[B! hadoop] yanbeã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

yanbe id:yanbe

hadoopã«é–¢ã™ã‚‹yanbeã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (13)

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Hadoopï¼‹Embulkï¼‹Kibanaã®ãƒ‡ãƒ¼ã‚¿é›†è¨ˆåŸºç›¤ã«ã‚ˆã‚‹ãƒ‡ãƒ¼ã‚¿å¯è¦–åŒ–ã¨é›†è¨ˆãƒ‡ãƒ¼ã‚¿ã‚’æ´»ç”¨ã—ãŸã‚ãƒ¼ãƒ¯ãƒ¼ãƒ‰ã‚µã‚¸ã‚§ã‚¹ãƒˆã®ä»•çµ„ã¿
é€£è¼‰ç›®æ¬¡ ãƒªã‚¯ãƒ«ãƒ¼ãƒˆã®å…¨ç¤¾æ¤œç´¢åŸºç›¤ã€ŒQassã€ã®äº‹ä¾‹ã‚’åŸºã«ã€å¤§è¦æ¨¡BtoCã‚µãƒ¼ãƒ“ã‚¹ã«æ±‚ã‚ã‚‰ã‚Œã‚‹æ¤œç´¢åŸºç›¤ã¯ã©ã†æ§‹ç¯‰ã•ã‚Œã‚‹ã‚‚ã®ãªã®ã‹ã€ã©ã‚“ãªæŠ€è¡“ãŒæŽ¡ç”¨ã•ã‚Œã¦ã„ã‚‹ã®ã‹ã€é‹ç”¨ã¯ã©ã†ãªã£ã¦ã„ã‚‹ã®ã‹ãªã©ã«ã¤ã„ã¦è§£èª¬ã™ã‚‹æœ¬é€£è¼‰ã€‚ åˆå›žã®ã€Œãƒªã‚¯ãƒ«ãƒ¼ãƒˆå…¨ç¤¾æ¤œç´¢åŸºç›¤ã®ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã€æŽ¡ç”¨æŠ€è¡“ã€é–‹ç™ºä½“åˆ¶ã¯ã©ã†ãªã£ã¦ã„ã‚‹ã®ã‹ã€ã§ã¯å…¨ä½“çš„ãªã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã€æŽ¡ç”¨æŠ€è¡“ã€é–‹ç™ºä½“åˆ¶ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¾ã—ãŸã€‚ æ¤œç´¢ã‚·ã‚¹ãƒ†ãƒ ã‚’æ§‹ç¯‰ã™ã‚‹éš›ã«ã€æœ€ã‚‚é‡è¦ãªã®ã¯ã€å‰å›žã®ã€ŒElasticsearchã¨Kuromojiã‚’ä½¿ã£ãŸå½¢æ…‹ç´ è§£æžã¨N-Gramã«ã‚ˆã‚‹æ¤œç´¢ã®é©åˆçŽ‡ã¨å†ç¾çŽ‡ã®å‘ä¸Šã€ã§ã‚‚ç´¹ä»‹ã—ãŸã€æ¤œç´¢å“è³ªã®å‘ä¸Šã§ã™ã€‚Qassã§ã¯ã€ã„ã‚ã„ã‚ãªãƒã‚°ã‚’çµ„ã¿åˆã‚ã›ã¦åˆ†æžã—ãŸã‚Šã€ãƒ©ãƒ³ã‚ãƒ³ã‚°ãƒ‡ãƒ¼ã‚¿ã«æ´»ç”¨ã—ãŸã‚Šã¨ã€ãƒã‚°ãƒ‡ãƒ¼ã‚¿ã¯ã‚·ã‚¹ãƒ†ãƒ ã®ä¸æ ¸ã¨ãªã£ã¦ã„ã¾ã™ã€‚ ç¬¬3å›žã®ä»Šå›žã¯Qassã®æ¤œç´¢åŸºç›¤ã‚’æ”¯ãˆã‚‹ãƒ‡ãƒ¼ã‚¿é›†è¨ˆåŸºç›¤ã¨ã€ãã‚Œã«ã‚ˆã‚‹ãƒ‡ãƒ¼ã‚¿å¯è¦–åŒ–ã€é›†è¨ˆã—ãŸãƒ‡ãƒ¼ã‚¿ã‚’ç”Ÿ
yanbe 2015/08/21
hadoop
ãƒªãƒ³ã‚¯
DMM inside
ãªã›ã‚™DMMã‹ã‚™web3ã«å‚å…¥ã—ãŸã®ã‹ã€‚Seamoon Protocolã‹ã‚™ç›®æŒ‡ã™æ–°ãŸãªã‚¨ãƒ³ã‚¿ãƒ¡ä½“é¨“ã®æœªæ¥ã¨ã¯
yanbe 2015/02/23
â€œAuthor tagomorisâ€ ãªã‚‹ã»ã©

