywords Query over Spatial Web with Grid Partitioning
Yuan Qiu; Tadashi Ohmori; Takahiko Shintani; Hideyuki Fujita
2015 16TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), IEEE, 507-514, 2015, Peer-reviwed, In this paper, we focus on the issue of the m-closest keywords (mCK) query over spatial data in the Web. The mCK query is a problem to find the optimal set of records in the sense that they are the spatially-closest records that satisfy m user-given keywords. The mCK query was proposed by Zhang et al[1]. They assumed a specialized R*-tree to store all records and proposed an Apriori-based enumeration of MBR-combinations. However, this assumption of the prepared R*tree is not always applicable; Twitter or Flickr provides only records having position information without any prepared data-partitioning. Many services like Google Maps only provide grid partitioning at most. Thus, in this paper, we do not expect any prepared data-partitioning, but assume that we create a grid partitioning from necessary data only when an mCK query is given. Under this assumption, we propose a new search-strategy termed Diameter Candidate Check (DCC), and show that DCC can efficiently find a better set of grid-cells at an earlier stage of search, thereby reducing search space greatly.
International conference proceedings, English

Map/Reduce におけるバケット再グループ化 を用いたハイブリッドハッシュ結合アルゴリズム
廣瀬繁雄; 大森匡; 新谷隆彦
日本データベース学会論文誌, 日本データベース学会, 12, 1, 61-66, Jun. 2013, Peer-reviwed
Scientific journal, Japanese

Mining association rules from data with missing values by database partitioning and merging
Takahiko Shintani
Proceedings - 5th IEEE/ACIS Int. Conf. on Comput. and Info. Sci., ICIS 2006. In conjunction with 1st IEEE/ACIS, Int. Workshop Component-Based Software Eng., Softw. Archi. and Reuse, COMSAR 2006, 2006, 193-200, 2006, Peer-reviwed, Often, real world applications contain many missing values. In mining association rules from real datasets, treating missing values is an important problem. In this paper, we propose a pattern-growth based algorithm for mining association rules from data with missing values. No data imputations are performed. Each association rule is evaluated using all the data records with which attributes of it are not missing values. Our algorithm partitions the database so that the data record with which the same attributes contain missing values is assigned to the same database partition, and the algorithm mines association rules by combining these database partitions. We propose methods of reducing processing workload: estimating the upper bound of global support using local supports, reutilizing part of the constructed tree structure, and merging redundant database partitions. Our performance study shows that our algorithm is efficient and can always find all association rules. © 2006 IEEE.
International conference proceedings, English

臨床研究を支援する症例検索システムの開発
瀬戸久美子; 新谷隆彦; 斎藤聡; 藤尾正和; 光山訓; 今井靖; 林同文; 永井良三
日本医療情報学会医療情報学, 25, 2, 99-105, 2005, Peer-reviwed
Scientific journal, Japanese

個人健康管理システムに基づく健康データと生活データの相関関係（I）
竹内裕之; 橋口猛志; 新谷隆彦
高崎健康福祉大学紀要, Takasaki University of Health and Welfare, 4, 11-21, 2005
Research institution, Japanese

Personal Dynamic Healthcare System Utilizing Mobile Phone and Web Technologies
Hiroyuki Takeuchi; Takeshi Hashiguchi; Takahiko Shintani
Proc. of Int'l Conf. on Advances in Biomedical Signal and Information Processing, 304-307, Sep. 2004, Peer-reviwed
International conference proceedings, English

Mining association rules with negative terms using candidate pruning
T Shintani; D Hayashi
DATA MINING V, WIT PRESS, 10, 157-166, 2004, Peer-reviwed, In this paper, we discuss an association rule with negative terms that contains negative and affirmative conditions intermingled, such as "80% of customers who buy A and B but do not buy X, also buy C and D". An association rule with negative terms can provide higher confidence rules, that is, we can attain more valuable information. To find them, itemsets containing negative conditions must be checked. We proposed two candidate pruning methods, upper bound pruning and database partition pruning, which are suitable for handling these itemsets. Upper bound pruning detects itemsets that cannot generate rules satisfying userspecified minimum thresholds. Database partition pruning detects itemsets that do not appear in database. Through performance evaluations, we show that the proposed methods not only reduce candidate itemsets but also avoid finding useless frequent itemsets for rule derivation. Moreover, we show an example of rules obtained by applying the proposed methods to a real dataset that is the hospitalization data of the cardiovascular medicine of the University of Tokyo hospital.
International conference proceedings, English

日常の健康管理を目的とした個人対応動的データベース
竹内裕之; 橋口猛志; 新谷隆彦
医療情報学, 23, 4, 467-502, 2004, Peer-reviwed
Scientific journal, Japanese

携帯電話を活用した健康管理システム
竹内裕之; 橋口猛志; 新谷隆彦
高崎健康福祉大学紀要, 3, 1-8, 2004
Research institution, Japanese

