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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IN, NS (Joint) |
2019-03-04 09:00 |
Okinawa | Okinawa Convention Center | Fast parallel processing for MapReduce on heterogeneous GPGPU environment Yusei Takagaki, Naoshi Sakamoto (Tokyo Denki Univ.)  IN2018-90 |
The performance of GPGPU is rapidly improving. Thus, we often own several GPGPU nodes that has different specification. ... [more] | IN2018-90 pp.37-42 |
| CPSY, RECONF, VLD, IPSJ-SLDM, IPSJ-ARC [detail] | 2017-01-25 14:25 |
Kanagawa | Hiyoshi Campus, Keio Univ. | Proxy Responses for MapReduce Delayed Task Using 10GbE FPGA Switch Koya Mitsuzuka, Ami Hayashi (Keio Univ.), Hiroki Matsutani (Keio Univ./PRESTO/NII) VLD2016-100 CPSY2016-136 RECONF2016-81 |
(To be available after the conference date) [more] | VLD2016-100 CPSY2016-136 RECONF2016-81 pp.209-214 |
| DE | 2016-09-13 16:10 |
Kanagawa | Keio Univ. (Hiyoshi Campus) | Performance Analysis of MapReduce Shuffling Harunobu Daikoku, Hideyuki Kawashima, Osamu Tatebe (Univ. Tsukuba) DE2016-15 |
This paper analyzes shuffling performance of Apache Spark, which is one of the most popular MapReduce implementations in... [more] | DE2016-15 pp.19-24 |
| NS | 2016-01-22 14:05 |
Fukuoka | Preliminary Evaluation of Distributed System Simulation with General-Purpose Distributed Processing Systems Yuya Kato, Takahiro Sugino, Masatoshi Hanai, Kazuyuki Shudo (Tokyo Tech) NS2015-162 |
In distributed systems operating on a large network such as the Internet, it is difficult to check the behavior in a re... [more] |
NS2015-162 pp.93-96 |
|
| SC | 2015-03-28 09:00 |
Fukushima | Aizu Univ. | Topology-aware Heuristic Data Allocation Algorithm for Big Data Infrastructure Wuhui Chen, Tetsuya Tashiro, Incheon Paik, Samantha Kumara (Aizu Univ.) SC2014-23 |
A novel optimal data replacement technique considering global data access cost to improve the performance of MapReduce i... [more] | SC2014-23 pp.55-60 |
| PRMU, BioX | 2015-03-20 14:00 |
Kanagawa | Training of Random Forests Using Covariate Shift on Parallel Distributed Processing Ryoji Wakayama (Chubu Univ.), Akisato Kimura (NTT), Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi (Chubu Univ.) BioX2014-73 PRMU2014-193 |
Machine learning with big data improves a classification performance but increases computatinal cost for learning. Paral... [more] | BioX2014-73 PRMU2014-193 pp.205-210 |
|
| NS, IN (Joint) |
2014-03-07 10:40 |
Miyazaki | Miyazaki Seagia | Data Transfer Aware Scheduler for Hadoop Hyuma Watanabe, Masatoshi Kawarasaki (Univ. of Tsukuba) IN2013-173 |
Data transfer time occupies large amount of Hadoop job processing time. In this paper, we propose new Hadoop task schedu... [more] | IN2013-173 pp.175-180 |
| IN, IA (Joint) |
2013-12-20 13:50 |
Hiroshima | Hiroshima City Univ. | Implementing Materialized View as a Service for Large-Scale House Log in Smart City Yuki Ise, Shintaro Yamamoto, Shinsuke Matsumoto, Sachio Saiki, Masahide Nakamura (Kobe Univ.) IN2013-115 |
We have proposed a logging platform, called Scallop4SC(Scalable Logging Platform for Smart City), for managing various a... [more] | IN2013-115 pp.95-100 |
| IN, IA (Joint) |
2013-12-20 14:15 |
Hiroshima | Hiroshima City Univ. | MapReduce Job Scheduling Based on Remaining Processing Times Tatsuma Matsuki, Tetsuya Takine (Osaka Univ.) IN2013-116 |
The MapReduce job scheduler implemented in Hadoop is a mechanism to decide which jobs are assigned to idle resources in... [more] |
IN2013-116 pp.101-106 |
| DC, CPSY (Joint) |
2013-08-02 15:15 |
Fukuoka | Kitakyushu-Kokusai-Kaigijyo | Implementation and Evaluation of JobTracker Initiative Task Scheduling on Hadoop Kazuki Yamazaki, Tomoaki Tsumura, Shoichi Saito, Hiroshi Matsuo (Nagoya Inst. of Tech.) CPSY2013-24 |
MapReduce is one of a framework to process large-scale data efficiently. Describing only two methods, Map and Reduce, di... [more] | CPSY2013-24 pp.85-90 |