hadoop

bigquery
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MPP on Hadoop, Redshift, BigQuery - Go ahead!
Twitterã§ã€Œæ—©ãä»Šæµè¡Œã®MPPã®å¤§ã¾ã‹ãªä½¿ã„æ–¹ã®é•ã„æ›¸ã‘ã‚ˆï¼ã€ã¨ã„ã†ãƒ—ãƒ¬ãƒƒã‚·ãƒ£ãƒ¼ãŒåŠç«¯ãªã„ã®ã§ã¦ãã¨ã†ã«æ›¸ãã¾ã™ï¼Žã“ã®è¨˜äº‹ã¯ä¿ºã®çµŒé¨“ã¨å‹‰å¼·ä¼šãªã©ã§ãƒ¦ãƒ¼ã‚¶ã‹ã‚‰èžã„ãŸè©±ã‚’ã‚‚ã¨ã«æ›¸ã„ã¦ã„ã‚‹ã®ã§ï¼Œã™ã¹ã¦ãŒä¿ºã®çµŒé¨“ã§ã¯ã‚ã‚Šã¾ã›ã‚“(ç‰¹ã«BigQuery)ï¼Žå„ç¤¾ã®SAã®äººã¨ã‹ã«èžã‘ã°ï¼Œã‚‚ã£ã¨è‰¯ã„ã‚¢ãƒ—ãƒãƒ¼ãƒã¨ã‹è©³ç´°ã‚’æ•™ãˆã¦ãã‚Œã‚‹ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“ï¼Ž ã‚ªãƒ³ãƒ—ãƒ¬ãƒŸã‚¹ã®å•†ç”¨MPPã¯ä½¿ã£ãŸã“ã¨ãªã„ã®ã§ãƒŽãƒ¼ã‚³ãƒ¡ãƒ³ãƒˆã§ã™ï¼Ž MPP on Hadoopã§PrestoãŒãƒ¡ã‚¤ãƒ³ãªã®ã¯ä»Šä¸€ç•ªä½¿ã£ã¦ã„ã‚‹ã‹ã‚‰ã§ï¼ŒImpalaãªã©ä»–ã®MPP on Hadoopçš„ãªã‚‚ã®ã‚‚ä¼¼ãŸã‚ˆã†ãªæ„Ÿã˜ã‹ãªã¨æ€ã£ã¦ã„ã¾ã™ï¼Ž ã‚‚ã¡ã‚ã‚“å®Ÿè£…ã®é•ã„ãªã©ãŒã‚ã‚‹ã®ã§ï¼Œãã®è¾ºã¯é©å®œè‡ªåˆ†ã§è£œé–“ã—ã¦ãã ã•ã„ï¼Ž å‰æ ã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã‚’é–‹ç™ºã—ã¦ã„ã¦ï¼Œãã®ãŸã‚ã®è§£æžåŸºç›¤ã‚’ä¸€ã‹ã‚‰ä½œã‚‹ï¼Ž ç°¡å˜ãªã¾ã¨ã‚ ãƒ‡ãƒ¼ã‚¿ã‚’è²¯ã‚ã‚‹æ‰€ãŒä½œã‚Œã‚‹ã®ã§ã‚ã‚Œã°ï¼Œãã“ã«ç›´æŽ¥ã‚¯ã‚¨ãƒªã‚’æŠ•ã’ã‚‰ã‚Œã‚‹Pre
yanbe 2014/09/09
å‚è€ƒã«ãªã£ãŸ

bigquery

redshift

hadoop
ãƒªãƒ³ã‚¯
For fast, interactive Hadoop queries, Drill may be the answer â€“ Old GigaOm
In the era of big data, there is increasing demand for ever-faster ways to analyze â€” preferably in an interactive way â€” Â information sitting in Hadoop. Now the Apache Foundation is backing an open-source version of Dremel, theÂ tool Google(s goog) uses for these jobs, as a way to bring that speedy analysis to the masses.Â The proposed tool is called Drill and the Apache Foundation documents describe
yanbe 2012/08/18
hadoop
ãƒªãƒ³ã‚¯
MongoDBã‚’ç”¨ã„ãŸã‚½ãƒ¼ã‚·ãƒ£ãƒ«ã‚¢ãƒ—ãƒªã®ãƒã‚°è§£æž ã€œè§£æžåŸºç›¤æ§‹ç¯‰ã‹ã‚‰ãƒ•ãƒãƒ³ãƒˆUIã¾ã§ã€MongoDBã‚’æœ€å¤§é™ã«æ´»ç”¨ã™ã‚‹ã€œ
Mongo DBã‚’ç”¨ã„ãŸã‚½ãƒ¼ã‚·ãƒ£ãƒ«ã‚¢ãƒ—ãƒªã®ãƒã‚°è§£æž ã€œè§£æžåŸºç›¤æ§‹ç¯‰ã‹ã‚‰ãƒ•ãƒãƒ³ãƒˆUIã¾ã§ã€Mongo DBã‚’æœ€å¤§é™ã«æ´»ç”¨ã™ã‚‹ã€œ
yanbe 2012/08/17
hadoop