A common Ile 823 Met variant of ATP-binding cassette transporter A1 gene (ABCA1) alters high density lipoprotein cholesterol level in Japanese population
T Harada; Y Imai; T Nojiri; H Morita; D Hayashi; K Maemura; K Fukino; D Kawanami; G Nishimura; K Tsushima; K Monzen; T Yamazaki; S Mitsuyama; T Shintani; N Watanabe; K Seto; T Sugiyama; F Nakamura; M Ohno; Y Hirata; T Yamazaki; R Nagai
ATHEROSCLEROSIS, ELSEVIER SCI IRELAND LTD, 169, 1, 105-112, Jul. 2003, Peer-reviwed, Recently, variants in ATP-binding cassette transporter Al (ABCA1) were demonstrated to be associated with increased level of high density lipoprotein cholesterol (HDL-C) and decreased risk of coronary artery disease (CAD) in Caucasians. However, this is not universally applicable due to the ethnic or environmental differences. In this context, to clarify the effect of ABCAI in Japanese, we evaluated the phenotypic effects of I/M 823 and R/K 219 variants on the plasma level of HDL-C in 410 patients recruited in our hospital. Subjects with M 823 allele had significantly higher level of HDL-C than those without M823 allele (49.0 +/- 15.1 vs. 44.9 +/- 11.5 mg/dl, respectively, P < 0.05). This statistical significance did not change even after multiple regression analysis. In contrast, there was no difference in HDL-C level among the genotypes in R/K 219 polymorphism. Further, in our study population an inverse relationship was shown to exist between HDL-C level and incidence of CAD. However, no positive association was observed between those variants and susceptibility to CAD. In this study, we provide evidence that I/M 823 variant, not R/K 219 variant, in ABCA1 is one of the determinants of HDL-C level, suggesting the importance of this gene on lipid metabolism in Japanese. (C) 2003 Elsevier Science Ireland Ltd. All rights reserved.
Scientific journal, English

医療安全に資する診療情報の体系化と先端情報処理技術の適用
渡部生聖; 林同文; 今井靖; 光山訓; 瀬戸久美子; 新谷隆彦; 橋口猛志; 野口清輝; 真鍋一郎; 戸辺一之; 山崎力; 永井良三
社会技術研究会社会技術研究論文集, Sociotechnology Research Network, 1, 0, 383-390, 2003, Our group of medical safety research has been studying to establish generally usable method for extracting and sharing knowledge of medical support by application of information technology (IT). We try extracting the knowledge of medical support from a great amount of various medical information arising in daily medical care. Using the actual clinical information that is collected and managed by ethically appropriate method, our group has obtained the useful knowledge of medical support by joint study of experts in medicine and engineering. We report the resulting knowledge of medical support and technical method for popularization.
Research society, Japanese

Parallel generalized association rule mining on large scale PC cluster
Takahiko Shintani; Masaru Kitsuregawa
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 1759, 145-160, 2002, One of the most important problems in data mining is dis- covery of association rules in large database. In our previous study, we proposed parallel algorithms and candidate duplication based load bal- ancing strategies for mining generalized association rules and showed our algorithms could attain good performance on 16 nodes parallel computer system. However, as the number of nodes increase, it would be difficult to achieve flat workload distribution. In this paper, we present the candidate partition based load balancing strategy for parallel algorithm of generalized association rule mining. This strategy partitions the candidate itemsets so that the number of candidate probes for each node is equalized each other with estimated support count by the information of previous pass. Moreover, we imple- ment the parallel algorithms and load balancing strategies for mining generalized association rules on a cluster of 100 PCs interconnected with an ATM network, and analyze the performance using a large amount of transaction dataset. Through the several experiments, we showed the load balancing strategy, which partition the candidate itemsets with con- sidering the distribution of candidate probes and duplicate the frequently occurring candidate itemsets, can attain high performance and achieve good workload distribution on one hundred PC cluster system.
International conference proceedings, English

User Behavior Analysis of Location Aware Search Engine.
Iko Pramudiono; Takahiko Shintani; Katsumi Takahashi; Masaru Kitsuregawa
Proceedings of the Third International Conference on Mobile Data Management (MDM 2002), IEEE Computer Society, 139-145, 2002
International conference proceedings

Parallel data mining on large scale PC cluster
Masaru Kitsuregawa; Takahiko Shintani; Masahisa Tamura; Iko Pramudiono
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 1846, 15-26, 2000, PC cluster is recently regarded as one of the most promising platforms for heavy data intensive applications, such as decision support query processing and data mining. We proposed some new parallel algorithms to mine association rule and generalized association rule with taxonomy and showed that PC cluster can handle large scale mining with them. During development of high performance parallel mining system on PC cluster, we found that heterogeneity is inevitable to take the advantage of rapid progress of PC hardware. However we can not naively apply existing parallel algorithms since they assume homogeneity. We proposed the new dynamic load balancing methods for association rule mining, which works under heterogeneous system. Two strategies, called candidate migration and transaction migration are proposed. Initially first one is invoked. When the load imbalance cannot be resolved with the first method, the second one is employed, which is costly but more effective for strong imbalance. The experimental results confirm that the proposed approach can very effectively balance the workload among heterogeneous PCs.
International conference proceedings, English

Web log mining and parallel SQL based execution
Masaru Kitsuregawa; Takahiko Shintani; Takeshi Yoshizawa; Iko Pramudiono
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 1966, 20-32, 2000, We performed association rule mining and sequence pattern mining against the access log which was accumulated at NTT Software Mobile Info Search portal site. Detail web log mining process and the rules we derived are reported in this paper. The integration of web data and relational database enables better management of web data. Some researches have even tried to implement applications such as web mining with SQL. Commercial RDBMSs support parallel execution of SQL. Parallelism is key to improve the performance. We showed that commercial RDBMS can achieve substantial speed up for web mining.
International conference proceedings, English

