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ttakezawa id:ttakezawa

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Googleã€å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã‚’ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã«åˆ†æžã§ãã‚‹ã‚¯ãƒ©ã‚¦ãƒ‰ã‚µãƒ¼ãƒ“ã‚¹ã€ŒGoogle Cloud Dataflowã€ã‚’ç™ºè¡¨ã€‚ã€Œ1å¹´å‰ã‹ã‚‰MapReduceã¯ä½¿ã£ã¦ã„ãªã„ã€ã€‚Google I/O 2014
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ttakezawa 2014/06/27
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google

mapreduce

webservice
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Apache Sparkã£ã¦ã©ã‚“ãªã‚‚ã®ã‹è¦‹ã¦ã¿ã‚‹ï¼ˆãã®ï¼‘ - å¤¢ã¨ã‚¬ãƒ©ã‚¯ã‚¿ã®é›†ç©å ´
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ttakezawa 2013/09/02
spark

mesos

mapreduce

hadoop
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Mapreduce2.0 - æ€¥ãŒã°å›žã‚Œã€é¸ã¶ãªã‚‰è¿‘é“
æ¬¡ä¸–ä»£Hadoopã®é–‹ç™ºãŒé€²ã‚“ã§ã„ã‚‹ã€‚ç¾çŠ¶ã®æŽ¨ç§»ã§ã¯ã€å°‘ãªãã¨ã‚‚åˆ†æ•£ã‚¯ãƒ©ã‚¦ãƒ‰ã§ã®ã€ŒOSSã‚¤ãƒ³ãƒ•ãƒ©ã€ã¨ã—ã¦ã¯HadoopãŒæœ€æœ‰åŠ›å€™è£œã§ã‚ã‚‹ã“ã¨ã¯é–“é•ã„ãªã„ã€‚ã‚¯ãƒ©ã‚¦ãƒ‰ä¸Šã§ã®åˆ†æ•£å‡¦ç†åŸºç›¤ã§ã®æŠ€è¡“ç«¶äº‰ã§ã¯Googleã‚„AmazonãŒç›¸å½“æŠœãã‚“ã§ã„ã‚‹ç¾åœ¨ã€ãã‚Œã«å¯¾æŠ—ã—ã†ã‚‹å¯èƒ½æ€§ãŒã‚ã‚‹OSSã¯Hadoopã®æ½®æµã®å»¶é•·ç·šä¸Šã«ã—ã‹è€ƒãˆã‚‰ã‚Œãªã„ã€‚ãã®å½¢ã¨ã—ã¦Hadoop-MapReduce2.0ãŒã‚ã‚‹ã‚ˆã†ã«è¦‹ãˆã‚‹ã€‚ç¾åœ¨ã®çŠ¶æ…‹ã§è‡ªåˆ†ãªã‚Šã®æ¬¡ä¸–ä»£Hadoopã®ç†è§£ã‚’ã¾ã¨ã‚ã¦ãŠãã€‚åŸºæœ¬çš„ã«å…¨éƒ¨ã¯è¦‹åˆ‡ã‚Œã¦ã„ãªã„ã®ã§ã€ãã®ã‚ãŸã‚Šã¯ã‚ã—ã‹ã‚‰ãšã€‚åŸºæœ¬çš„ã«æ¬¡ä¸–ä»£Hadoopã®ä»•çµ„ã¿ã¯å¤§ããäºŒã¤ã®è¦ç´ ã‹ã‚‰ãªã‚‹ ç¾åœ¨ã®ã¨ã“ã‚ã®æŸ±ã¯HDFSã¨Mapreduce2.0ã®äºŒã¤ã ã€‚ ã¾ãšMapReduceã€‚ã“ã‚Œã¯å¾“æ¥ã®ã€ŒMapReduceã€ã¨ã„ã†ã‚‚ã®ã‹ã‚‰ã¯ã»ã©é ã„ã€‚ã‚€ã—ã‚ã€Œä»»æ„ã€ã®åˆ†æ•£å‡¦ç†å®Ÿè¡Œãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã«ãŸã„ã—ã¦ã€é©åˆ‡ãªãƒªã‚½ãƒ¼ã‚¹ã‚’
ttakezawa 2011/10/21
hadoop