mongodb
ãƒªãƒ³ã‚¯
[ã‚¤ãƒ³ã‚¿ãƒ“ãƒ¥ãƒ¼] Solr 3.5 ãƒˆãƒ¬ãƒ¼ãƒ‹ãƒ³ã‚°ã‚³ãƒ¼ã‚¹å—è¬›è€… Cloudera ç”°æ¾¤åä¹‹ æ§˜ - æ ªå¼ä¼šç¤¾ãƒãƒ³ã‚¦ã‚¤ãƒƒãƒˆ
Solr 3.5 åŸºç¤Žã®å®šæœŸé–‹å‚¬ãƒˆãƒ¬ãƒ¼ãƒ‹ãƒ³ã‚°ã‚³ãƒ¼ã‚¹ã‚’å—è¬›ã•ã‚Œã¾ã—ãŸã€ç±³Clouderaãƒ‡ã‚£ãƒ¬ã‚¯ã‚¿ãƒ¼ã®ç”°æ¾¤ã•ã‚“ã«ã€å—è¬›å¾Œã®ã‚¤ãƒ³ã‚¿ãƒ“ãƒ¥ãƒ¼ã«å¿œã˜ã¦ã„ãŸã ãã¾ã—ãŸã€‚ Clouderaã«ã¤ã„ã¦ ãƒãƒ³ã‚¦ã‚¤ãƒƒãƒˆé–¢å£ã€ŒClouderaã¨ã„ã†ä¼šç¤¾ã«ã¤ã„ã¦æ•™ãˆã¦ãã ã•ã„ã€ Clouderaç”°æ¾¤ã€Œ2008å¹´10æœˆã«å‰µæ¥ã—ãŸHadoopã®å°‚æ¥ãƒ™ãƒ³ãƒ€ãƒ¼ã§ã™ã€‚CDHï¼ˆClouderaâ€™s Distribution including Apache Hadoopï¼‰ã‚„Cloudera Managerã¨ã„ã†è£½å“ã‚’æŒã£ã¦ã„ã¾ã™ã€‚ã‚‚ã¡ã‚ã‚“Hadoopã«é–¢é€£ã—ãŸã‚³ãƒ³ã‚µãƒ«ãƒ†ã‚£ãƒ³ã‚°ã‚„ã‚µãƒãƒ¼ãƒˆã€æ•™è‚²ã‚‚è¡Œã£ã¦ã„ã¾ã™ã€‚Hadoopå‰µå§‹è€…ã®Dougã¯2009å¹´å‰åŠã«Joinã—ã¾ã—ãŸã€ é–¢å£ã€ŒHadoopã®ãƒ‡ã‚£ã‚¹ãƒˆãƒªãƒ“ãƒ¥ãƒ¼ã‚¿ãƒ¼ã¨ã„ã†ã¨ä»–ã«ã‚‚ã‚ã‚‹ã‚ˆã†ã§ã™ãŒã€Clouderaã®ç‰¹å¾´ã¯ãªã‚“ã§ã—ã‚‡ã†ã‹ã€ ç”°æ¾¤ã€ŒMapRã‚„Hortonworksã§ã™ãã€‚
yanbe 2012/04/19
Hadoopã¨çµ„ã¿åˆã‚ã›ã¦ä½¿ã†ã®ãŸã—ã‹ã«ä¾¿åˆ©ãã†