Implementation of parallel data mining on an ATM-connected PC cluster and performance analysis of TCP retransmission mechanisms
M Oguchi; T Tamura; T Shintani; M Kitsuregawa
ELECTRONICS AND COMMUNICATIONS IN JAPAN PART I-COMMUNICATIONS, SCRIPTA TECHNICA-JOHN WILEY & SONS, 82, 7, 12-24, Jul. 1999, Peer-reviwed, A recent tendency in parallel computer design has been to use general-purpose components for system configuration elements such as CPUs, disks, and memories, which used to be specially developed. Although the connection network between the processors has been specially developed, it is now possible to configure a large-scale PC cluster with good performance at low cost by making use of an ATM network as a processor connection network because of the development and cost reduction of ATM network technologies in the communication field. In this paper, a large-scale PC cluster is constructed by connecting 100 personal computers by means of a general-purpose ATM network. Applications to parallel data mining are evaluated and discussed. In particular, an analysis is carried out with a focus on the effect of TCP retransmission with cell discarding of the ATM switch on the performance. The parameter setting of a retransmission mechanism suitable for the parallel processing in the cluster is found. Further, by developing a method for setting the retransmission spacing parameters to random values for each node, it is shown that a further improvement is possible. (C) 1999 Scripta Technica.
Scientific journal, English

Parallel SQL based association rule mining on large scale PC cluster: Performance comparison with directly coded C implementation
Iko Pramudiono; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 1574, 94-99, 1999, Data mining is becoming increasingly important since the size of databases grows even larger and the need to explore hidden rules from the databases becomes widely recognized. Currently database systems are dominated by relational database and the ability to perform data mining using standard SQL queries will definitely ease implementation of data mining. However the performance of SQL based data mining is known to fall behind specialized implementation. In this paper we present an evaluation of parallel SQL based data mining on large scale PC cluster. The performance achieved by parallelizing SQL query for mining association rule using 4 processing nodes is even with C based program.
International conference proceedings, English

Performance analysis for parallel generalized association rule mining on a large scale PC cluster
T Shintani; M Oguchi; M Kitsuregawa
EURO-PAR'99: PARALLEL PROCESSING, SPRINGER-VERLAG BERLIN, 1685, 1455-1459, 1999, Peer-reviwed, One of the most important problems in data mining is discovery of association rules in large database. We had proposed parallel algorithms for mining generalized association rules with classification hierarchy. In this paper, we implemented the proposed algorithms on a large scale PC cluster which consists of one hundred PCs interconnected by an ATM switch, and analyzed the performance of our algorithms using a large amount of transaction dataset. Performance evaluations show our parallel algorithms are effective for handling skew for such large scale parallel systems.
Scientific journal, English

Mining generalized association rule parallel RDB engine on PC cluster
Iko Pramudiono; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 1676, 281-292, 1999, Data mining has been widely recognized as a powerful tool to explore added value from large-scale databases. One of data mining techniques, generalized association rule mining with taxonomy, is potential to discover more useful knowledge than ordinary at association mining by taking application specific information into account. We proposed SQL queries, named TTR-SQL and TH-SQL to perform this kind of mining and evaluated them on PC cluster. Those queries can be more than 30% faster than Apriori based SQL query reported previously. Although RDBMS has powerful query processing ability through SQL, most data mining systems use specialized implementations to achieve better performance. There is a tradeoff between performance and portability. Performance is not necessarily sufficiently high but seamless integration with existing RDBMS would be considerably advantageous. Since RDB is already very popular, the feasibility of generalized association rule mining can be explored using the proposed SQL query instead of purchasing expensive mining software. In addition, parallel RDB is now also widely accepted. We showed that paralleling the SQL execution can offer the same performance with those native programs with 10 to 15 nodes. Since most organizations have a lot of PCs, which are not fully utilized. We are able to exploit such resources to explore the performance significantly.
International conference proceedings, English

Parallel Data Mining on a Large Scale ATM Connected PC Cluster and Performance Analysis of TCP Retransmission Mechanisms
Masato Oguchi; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Proc. of Enterprise Networking and Computing Conf., 4-D, Jun. 1998, Peer-reviwed
International conference proceedings, English

Mining algorithms for sequential patterns in parallel: Hash based approach
T Shintani; M Kitsuregawa
RESEARCH AND DEVELOPMENT IN KNOWLEDGE DISCOVERY AND DATA MINING, SPRINGER-VERLAG BERLIN, 1394, 283-294, 1998, Peer-reviwed, In this paper, we study the problem of mining sequential patterns in a large database of customer transactions. Since finding sequential patterns has to handle a large amount of customer transaction data and requires multiple passes over the database, it is expected that parallel algorithms help to improve the performance significantly.
We consider the parallel algorithms for mining sequential patterns on a shared-nothing environment. Three parallel algorithms (Non Partitioned Sequential Pattern Mining(NPSPM), Simply Partitioned Sequential Pattern Mining(SPSPM) and Hash Partitioned Sequential Pattern Mining(HPSPM)) are proposed. In NPSPM, the candidate sequences are just copied among all the nodes, which can lead to memory overflow for large databases. The remaining two algorithms partition the candidate sequences over the nodes, which can efficiently exploit the total system's memory as the number of nodes in increased. If it is partitioned simply, customer transaction data has to be broadcasted to all nodes. HPSPM partitions the candidate sequences among the nodes using hash function, which eliminates the customer transaction data broadcasting and reduces the comparison workload. We describe the implementation of these algorithms on a shared-nothing parallel computer IBM SP2 and its performance evaluation results. Among three algorithms HPSPM attains best performance.
Scientific journal, English