| ICM, IPSJ-IOT, IPSJ-CSEC | 2013-05-10 11:45 |
Aomori | Hirosaki University | A Study on Design Method of Hadoop-based OSSs Manabu Nishio, Naoki Take (NTT) ICM2013-8 |
In recent years, with expanding of network scale and diversification of services, it also increases the amount of data t... [more] | ICM2013-8 pp.151-156 |
| NS, IN (Joint) |
2013-03-08 10:30 |
Okinawa | Okinawa Zanpamisaki Royal Hotel | Simulation of Large-Scale Distributed Systems with MapReduce Takahiro Sugino, Masatoshi Hanai, Kazuyuki Shudo (Tokyo Inst. of Tech.) NS2012-278 |
Distributed systems research requires a supporting simulator. Today, there are distributed systems with a scale of from... [more] |
NS2012-278 pp.657-662 |
| MSS, SS | 2013-03-06 13:40 |
Fukuoka | Shikanoshima | Implementing Materialized View of Large-Scale Power Consumption Log Using MapReduce Yuki Ise, Shintaro Yamamoto, Shinsuke Matsumoto, Masahide Nakamura (Kobe Univ.) MSS2012-60 SS2012-60 |
The smart city provides various value-added services by collecting large-scale data from houses and infrastructures with... [more] | MSS2012-60 SS2012-60 pp.7-12 |
| TL | 2013-02-22 14:45 |
Tokyo | Institute for Civilization & Management | Hierarchical phrase clustering based on different types of predicate argument relations Koji Kumanami, Kazuhiro Seki, Kuniaki Uehara (Kobe Univ.) TL2012-58 |
This paper proposes an approach to clustering synonymous phrases focusing on two types of predicate argument relations e... [more] | TL2012-58 pp.49-54 |
| IN | 2013-01-25 15:00 |
Kagoshima | Kagoshima-Ken-Sangyo-Kaikan | Enhancing Hadoop Performance by Network-Aware Task Scheduling Shun Kataoka, Hyuma Watanabe, Masatoshi Kawarasaki (Tsukuba Univ.) IN2012-152 |
Hadoop uses MapReduce for distributed processing of large-scale data. Although a large amount of data transfer between ... [more] |
IN2012-152 pp.81-86 |
| ICM, LOIS | 2013-01-18 13:30 |
Saga | [Encouragement Talk]
An Inplementation and Its Fundamental Evaluation of a Distributed Computing Platform Based on Structured Overlay Network Kimihiro Mizutani, Toru Mano, Osamu Akashi (NTT), Kensuke Fukuda (NII) ICM2012-56 LOIS2012-66 |
We propose new distributed computing platform for analyzing big data such as traffic and sensor data. The proposed platf... [more] | ICM2012-56 LOIS2012-66 pp.109-114 |
|
| DE | 2012-12-13 15:50 |
Kyoto | Campus Plaza Kyoto | Consideration on Ad hoc query processing with Adaptive Index in Map Reduce Environment Shohei Okudera, Daisaku Yokoyama, Miyuki Nakano, Masaru Kitsuregawa (Univ. Tokyo) DE2012-37 |
MapReduce-based processing infrastructure is getting more important as a data analysis system,which executes ad-hoc quer... [more] | DE2012-37 pp.131-136 |
| CPSY | 2012-10-12 15:30 |
Hiroshima | Improvement of MapReduce implementation of PrefixSpan Method Hidemoto Nakada (AIST), Tatsuhiko Inoue (AIST/Soum), Hirotaka Ogawa, Tomohiro Kudoh (AIST) CPSY2012-40 |
We have been implementing a Key-Value Store based MapReduce System, called SSS, which enables quick MapReduce iteration ... [more] | CPSY2012-40 pp.55-60 |
|
| DC, CPSY (Joint) |
2012-08-03 16:15 |
Tottori | Torigin Bunka Kaikan | Simulation of Large-Scale Distributed Systems with MapReduce Takahiro Sugino, Masatoshi Hanai, Kazuyuki Shudo (Tokyo Inst. of Tech.) |
Distributed systems research requires a supporting simulator. Today, there are distributed systems with a scale of from... [more] |
|
| NS, IN (Joint) |
2012-03-09 13:50 |
Miyazaki | Miyazaki Seagia | Impact of the Interconnection Network Structure on Shuffle Completion Time in MapReduce Processing Tatsuma Matsuki (Osaka Univ.), Tatsuaki Kimura, Tatsuya Mori (NTT), Tetsuya Takine (Osaka Univ.) IN2011-200 |
MapReduce processing, a typical distributed processing scheme in data centers, includes shuffle operation, where a mass... [more] |
IN2011-200 pp.377-382 |
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