solr

hadoop
ãƒªãƒ³ã‚¯
https://www.sraoss.co.jp/event_seminar/2012/Hadoop_nagata_OSC2012TokyoSpring.pdf
Copyright Â© SRA OSS, Inc. Japan All rights reserved. 1 è¶…å…¥é–€ å¤§è¦æ¨¡åˆ†æ•£å‡¦ç†ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ Hadoop SRA OSS, Inc. æ—¥æœ¬æ”¯ç¤¾ æŠ€è¡“é–‹ç™ºéƒ¨ ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ é•·ç”° æ‚ å¾ Cloudera Certified Developer for Apache Hadoop Cloudera Certified Administrator for Apache Hadoop Copyright Â© SRA OSS, Inc. Japan All rights reserved. 2 Hadoopã¨ã¯ â— å¤§è¦æ¨¡ãªãƒ‡ãƒ¼ã‚¿ã‚’ä¸¦åˆ—åˆ†æ•£å‡¦ç†ã‚’è¡Œã†ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã‚’æä¾› â— Googleã«ã‚ˆã‚‹ MapReduce ãŠã‚ˆã³ Google File System(GFS) ã®è«–æ–‡ã‚’ãƒ™ãƒ¼ã‚¹ã«é–‹ç™ºã•ã‚ŒãŸApacheãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã®OSS GFS HDFS M
yanbe 2012/04/12
hadoop
ãƒªãƒ³ã‚¯
Hadoop Hacks
TOPICS Hacks , Database , Java ç™ºè¡Œå¹´æœˆæ—¥ 2012å¹´04æœˆ PRINT LENGTH 434 ISBN 978-4-87311-546-7 FORMAT PDF æœ¬æ›¸ã¯ã€Hadoopã®æ¦‚è¦ã‚’ç†è§£ã—ãŸèªè€…ã‚’å¯¾è±¡ã«ã€Hadoopã‚’å®Ÿéš›ã«ä½¿ã„ã“ãªã™ãŸã‚ã®ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ã¨Tipsã‚’ã¾ã¨ã‚ãŸæ›¸ç±ã§ã™ã€‚ã‚·ã‚¹ãƒ†ãƒ æ§‹ç¯‰/é‹ç”¨ã€ã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³é–‹ç™ºãªã©ã€Hadoopãªã‚‰ã§ã¯ã®ç‰¹å¾´ã‚’ä½¿ã„ã“ãªã™ãŸã‚ã®æ–¹æ³•ã‹ã‚‰ã€HBaseã€Hiveã€Pigã€Mahoutã€ZooKeeperãªã©ã‚µãƒ–ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã«ã¤ã„ã¦ã®Hackã¾ã§ã€å¹…åºƒã„å†…å®¹ã‚’ã‚«ãƒãƒ¼ã—ã¦ã„ã¾ã™ã€‚Hadoopã®åŠ›ã‚’å¼•ãå‡ºã™ã®ã«å¿…è¦ãªãƒ„ãƒ¼ãƒ«ã¨ãã®ä½¿ã„æ–¹ã€ã¾ãŸå†…éƒ¨ã®å‹•ãã‚’çŸ¥ã‚‹ãŸã‚ã®æ–¹æ³•ã€ã•ã‚‰ã«å…ˆé€²çš„ãªãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ãªã©ã€é–‹ç™ºã®ç¾å ´ã§å¿…è¦ã¨ã•ã‚Œã‚‹74ã®ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ã‚’æŽ²è¼‰ã—ã¦ã„ã¾ã™ã€‚æ—©ãã‹ã‚‰Hadoopã«æ³¨ç›®ã—ã€å®Ÿéš›ã«æ´»ç”¨ã—ã¦ããŸæŠ€è¡“è€…ã«ã‚ˆã£ã¦åŸ·ç†ã•
yanbe 2012/04/11
hadoop

book
ãƒªãƒ³ã‚¯
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‚’
yanbe 2011/10/21
hadoop