Parallel mining algorithms for generalized association rules with classification hierarchy
Takahiko Shintani; Masaru Kitsuregawa
SIGMOD Record, Association for Computing Machinery, 27, 2, 25-36, 1998, Peer-reviwed, Association rule mining recently attracted strong attention. Usually, the classification hierarchy over the data items is available. Users are interested in generalized association rules that span different levels of the hierarchy, since sometimes more interesting rules can be derived by taking the hierarchy into account. In this paper, we propose the new parallel algorithms for mining association rules with classification hierarchy on a shared-nothing parallel machine to improve its performance. Our algorithms partition the candidate itemsets over the processors, which exploits the aggregate memory of the system effectively. If the candidate itemsets are partitioned without considering classification hierarchy, both the items and its all the ancestor items have to be transmitted, that causes prohibitively large amount of communications. Our method minimizes interprocessor communication by considering the hierarchy. Moreover, in our algorithm, the available memory space is fully utilized by identifying the frequently occurring candidate itemsets and copying them over all the processors, through which frequent itemsets can be processed locally without any communication. Thus it can effectively reduce the load skew among the processors. Several experiments are done by changing the granule of copying itemsets, from the whole tree, to the small group of the frequent itemsets along the hierarchy. The coarser the grain, the easier the control but it is rather difficult to achieve the sufficient load balance. The finer the grain, the more complicated the control is required but it can balance the load quite well. We implemented proposed algorithms on IBM SP-2. Performance evaluations show that our algorithms are effective for handling skew and attain sufficient speedup ratio. © 1998 ACM.
Scientific journal, English

ATM結合PCクラスタにおける並列データマイニングの実装とTCP再送機構の性能解析
小口正人; 新谷隆彦; 田村孝之; 喜連川優
電子情報通信学会論文誌B-I, The Institute of Electronics, Information and Communication Engineers, J81-B-1, 8, 461-472, 1998, Peer-reviwed, 並列計算機の最近の傾向として, 従来は専用に開発していたCPUやディスク, メモリ等のシステム構成要素に, 汎用の部品を利用するようになってきたことがあげられる.プロセッサ間結合網だけは従来独自に開発されていたが, 通信分野におけるATMネットワーク技術の発展ならびに低価格化によって, プロセッサ間結合網としてATMを利用することにより, 低いコストで性能の良い大規模PCクラスタを構築することが可能になりつつある.本論文では, 100台のパーソナルコンピュータを汎用のATMネットワークで接続することにより大規模PCクラスタを構築し, 並列データマイニングのアプリケーションを実装して評価, 検討を行った.特にATMスイッチのセル廃棄に伴うTCPの再送による性能への影響に焦点を当てて解析を行うことにより, クラスタにおける並列処理に適した再送機構のパラメータ設定を明らかにした.更に再送間隔のパラメータをノードごとにランダムな値に設定する方法をとることにより, いっそう改善可能であることを明らかにした.
Scientific journal, Japanese

Parallel Association Rule Mining on ATM Connected PC Clusters
OGUCHI Masato; SHINTANI Takahiko; TAMURA Takayuki; KITSUREGAWA Masaru
Proceedings of the IEICE General Conference, The Institute of Electronics, Information and Communication Engineers, 1997, 1, 06 Mar. 1997, ATM結合PCクラスタはコストパフォーマンスの観点からみて今後有望であると考えられるので、PCクラスタ上で並列データマイニングアプリケーションの実装を試みることは非常に有意義である。これまでPCクラスタのプロジェクトがいくつか報告されており、この中で科学技術計算のベンチマークプログラムの実行結果などが示されている。我々は、アドホックな問い合わせ処理やデータマイニングのようにデータベース処理を中心とするビジネス系アプリケーションは、従来の科学技術計算と並び並列処理の非常に重要なアプリケーションであると考える。本稿においては、相関関係 (association rule) 抽出をこのようなデータベース処理に重点の置かれたアプリケーションの例として取り上げ、検討を行う。
Japanese

Characteristics of a parallel data mining application implemented on an ATM connected PC cluster
M Oguchi; T Shintani; T Tamura; M Kitsuregawa
HIGH-PERFORMANCE COMPUTING AND NETWORKING, SPRINGER-VERLAG BERLIN, 1225, 303-317, 1997, Peer-reviwed, Until recently, workstations were overwhelmingly superior to personal computers in terms of performance. However, recent PC technology has dramatically increased its CPU, main memory, and cache memory performance. Therefore massively parallel computer systems are moving away from proprietary components such as CPU, disks, etc, to commodity parts.
As far as applications are concerned, we believe that data intensive applications such as ad-hoc query processing and data mining is very important for parallel processors in addition to the conventional scientific applications. Since ATM connected PC clusters are very promising from the cost/performance point of view, we are examining the feasibility of implementing data mining over PC clusters. In this paper, we report our preliminary experimental results for parallel data mining on 2 suites of ATM connected PC clusters, consisting of 8 PCs. Although there are several kinds of problems such as immaturity of NIC and driver software, we achieved reasonably good performance for parallel data mining.
Scientific journal, English

Hash based parallel algorithms for mining association rules
T Shintani; M Kitsuregawa
PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED INFORMATION SYSTEMS, I E E E, COMPUTER SOC PRESS, 19-30, 1996, Peer-reviwed, In this paper, we propose four parallel algorithms (NPA, SPA, HPA and HPA-ELD) for mining association rules on shared-nothing parallel machines to improve its performance.
In NPA, candidate itemsets are just copied amongst all the processors, which can lead to memory overflow for large transaction databases. The remaining three algorithms partition the candidate itemsets over the processors. If it is partitioned simply (SPA), transaction data has to be broadcast to all processors. HPA partitions the candidate itemsets using a hash function to eliminate broadcasting, which also reduces the comparison workload significantly. HPA-ELD fully utilizes the available memory space by detecting the extremely large itemsets and copying them, which is also very effective at flattering the load over the processors.
We implemented these algorithms in a shared-nothing environment. Performance evaluations shout that the best algorithm, HPA-ELD, attains good linearity on speedup ratio and is effective for handling skew.
International conference proceedings, English