mapreduce
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Menjelajahi Masa Depan Teknologi
Instagram Stories hanya bertahan selama 24 jam, tetapi ada cara untuk menyimpan momen berharga tersebut. Dengan mengikuti beberapa langkah mudah, pengguna dapat download story IG sebelum menghilang. Teknik ini memungkinkan penyimpanan konten penting tanpa kehilangan data berharga. Teknologi CRISPR telah merevolusi bidang rekayasa genetik dengan kemampuannya untuk memodifikasi DNA secara presisi. T
ttakezawa 2011/07/10
webservice

hadoop

mapreduce
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Sign in - Google Accounts
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ttakezawa 2011/06/23
nlp

google

lecture

mapreduce
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æ©Ÿæ¢°å¦ç¿’ Ã— MapReduce - ny23ã®æ—¥è¨˜
å€‹äººçš„ãªèˆˆå‘³ã¨ã„ã†ã‚ˆã‚Šï¼Œé›‘ç”¨çµ¡ã¿ã§çœºã‚ãŸè«–æ–‡ã®ç´¹ä»‹ï¼Žæ©Ÿæ¢°å¦ç¿’ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ä¸¦åˆ—åˆ†æ•£åŒ–ã™ã‚‹ã¨ã„ã†è©±ãŒæœ€è¿‘æµè¡Œã£ã¦ã„ã‚‹ã‚ˆã†ã ï¼Žå…¨ç„¶ç¶²ç¾…çš„ã§ã¯ãªã„ã‘ã©ï¼Œèª°ã‹ã®å½¹ã«ç«‹ã¤ã‹ã‚‚çŸ¥ã‚Œãªã„ã®ã§ï¼Œå¹¾ã¤ã‹ãƒ¡ãƒ¢ã—ã¦ãŠãï¼Žã¾ãšå¤å…¸çš„ã«ã¯ã“ã‚Œï¼Œ Map-reduce for machine learning on multicore (NIPS 2006) å¤å…¸çš„ãªæ©Ÿæ¢°å¦ç¿’ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ï¼ˆãƒãƒƒãƒå¦ç¿’ï¼‰ã®å¤šãã¯ï¼ŒStatistical Query Model ã§è¨˜è¿°ã§ãã¦ï¼Œãã‚Œã‚‰ã¯ summation form ã§è¨˜è¿°ã§ãã‚‹ (ã‹ã‚‰ï¼ŒMapReduce ã§ä¸¦åˆ—åŒ–ã§ãã‚‹)ï¼Žå®Ÿè£…ã¯ Mahoutï¼ŽãŸã æœ€è¿‘ã¯ï¼Œãƒãƒƒãƒã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã§è§£ã‘ã‚‹å•é¡Œã«ã¯å¤šãã®å ´åˆå¯¾å¿œã™ã‚‹ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ãŒææ¡ˆã•ã‚Œã¦ã„ã¦ï¼Œãƒãƒƒãƒã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ä¸¦åˆ—åŒ–ã™ã‚‹ã“ã¨ã®ãƒ¡ãƒªãƒƒãƒˆã¯ã‚ã¾ã‚Šç„¡ã„ï¼Žã‚ªãƒ³ãƒ©ã‚¤ãƒ³ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã ã¨ãƒ‘ãƒ©ãƒ¡ã‚¿ãŒé€£ç¶šçš„ã«æ›´æ–°ã•ã‚Œã‚‹ã®ã§ï¼ŒMapR
ttakezawa 2010/10/05
algorithm

mapreduce

machinelearning
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schedule/2007-05-17 - æ©Ÿæ¢°å¦ç¿’å‹‰å¼·ä¼šWiki
2010-05-21 schedule/2010-07-08 2010-05-14 schedule/2010-05-20 2010-05-13 schedule/2010-05-13 2010-05-10 FrontPage 2010-05-06 schedule/2010-07-01 schedule/2010-05-06 schedule/2010-06-24 schedule/2010-06-17 schedule/2010-05-27 schedule/2010-06-10 schedule/2010-04-15 schedule/2010-06-03 2010-04-15 ç™ºè¡¨è€…ã®ä»•äº‹ 2010-04-13 schedule schedule/2010-03-18 RecentDeleted 2010-04-08 MenuBar 2010-04-05 schedule%2F20
ttakezawa 2010/05/23
mapreduce