mapreduce
ãƒªãƒ³ã‚¯
Hadoopã®ç¾åœ¨ - æ€¥ãŒã°å›žã‚Œã€é¸ã¶ãªã‚‰è¿‘é“
ã‚‚ã¨ã‚‚ã¨Hadoopã¯æ³¨ç›®ã®ä»•çµ„ã¿ã§ã‚ã£ãŸã‘ã© ã“ã“ã«æ¥ã¦ã•ã‚‰ã«å¤§ããªæµã‚Œã«ãªã‚ã†ã¨ã—ã¦ã‚‹ã€‚ å„ç¨®ã®ã‚¤ãƒ™ãƒ³ãƒˆã‚„è¨˜äº‹ã«ã—ã¦ã‚‚å¤§åž‹ã®ã‚‚ã®ãŒå¤šã ä¸€ç¨®ã®HypeçŠ¶æ…‹ã«ãªã£ã¦ãã¦ã„ã‚‹ã€‚ Hadoop Japan Conference 2011 Fall Hadoop Conference Japan 2011 Fall Tickets, Mon, Sep 26, 2011 at 10:00 AM | Eventbrite ç™»éŒ²äººæ•°ã§1000äººã‚’è¶…ãˆã¦ã„ã‚‹ã€‚ Cloud Computing World Tokyo 2011 ï¼† Next Generation Data Center2011 Apache Hadoop: A New Paradigm for Data Processing http://www.idg.co.jp/expo/ngdc/2011/index.html ã“ã®ã‚¤ãƒ™ãƒ³ãƒˆãŒã‚ã£
yanbe 2011/09/19
hadoop
ãƒªãƒ³ã‚¯
Twitterã‚„Facebookã§ä½¿ã‚ã‚Œã¦ã„ã‚‹ã€ŒApache Hadoopã€ã®ãƒ¡ãƒªãƒƒãƒˆã‚„æ´å²ã‚’ä½œè€…è‡ªã‚‰ãŒèªžã‚‹
å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã®åˆ†æ•£å‡¦ç†ã‚’æ”¯ãˆã‚‹Javaã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã§ã‚ã‚Šã€ãƒ•ãƒªãƒ¼ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã¨ã—ã¦é…å¸ƒã•ã‚Œã¦ã„ã‚‹ã€ŒApache Hadoopã€ã€‚ãã®ä½œè€…ãƒ€ã‚°ãƒ»ã‚«ãƒ†ã‚£ãƒ³ã‚°ï¼ˆDoug Cuttingï¼‰ã•ã‚“ãŒã€ŒCloud Computing World Tokyo 2011ã€ï¼†ã€ŒNext Generation Data Center 2011ã€ã«ãŠã„ã¦ã€ŒApache Hadoop: A New Paradigm for Data Processingã€ã¨ã„ã†è¬›æ¼”ã‚’ã—ã¦ã„ãŸã®ã§èžãã«è¡Œã£ã¦ãã¾ã—ãŸã€‚ æº€å“¡ã®å®¢å¸ã€‚ çš†æ§˜ã‚’å‰ã«ã—ã¦è¬›æ¼”ã§ãã‚‹ã“ã¨ã‚’å¤§å¤‰å…‰æ „ã«æ€ã£ã¦ãŠã‚Šã¾ã™ã€‚ã€ŒApache Hadoopã€ã«ã¤ã„ã¦çš†æ§˜ã«ä¼ãˆã¦ã„ãã¾ã™ãŒã€ã“ã‚Œã¯ã¾ã•ã«ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®æ–°ãŸãªã‚‹ãƒ‘ãƒ©ãƒ€ã‚¤ãƒ ã‚’æä¾›ã™ã‚‹ã‚‚ã®ã§ã¯ãªã„ã‹ã¨ç§ã¯æ€ã£ã¦ãŠã‚Šã¾ã™ã€‚ ã¾ãšã¯ç°¡å˜ã«è‡ªå·±ç´¹ä»‹ã‚’ã•ã›ã¦ã„ãŸã ãã¾ã—ã‚‡ã†ã€‚ç§ã¯25å¹´ã«æ¸¡ã£ã¦ã‚·ãƒªã‚³ãƒ³ãƒãƒ¬ãƒ¼ã§ä»•
yanbe 2011/09/02
hadoop
ãƒªãƒ³ã‚¯
Parallelizing LDA Using Hadoop Map-Reduce
PARALLELIZING LDA USING HADOOP MAP-REDUCE (CSCI 596 Project) William Chang Abijit Bej Objective: Parallelize Latent Dirichlet Allocation using Hadoop Map/Reduce framework. Project Abstract: Latent Dirichlet Allocation (LDA) is a generative model for modeling text. The LDA model hypothesizes that documents contain topics with a certain probability, and these topics contain words with a certain pro
yanbe 2011/08/19
çµè«–ã‹ã‚‰è¨€ã†ã¨ä¸Šæ‰‹ãã„ã‹ãªã„ã‚‰ã—ã„ã‘ã©ã€ä¸Šæ‰‹ãã„ã‹ãªã„ç†ç”±ã‚‚åˆ†æžã•ã‚Œã¦ã¦ã“ã‚Œã¯ã“ã‚Œã§é‡è¦ãªæˆæžœ

lda

hadoop

machine-learning
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Preview of Storm: The Hadoop of Realtime Processing - BackType Technology
We have been doing realtime processing for a long time at BackType. We've recently developed a new system for doing realtime processing called Storm to replace our old system of queues and workers. Storm is a distributed, reliable, and fault-tolerant stream processing system. Its use cases are so broad that we consider it to be a fundamental new primitive for data processing. That's why we call it
yanbe 2011/08/05
hadoop

storm
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