Parallel SQL Based Association Rule Mining on Large Scale PC Cluster: Performance Comparison with Directly Coded C Implementation
Iko Pramudiono; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Proc of Pacific-Asia Conf. on Knowledge Discovery and Data Mining, 94-98, Apr. 1994, Peer-reviwed
International conference proceedings, English

Consideration on candidate pruning method by excluding overlapped intervals of occurrences for mining long-duration episodes in multiple time-ranges
Taichi Matsuzaki; Takahiko Shintani; Tadashi Omori; Hideyuki Fujita
2023, 情報科学技術フォーラム講演論文集, 22nd

Examination for Visualizing Lifestyle Change by the Differences of Long-duration Episodes in Daily Life
中田真侑子; 新谷隆彦; 大森匡; 藤田秀之
2022, 日本感性工学会大会(Web), 24th, 202202232672142684

Method for Reducing Occurrence Creation Processes by Avoiding Redundant Overlaps in Long Duration Episode Mining
橋本一輝; 新谷隆彦; 大森匡; 藤田秀之
2022, 情報科学技術フォーラム講演論文集, 21st, 202202284574581330

Method of pruning candidates by considering amount of appearing time for mining long-duration episodes with biased occurrence time-range
安井壱陽; 新谷隆彦; 大森匡; 藤田秀之
2021, 情報科学技術フォーラム講演論文集, 20th, 202102225488463026

Examination of Classifying the Activity Status Data by Start Time and Duration
柳川凜太朗; 新谷隆彦; 大森匡; 藤田秀之
2021, 情報科学技術フォーラム講演論文集, 20th, 202102261748857588

継続時間を考慮したエピソードマイニングにおける行動時間帯の偏りに関する一考察
安井, 壱陽; 新谷, 隆彦; 大森, 匡; 藤田, 秀之
我々はリストバンド型センサを常時装着することでどの程度の運動状態をいつからいつまで継続したかを表す運動状態データをライフログとして収集している。運動状態データにエピソードマイニングを適用することで、全期間において頻出または長時間行われた行動に相当するエピソードを抽出できる。人の行動では、どの時間帯に行われていたかも重要であり、行動した時間帯の偏りによって生活を特徴づけることが期待できる。本研究では、時間帯を考慮したエピソードを検討する。エピソードが発生した時間帯の偏りと実際の行動との関連を実データを用いた実験により考察する。, 20 Feb. 2020, 第82回全国大会講演論文集, 2020, 1, 375-376, Japanese, 170000183110, AN00349328

Examination of negative terms for long-duration episodes considering ordinal scale of motion status using lifelog of wristband wearable device
野瀬祥吾; 新谷隆彦; 大森匡; 藤田秀之
2020, 情報科学技術フォーラム講演論文集, 19th, 202002272163872131

Consideration of comparison method for life characteristics with physical activity level
新谷隆彦; 中島彩花; 大森匡; 藤田秀之
2018, 日本感性工学会春季大会予稿集(CD-ROM), 13th, 201802280923443768

頻出長時間エピソードを用いた生活比較に関する一考察
中山恭明; 新谷隆彦; 大森匡; 藤田秀之
2018, 情報科学技術フォーラム講演論文集, 17th, 201802213166323724

File Systems and Storage：0. Foreword
新谷, 隆彦; 福田, 茂紀; 宮澤, 慎一
15 Nov. 2017, 情報処理, 58, 12, 1090-1091, Japanese, 170000149047, AN00116625

リストバンド型センサで取得した動作データからの運動状態の分類に対するSAX適用の試み
中島彩花; 新谷隆彦; 大森匡; 藤田秀之
近年，センサ技術の発展と普及により，日常生活の行動をライフログとして収集することが可能になった．報告者は，リストバンド型センサから腕の動きに関するデータを常時収集している．本研究では，腕の動きデータをいつからいつまでどのような運動状態であったかに分類することを目的とする．数値時系列データを記号化する手法の一つであるSymbolic Aggregate Approximation（SAX）を用いることによって腕の動きデータを記号化し，運動状態の分類を試みた．SAXを適用して自身で収集した腕の動きデータを運動状態に分類し、得られた結果を評価する．, 16 Mar. 2017, 情報処理学会全国大会講演論文集, 79th, 1, 501-502, Japanese, 201702278069755358, 170000174497, AN00349328

長時間エピソードマイニングにおけるインスタンス数え上げ処理量低減の検討
FU JinHui; 新谷隆彦; 大森匡; 藤田秀之
2016, 電子情報通信学会大会講演論文集(CD-ROM), 2016, 1349-144X, 201602219839408985

アイテムシーケンスデータからの頻出否定シーケンシャルパターン抽出方式の検討
SU Liyan; 新谷隆彦; 大森匡; 藤田秀之
2016, 情報科学技術フォーラム講演論文集, 15th, 201602220847667622

D-045 Effects of two-level hash-partitioning for edit-distance join on mapreduce
OHMORI Tadashi; KONNO Atsuhito; SHINTANI Takahiko
Forum on Information Technology, 24 Aug. 2015, 情報科学技術フォーラム講演論文集, 14, 2, 183-186, Japanese, 110009988470, AA1242354X

D-029 Storyline Creation Method for Data storytelling using Location-Direction-enabled Photographs
Sagara Shota; Fujita Hideyuki; Ohmori Tadashi; Shintani Takahiko
Forum on Information Technology, 24 Aug. 2015, 情報科学技術フォーラム講演論文集, 14, 2, 135-138, Japanese, 110009988454, AA1242354X