machinelearning
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Amazon.co.jp: ã‚¯ãƒ©ã‚¦ãƒ‰å¤§å…¨: æ—¥çµŒBPç¤¾å‡ºç‰ˆå±€ (è‘—), æ—¥çµŒBPç¤¾å‡ºç‰ˆå±€ (ç·¨é›†): æœ¬
ttakezawa 2010/03/22
amazon

book

hadoop

mapreduce
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Mapreduce & Hadoop Algorithms in Academic Papers | atbrox
The newest and most up-to-date version (May 2010) this blog post is available at http://mapreducebook.org An updated and extended version of this blog post can be found here. Motivation Learn from academic literature about how the mapreduce parallel model and hadoop implementation is used to solve algorithmic probl ems. Disclaimer: this is work in progress (look for updates) Input Data â€“ Academic P
ttakezawa 2009/10/04
hadoop

mapreduce

algorithm

paper

research
ãƒªãƒ³ã‚¯
mrtoolkit - Google Code
Code Archive Skip to content Google About Google Privacy Terms
ttakezawa 2009/05/12
hadoop

mapreduce

ruby
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Big Data Platform - Amazon EMR - AWS
Amazon EMR Serverless is a new option in Amazon EMR that makes it easy and cost-effective for data engineers and analysts to run applications built using open source big data frameworks such as Apache Spark, Hive or Presto, without having to tune, operate, optimize, secure or manage clusters.
ttakezawa 2009/04/02
amazon

webservice

mapreduce
ãƒªãƒ³ã‚¯
IBM Developer
IBM Developer is your one-stop location for getting hands-on training and learning in-demand skills on relevant techno logies such as generative AI, data science, AI, and open source.
ttakezawa 2009/01/22
mapreduce

rails

erlang

couchdb
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Hadoopã€hBaseã§æ§‹ç¯‰ã™ã‚‹å¤§è¦æ¨¡åˆ†æ•£ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚·ã‚¹ãƒ†ãƒ
CodeZineç·¨é›†éƒ¨ã§ã¯ã€ç¾å ´ã§æ´»èºã™ã‚‹ãƒ‡ãƒ™ãƒãƒƒãƒ‘ãƒ¼ã‚’ã‚¹ã‚¿ãƒ¼ã«ã™ã‚‹ãŸã‚ã®ã‚«ãƒ³ãƒ•ã‚¡ãƒ¬ãƒ³ã‚¹ã€ŒDevelopers Summitã€ã‚„ã€ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã®ç”Ÿãã–ã¾ã‚’ãƒ–ãƒ¼ã‚¹ãƒˆã™ã‚‹ãŸã‚ã®ã‚¤ãƒ™ãƒ³ãƒˆã€ŒDevelopers Boostã€ãªã©ã€ã•ã¾ã–ã¾ãªã‚«ãƒ³ãƒ•ã‚¡ãƒ¬ãƒ³ã‚¹ã‚’ä¼ç”»ãƒ»é‹å–¶ã—ã¦ã„ã¾ã™ã€‚
ttakezawa 2008/10/12
amazon

ec2

hadoop

hbase

google

mapreduce

bigtable

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Hadoopã®è§£æžè³‡æ–™
ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹åˆ†æ•£ã‚·ã‚¹ãƒ†ãƒ ã€ŒHadoopã€ã«é–¢ã™ã‚‹è§£æžè³‡æ–™ã‚’å…¬é–‹ã•ã›ã¦é ‚ã„ã¦ãŠã‚Šã¾ã™ã€‚ã“ã®èª¿æŸ»ã¯NTTãƒ¬ã‚¾ãƒŠãƒ³ãƒˆæ ªå¼ä¼šç¤¾æ§˜ã¨å…±åŒã§è¡Œã„ã¾ã—ãŸ(ãƒ—ãƒ¬ã‚¹ãƒªãƒªãƒ¼ã‚¹)ã€‚ Hadoopè§£æžè³‡æ–™(PDF), æœ€çµ‚æ›´æ–°: 2008/08/25, å…¬é–‹: 2008/08/25 Hadoopã®å®Ÿéš›ã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«æ–¹æ³•ãªã©ã«ã¤ãã¾ã—ã¦ã¯ã€å¼Šç¤¾å¤ªç”°ã«ã‚ˆã‚‹ä»¥ä¸‹ã®è¨˜äº‹ã‚‚ã”å‚è€ƒä¸‹ã•ã„ã€‚ Hadoopã€hBaseã§æ§‹ç¯‰ã™ã‚‹å¤§è¦æ¨¡åˆ†æ•£ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚·ã‚¹ãƒ†ãƒ Hadoopã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã¨ã‚µãƒ³ãƒ—ãƒ«ãƒ—ãƒã‚°ãƒ©ãƒ ã®å®Ÿè¡Œ è¤‡æ•°ãƒžã‚·ãƒ³ã¸Hadoopã‚’ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã™ã‚‹
ttakezawa 2008/09/26
hadoop

google

reference

mapreduce
ãƒªãƒ³ã‚¯
MapReduce - naoyaã®ã¯ã¦ãªãƒ€ã‚¤ã‚¢ãƒªãƒ¼
"MapReduce" ã¯ Google ã®ãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰ã§åˆ©ç”¨ã•ã‚Œã¦ã„ã‚‹ä¸¦åˆ—è¨ˆç®—ã‚·ã‚¹ãƒ†ãƒ ã§ã™ã€‚æ¤œç´¢ã‚¨ãƒ³ã‚¸ãƒ³ã®ã‚¤ãƒ³ãƒ‡ãƒƒã‚¯ã‚¹ä½œæˆã‚’ã¯ã˜ã‚ã¨ã™ã‚‹ã€å¤§è¦æ¨¡ãªå…¥åŠ›ãƒ‡ãƒ¼ã‚¿ã«å¯¾ã™ã‚‹ãƒãƒƒãƒå‡¦ç†ã‚’æƒ³å®šã—ã¦ä½œã‚‰ã‚ŒãŸã‚·ã‚¹ãƒ†ãƒ ã§ã™ã€‚ MapReduce ã®é¢ç™½ã„ã¨ã“ã‚ã¯ã€map() ã¨ reduce() ã¨ã„ã†äºŒã¤ã®é–¢æ•°ã®çµ„ã¿åˆã‚ã›ã‚’å®šç¾©ã™ã‚‹ã ã‘ã§ã€å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã«å¯¾ã™ã‚‹æ§˜ã€…ãªè¨ˆç®—å•é¡Œã‚’è§£æ±ºã™ã‚‹ã“ã¨ãŒã§ãã‚‹ç‚¹ã§ã™ã€‚ MapReduce ã®è¨ˆç®—ãƒ¢ãƒ‡ãƒ« map() ã«ã¯ãã®è¨ˆç®—å•é¡Œã®ãƒ‡ãƒ¼ã‚¿ã¨ã—ã¦ã® key-value ãƒšã‚¢ãŒæ¬¡ã€…ã«æ¸¡ã£ã¦ãã¾ã™ã€‚map() ã§ã¯ key-value å€¤ã®ãƒšã‚¢ã‚’ç•°ãªã‚‹è¤‡æ•°ã® key-value ãƒšã‚¢ã«å¤‰æ›ã—ã¾ã™ã€‚reduce() ã«ã¯ã€map() ã§ä½œã£ãŸ key-value ãƒšã‚¢ã‚’åŒä¸€ã® key ã§æŸããŸã‚‚ã®ãŒé †ç•ªã«æ¸¡ã£ã¦ãã¾ã™ã€‚ãã® key-values ãƒšã‚¢ã‚’ä»»æ„ã®å½¢å¼ã«å¤‰æ›ã™ã‚‹ã“ã¨
ttakezawa 2008/05/12
google