D-030 Optimization of mCK Search by using Recursive DCC Strategy
Qiu Yuan; Ohmori Tadashi; Shintani Takahiko; Fujita Hideyuki
Forum on Information Technology, 24 Aug. 2015, 情報科学技術フォーラム講演論文集, 14, 2, 139-142, Japanese, 110009988455, AA1242354X

D-028 データ取得制限のある Deep Web からのサンプルデータ収集方式(D分野:データベース,一般論文)
杜 翔; 大森 匡; 藤田 秀之; 邱 原; 新谷 隆彦
FIT(電子情報通信学会・情報処理学会)運営委員会, 24 Aug. 2015, 情報科学技術フォーラム講演論文集, 14, 2, 131-133, Japanese, 110009988453, AA1242354X

アイテムセットと時系列パターンの出現順序を考慮した分類パターンによる分類モデルの精度向上に関する一考察
小柳, 暁奨; 新谷, 隆彦; 大森, 匡; 藤田, 秀之
今日の技術進歩によって多種類のデータが取得されるようになると共に，多種類のデータを組み合わせた分類パターンマイニングの研究が行われてきた．従来研究では，分類パターンを抽出する際に，データ間の出現順序を考慮せずに多種データのパターンを組み合わせていた．しかし，データにタイムスタンプが含まれている場合，データ間の出現順序を考慮して分類パターンを抽出することが可能である．本研究では，時系列パターンとアイテムセットの出現順序を考慮して分類パターンを抽出し，これらのパターンを用いて分類モデルを構築する．そして，実際のデータを用いた実験により構築した分類モデルを評価する．, 17 Mar. 2015, 第77回全国大会講演論文集, 2015, 1, 671-672, Japanese, 170000164708, AN00349328

時系列パターンとアイテムセットの出現順序を考慮した分類パターンによる分類モデルの精度向上に関する一考察
小柳暁奨; 新谷隆彦; 大森匡; 藤田秀之
2015, 情報処理学会全国大会講演論文集, 77th, 1, 201502217563879163

リストバンド型センサで取得した腕の向きのパターンによる運動状態分類の検討
YAN Dei; 新谷隆彦; 大森匡; 藤田秀之
2015, 情報処理学会全国大会講演論文集, 77th, 1, 201502283966869110

リストバンド型センサで取得した運動データを用いた生活比較による生活の変化検出の検討
後藤佑一郎; 新谷隆彦; 大森匡; 藤田秀之
2015, 情報処理学会研究報告(Web), 2015, UBI-48, 201602216796239557

D-4-8 A Study of Candidate Reduction Method for Mining Frequent Patterns from Uncertain Data
Tateshima Hiroto; Shintani Takahiko; Oomori Tadashi; Hujita Hideyuki
The Institute of Electronics, Information and Communication Engineers, 24 Feb. 2015, Proceedings of the IEICE General Conference, 2015, 1, 39-39, Japanese, 1349-144X, 201502221149252911, 110009944824, AN10471452

D-4-9 A proposal of episode mining with duration as threshold
Sakurada Shigehiro; Shintani Takahiko; Ohmori Tadashi; Fujita Hideyuki
The Institute of Electronics, Information and Communication Engineers, 24 Feb. 2015, Proceedings of the IEICE General Conference, 2015, 1, 40-40, Japanese, 1349-144X, 201502279025335000, 110009944825, AN10471452

D-005 Evaluation of m-CK search algorithm DCC on web spatial data
Qiu Yuan; Ohmori Tadashi; Shintani Takahiko; Fujita Hideyuki
Forum on Information Technology, 19 Aug. 2014, 情報科学技術フォーラム講演論文集, 13, 2, 79-82, Japanese, 110009904863, AA1242354X

Techonogies for LifeLog Utilization
Takahiko Shintani
日本知能情報ファジィ学会, Apr. 2014, Japan Society for Fuzzy Theory and Intelligent Informatics, 26, 2, 51-56, Japanese, Invited, Introduction scientific journal, 1347-7986, 40020075144, AA1181479X

Flickrデータを用いたm-最近傍キーワード検索の評価
ほあんあいんだん; 邱原; 大森匡; 藤田秀之; 新谷隆彦
M-closest keyword search (m-CK search) is a search of spatial objects for the spatially closest set of objects which match m query keywords.We implemented a map based search interface for geotagged photographs of Flickr, and evaluated the quality and practicality of our m-CK search algorithm., 11 Mar. 2014, 第76回全国大会講演論文集, 2014, 1, 531-532, English, 170000085601, AN00349328

空間データベースにおけるm-最近接キーワード検索の一方式
邱原; 大森匡; 新谷隆彦; 藤田秀之
本稿では空間データにおけるmCK検索問題を扱う．先行研究ではbR-treeというR木の変種でデータを与えることを前提にAprioriで領域セル組合せ列挙する．これに対して，本稿では，gridデータ構造から開始してキーワード単位で領域セルを組み合わせて列挙する枠組みにおいて探索戦略を工夫した方式を述べる．, 11 Mar. 2014, 第76回全国大会講演論文集, 2014, 1, 529-530, Japanese, 170000085600, AN00349328

Large-Scale Parallel Data Mining
Takahiko Shintani; Masaru Kitsuregawa
English, Joint work, Section 2. Parallel Generalized Association Rule Mining on Large Scale PC Cluster, Springer, 2000

階層的に細分化したマルチレベルイベントタイプからなる長時間マルチレベルエピソード抽出手法
橋本一輝; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 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(DEIM2024)
Mar. 2024