algorithm

mapreduce
ãƒªãƒ³ã‚¯
space
A Ruby MapReduce Framework Skynet RubyForge Home Documentation See Skynet at RailsConf 08 Sat Mar 31, 2008 4:25pm â€“ 5:15pm PDT @ Portland Ballroom 255 More Info What is Skynet Skynet is an open source Ruby implementation of Googleâ€™s MapReduce framework, created at Geni. With Skynet, one can easily convert a time-consuming serial task, such as a computationally expensive Rails migration, into a di
ttakezawa 2008/02/09
ruby

mapreduce

library
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Skynetâˆ’æ–°ã—ã„Rubyã®MapReduce
ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚’åˆ†æ•£ã•ã›ã‚‹MapReduceè¨è¨ˆãƒ‘ã‚¿ãƒ¼ãƒ³(source)ãŒ2004å¹´ã«Googleã«ã‚ˆã£ã¦å°Žå…¥ã•ã‚Œã€ãã‚Œã«ã¯C++ã®å®Ÿè£…ãŒã¤ã„ã¦ããŸã€‚ä»Šã§ã¯ã€Adam Pisoniæ°(ã‚µã‚¤ãƒˆãƒ»è‹±èªž)ãŒSkynet(ã‚µã‚¤ãƒˆãƒ»è‹±èªž)ã¨ã„ã†åç§°ã§ãƒªãƒªãƒ¼ã‚¹ã—ãŸæ–°ã—ã„Rubyã§ã®å®Ÿè£…ç‰ˆãŒåˆ©ç”¨å¯èƒ½ã ã€‚ Skynetã¯é †å¿œæ€§ãŒã‚ã‚Šã€è‡ªå‹•ã‚¢ãƒƒãƒ—ã‚°ãƒ¬ãƒ¼ãƒ‰æ©Ÿèƒ½ä»˜ãã§è€éšœå®³æ€§ã®å®Œå…¨åˆ†æ•£åž‹ã‚·ã‚¹ãƒ†ãƒ ã§ã€å˜ä¸€éšœå®³ç‚¹ã¯çš†ç„¡ã§ã™ã€‚ Googleã®ãƒ‡ã‚¶ã‚¤ãƒ³ãƒšãƒ¼ãƒ‘ãƒ¼ã¨Skynetã«ã¯è‘—ã—ãé‡è¦ãªç›¸é•ç‚¹ãŒ2ã¤ã‚ã‚‹ã€‚ SkynetãŒç”Ÿã‚³ãƒ¼ãƒ‰ã‚’ãƒ¯ãƒ¼ã‚«ãƒ¼ã«é€ã‚Œãªã„ã“ã¨ã€‚ Skynetã¯ãƒ”ã‚¢ãƒ»ãƒªã‚«ãƒãƒªã‚·ã‚¹ãƒ†ãƒ ã‚’åˆ©ç”¨ã—ã¦ãŠã‚Šã€ãƒ¯ãƒ¼ã‚«ãƒ¼ãŒãŠäº’ã„ã‚’ç›£è¦–ã—ã¦ã„ã‚‹ã“ã¨(ã‚µã‚¤ãƒˆãƒ»è‹±èªž)ã€‚ ç†ç”±ãŒä½•ã§ã‚ã‚Œãƒ¯ãƒ¼ã‚«ãƒ¼ãŒæ•…éšœã™ã‚‹ã€ã‚ã‚‹ã„ã¯æ©Ÿèƒ½ã—ãªããªã‚‹ã¨ã€åˆ¥ã®ãƒ¯ãƒ¼ã‚«ãƒ¼ãŒæ°—ä»˜ã„ã¦ã€ãã®ã‚¿ã‚¹ã‚¯ã‚’å¼•ãç¶™ãŽã¾ã™ã€‚ã¾ãŸã€Skynetã«ã¯ç‰¹åˆ¥ãªã€Œãƒžã‚¹ã‚¿ãƒ¼ã€ã‚µãƒ¼ãƒã¯å˜åœ¨ã›
ttakezawa 2008/02/09
mapreduce

ruby
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Google Research Publication: MapReduce: Simplified Data Processing on Large Clusters
MapReduce:Â SimplifiedÂ DataÂ ProcessingÂ onÂ LargeÂ Clusters Jeffrey Dean and Sanjay Ghemawat Abstract MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with t
ttakezawa 2008/01/13
algorithm

google

mapreduce

research

clustering

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