オカレンスの非重複を考慮した探索候補削減による複数の時間帯に注目した長時間エピソード抽出手法
松崎太一; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, 第22回情報科学技術フォーラム（FIT2023）
07 Sep. 2023

長時間エピソードマイニングにおける冗長な重複の回避によるオカレンス作成処理の削減手法
橋本一輝; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第21回情報科学技術フォーラム, Domestic conference
15 Sep. 2022

生活を構成する長時間エピソードの違いに着目した生活推移の提示の検討
中田真侑子; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第24回日本感性工学会大会, Domestic conference
31 Aug. 2022

行動時間帯に偏りのある長時間エピソード抽出における探索候補枝刈り手法
安井壱陽; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第14回データ工学と情報マネジメントに関するフォーラム, Domestic conference
28 Feb. 2022

リストバンド型センサで取得した運動状態データの開始時刻と継続時間を考慮した細分化の試み
柳川凛太朗; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第20回情報科学技術フォーラム, Domestic conference
27 Aug. 2021

行動時間帯に偏りのある長時間エピソード抽出における発生区間の範囲による探索候補の枝刈りの提案
安井壱陽; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第20回情報科学技術フォーラム, Domestic conference
25 Aug. 2021

リストバンド型センサで取得したライフログを用いた長時間エピソードに対する運動状態の順序尺度を考慮した否定イベントの検討
野瀬祥吾; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第19回情報科学技術フォーラム, Domestic conference
02 Sep. 2020

継続時間を考慮したエピソードマイニングにおける行動時間帯の偏りに関する一考察
安井壱陽; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 情報処理学会第82回全国大会, Domestic conference
06 Mar. 2020

Method for Comparing Long-term Daily Life using Long-duration Episodes
Takahiko Shintani; Tadashi Ohmori; Hideyuki Fujita
Oral presentation, English, 3rd International Workshop on Data Analytics solutions for Real-LIfe APplications, International conference
26 Mar. 2019

細分化エピソードを用いた生活比較手法に関する一考察
中山恭明; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第11回データ工学と情報マネジメントに関するフォーラム, Domestic conference
06 Mar. 2019

Experimental Demonstration by Life Log Analysis
Takahiko Shintani
Nominated symposium, English, 2019 International Symposium on Secure Data Sharing and Distribution Platform for Integrated Big Data Utilization
18 Feb. 2019

頻出長時間エピソードを用いた生活比較に関する一考察
中山恭明; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第17回情報科学技術フォーラム, Domestic conference
19 Sep. 2018

リストバンド型センサで取得した運動量のレベル化による異なる期間の生活比較手法の検討
新谷隆彦; 中島彩花; 大森匡; 藤田秀之
Oral presentation, Japanese, 第13回日本感性工学会春季大会, Domestic conference
27 Mar. 2018

リストバンド型センサで取得した動作データからの運動状態の分類に対するSAX適用の試み
中島彩花; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 情報処理学会第79回全国大会, Domestic conference
17 Mar. 2017

アイテムシーケンスデータからの頻出否定シーケンシャルパターン抽出方式の検討
蘇麗妍; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第15回情報科学技術フォーラム, Domestic conference
07 Sep. 2016

ライフログ実証実験
新谷隆彦
Nominated symposium, Japanese, JST CREST「ビッグデータ統合利活用のための次世代基盤技術の創出・体系化」領域 ビッグデータ統合利用のためのセキュアなコンテンツ共有・流通基盤の構築シンポジウム2016
21 Mar. 2016

長時間エピソードマイニングにおけるインスタンス数え上げ処理量低減の検討
富金輝; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 電子情報通信学会2016年総合大会
16 Mar. 2016

アイテムセットと時系列パターンの出現順序を考慮した分類パターンによる分類モデルの精度向上に関する一考察
小柳暁奨; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 情報処理学会第77回全国大会
19 Mar. 2015

リストバンド型センサで取得した腕の向きのパターンによる運動状態分類に関する検討
楊デイ; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 情報処理学会第77回全国大会
17 Mar. 2015

継続時間を閾値としたエピソードマイニングの提案
櫻田滋大; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 電子情報通信学会2015年総合大会
12 Mar. 2015

不確実データからの頻出パターンの抽出における探索候補削減手法の検討
建島広翔; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 電子情報通信学会2015年総合大会
12 Mar. 2015

スキップ探索を用いた不確実データからの頻出パターン抽出
建島広翔; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第6回データ工学と情報マネジメントに関するフォーラム
04 Mar. 2015

マルチ最小サポートを用いて継続時間と時間間隔を考慮した時系列パターンマイニングアルゴリズム
史旭; 新谷隆彦; 大森匡; 藤田秀之
Oral presentation, Japanese, 第5回データ工学と情報マネジメントに関するフォーラム
04 Mar. 2014

運動状態のパターンを用いた異なる期間の人の生活特性比較に関する一考察
Hiroshi Isomura; Takahiko Shintani; Tadashi Ohmori
Oral presentation, Japanese, 第5回データ工学と情報マネジメントに関するフォーラム
Mar. 2013

Map/Reduceにおけるバケット再グループ化を用いたハイブリッドハッシュ結合アルゴリズム
廣瀬繁雄; 大森匡; 新谷隆彦
Oral presentation, Japanese, 電子情報通信学会データ工学研究専門委員会、日本データベース学会、情報処理学会データベースシステム研究会,第5回データ工学と情報マネジメントに関するフォーラム
Mar. 2013

User Behavior Analysis of Location Aware Search Engine
Iko Pramudiono; Takahiko Shintani; Katsumi Takahashi; Masaru Kitsuregawa
Public symposium, English, Int'l Workshop on Multimedia Data Management, IEEE
Jan. 2002

Web Mining and its SQL based Parallel Execution
Takahiko Shintani; Iko Pramudiono; Masaru Kitsuregawa
Public symposium, English, Workshop on Database Technology for Virtual Enterprises, IEEE
Feb. 2001

Web Mining and its SQL based Parallel Execution
Masaru Kitsuregawa; Takahiko Shintani; Iko Pramudiono
Public symposium, English, IEEE Workshop on Information Technology for Virtual Enterprises, IEEE
Jan. 2001

Web Log Mining and Parallel SQL Based Execution
Masaru Kitsuregawa; Takahiko Shintani; Takeshi Yoshizawa; Iko Pramudiono
Public symposium, English, Int'l Workshop on Databases in Networked Information Systems, Univ. of Aizu
Dec. 2000

Parallel Data Mining on Large Scale PC Cluster
Masaru Kitsuregawa; Takahiko Shintani; Masahisa Tamura; Iko Pramudiono
Keynote oral presentation, English, ACM SIGMOD Int'l Conf. on Web-Age Information Management, ACM, International conference
Jun. 2000

Parallel Generalized Association Rule Mining on Large Scale PC Cluster
Takahiko Shintani; Masaru Kitsuregawa
Public symposium, English, Workshop on Large-Scale Parallel KDD Systems, ACM SIGKDD, San Siego, CA, USA
Aug. 1998

Optimized Protocol Parameters to Large Scale PC Cluster and Evaluation of its Effectiveness with Parallel Data Mining
Masato Oguchi; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Public symposium, English, Proc. of Int'l Symp. on High Performance Distributed Computing, IEEE
Jul. 1998

Parallel Data Mining on a Commodity PC Cluster Connected with an ATM Switch
Masato Oguchi; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Public symposium, English, Int'l Symp. on Information Systems and Technologies for Network Society, IPSJ
Sep. 1997

Developing a PC Cluster using Commoditized Network Technology Oriented towards Database Applications
Masato Oguchi; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Invited oral presentation, English, Gigabit Network Workshop at IEEE Infocom'97, IEEE, International conference
Feb. 1997

Preliminary Experimental Results of a Parallel Association Rule Mining on ATM connected PC Clusters
Masato Oguchi; Takahiko Shintani; Takayuki Tamura; Masaru Kitsuregawa
Public symposium, English, Int'l Symp. on Cooperative Database Systems for Advanced Applications, CCF DBTC
Dec. 1996

データマイニングにおける相関関係抽出の並列処理方式の実装とその評価
新谷隆彦; 喜連川優
Public symposium, Japanese, 並列処理シンポジウム, 情報処理学会
Jun. 1996

輪講Ａ（Ｋ課程）
The University of Electro-Communications

輪講Ａ（Ｋ課程）
電気通信大学

アルゴリズム・データ構造および演習
The University of Electro-Communications

アルゴリズム・データ構造および演習
電気通信大学

Database Systems
The University of Electro-Communications

データベース論
電気通信大学

アルゴリズム論第一
The University of Electro-Communications

Database systems 1
The University of Electro-Communications

データベース論１
電気通信大学

アルゴリズム論第一
The University of Electro-Communications

アルゴリズム論第一
電気通信大学

Principles of Data Engineering 1
The University of Electro-Communications

データ工学原論１
電気通信大学

Information Prcocessing Sciety of Japan

Database Society of Japan

IEICE

大規模データからの多種の時間概念が混在するシーケンシャルパターン高速抽出技術
Takahiko Shintani
Principal investigator
2014 - 31 Mar. 2018

データ分析支援システム及び方法
Patent right, 新谷隆彦, 鈴木敬, 特願2010-218089, Date applied: 29 Sep. 2010, 特開2012-073812, Date announced: 12 Apr. 2012

データ解析システム、及びその方法
Patent right, 特願2009-280525, Date applied: 10 Dec. 2009, 特開2011-123652, Date announced: 23 Jun. 2011

診療支援システム
Patent right, 瀬戸久美子, 新谷隆彦, 光山訓, 斎藤聡, -, Hitachi, Ltd.,, 特許第4034741, Date issued: 02 Nov. 2007

データ中継サーバ，データベースサーバおよびデータベースのアクセス方法
Patent right, 西澤格, 牛嶋一智, 新谷隆彦, -, Hitachi, Ltd.,, 特許第4006214, Date issued: 31 Aug. 2007

Database access method and system capable of concealing the contents of query
Patent right, Itaru Nishizawa, Kazutomo Ushijima, Takahiko Shintani, -, Hitachi Ltd., US Patent 7,228,416 B2, Date issued: 05 Jun. 2007

複数データベースにまたがる項目パターン抽出方法、ネットワークシステム及び処理装置
Patent right, 新谷隆彦, 光山訓, 伴秀行, 橋口猛志, 特願2002-181135, Date applied: 21 Jun. 2002, Hitachi, Ltd.,, 特開2004-29902, Date announced: 29 Jan. 2004, 特許第3701633, Date issued: 22 Jul. 2005

Integrated database system and program storage medium
Patent right, Kazutomo Ushijima, Itaru Nishizawa, Takahiko Shintani, -, Hitachi Ltd., US Patent 6,898,594 B2, Date issued: 24 May 2005

Method and system for mining association rules with negative items
Patent right, Takahiko Shintani, Itaru Nishizawa, Kazutomo Ushijima, -, Hitachi, Ltd., US Patent 6,832,216 B2, Date issued: 14 Dec. 2004