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checkitout_researchã«é–¢ã™ã‚‹rawwellã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (74)

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rawwell 2013/05/26
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The LLVM Compiler Infrastructure Project
What: The third general meeting of LLVM Developers and Users. Why: To get acquainted, learn how LLVM is used, and exchange ideas. When: October 2, 2009 SPONSORED BY: Apple, Google, Adobe, Qualcomm Incorporated The meeting serves as a forum for both LLVM and Clang developers and users to get acquainted, lea rn how LLVM is used, and exchange ideas about LLVM and its (potential) applications. More br
rawwell 2012/06/29
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HBase Architecture 101 - Storage
One of the more hidden aspects of HBase is how data is actually stored. While the majority of users may never have to bother about it you may have to get up to speed when you want to learn what the various advanced configuration options you have at your disposal mean. "How can I tune HBase to my needs?", and other similar questions are certainly interesting once you get over the (at times steep) l
rawwell 2012/06/27
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Terry Jones Â» Blog Archive Â» Facebook release Tornado and itâ€™s not based on Twisted?
rawwell 2012/06/17
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Vimã§Markdownå‡ºåŠ›ã—ã¦ã¿ãŸ - Archiva
Make a note of it: Web tech, montaineering, and so on. ç´ ã®çŠ¶æ…‹ã§ã‚‚ã‚«ãƒƒã‚³è‰¯ã„ãƒ†ã‚ã‚¹ãƒˆè¨˜æ³•ã€Markdownã‚’å®Ÿç”¨ã™ã‚‹ã‚¢ãƒ¬ã‚³ãƒ¬ã€‚ã‚ã‚‹ã„ã¯å¦‚ä½•ã«ã—ã¦ç§ã¯Markdownå½¢å¼ã®å®Ÿç”¨åŒ–ã«è‡³ã£ãŸã‹ã€‚ Markdownã«é–¢ã—ã¦ã¯å‰å›žã®Sugamoã§ã‚‚è§¦ã‚ŒãŸã‘ã‚Œã©ã€ã¾ã‚ã‹ã£ã“è‰¯ã„ã€Œã¯ã¦ãªè¨˜æ³•ã€ã¨è€ƒãˆã¦å·®ã—æ”¯ãˆãªã„ã€‚Vimã§ä½¿ã†ã¨ã€HTMLã»ã©æ°—åˆã„å…¥ã‚Œãªãã¦ã‚‚è‰²åˆ†ã‘è¡¨ç¤ºã§ãã‚‹ã—ã€ã¡ã‚‡ã£ã¨ã—ãŸãƒ¡ãƒ¢æ›¸ãã‚„ãƒ–ãƒã‚°ã®ä¸‹æ›¸ãã¨ã—ã¦é‡å®ã—ã¦ã¾ã™ã€‚HTMLç›´æ›¸ãã‚‚è¨±å®¹ã™ã‚‹ã‚†ã‚‹ãƒ¼ã„è¨˜æ³•ã£ã¦ã®ã‚‚ãƒã‚¤ãƒ³ãƒˆé«˜ã„ã ãã—ã¦ä¿ºã¯æ€ã£ãŸã€‚ã“ã‚Œä½¿ã£ã¦ã€Œgfã€ã—ã¦ã„ã‘ã°ã€VimWikiè¦ã‚‰ãªã„ã‚“ã˜ã‚ƒãï¼Ÿ ã¾ãšã¯çŽ‹é“ã‹ã‚‰ Hack #52: Markdownå½¢å¼ã®æ–‡æ›¸ã‚’æ›¸ã Hack #7: ç·¨é›†ä¸ãƒ•ã‚¡ã‚¤ãƒ«ã‚’å®Ÿè¡Œã—ã€çµæžœã‚’è¡¨ç¤ºã—ãŸã¾ã¾ã«ã™ã‚‹ ã“ã“ã‚‰è¾ºã‚’å‚è€ƒã«ã‚´ãƒ‹ãƒ§ã‚´ãƒ‹ãƒ§ã‚„ã£ã¦ã¿ãŸã®
rawwell 2012/06/17
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gmane.orgÂ -Â gmane ãƒªã‚½ãƒ¼ã‚¹ãŠã‚ˆã³æƒ…å ±
gmane.org ã¯ã€ã‚ãªãŸãŒãŠæŽ¢ã—ã®æƒ…å ±ã®å…¨ã¦ã®æœ€æ–°ã‹ã¤æœ€é©ãªã‚½ãƒ¼ã‚¹ã§ã™ã€‚ä¸€èˆ¬ãƒˆãƒ”ãƒƒã‚¯ã‹ã‚‰ã“ã“ã‹ã‚‰æ¤œç´¢ã§ãã‚‹å†…å®¹ã¯ã€gmane.orgãŒå…¨ã¦ã¨ãªã‚Šã¾ã™ã€‚ã‚ãªãŸãŒãŠæŽ¢ã—ã®å†…å®¹ãŒè¦‹ã¤ã‹ã‚‹ã“ã¨ã‚’é¡˜ã£ã¦ã„ã¾ã™ï¼
rawwell 2012/06/17
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The Future of Database Clustering
Creating robust applications using open source databases and commodity hardware Baron Schwartz started a good discussion about MMM use cases that quickly veered into an argument about clustering in general. As Florian Haas put it on his blog, this is not just an issue of DRBD vs. MySQL Replication. Is a database cluster something you cobble together through bits and pieces like MMM? Or is it somet
rawwell 2012/06/17
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SIGIR 2009 Accepted Papers - Web Search and Data Mining | Google Groups
rawwell 2010/02/06
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https://docs.google.com/viewer?url=http://olivier.chapelle.cc/pub/sigir09_ssl.pdf
rawwell 2010/02/06
"It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this paper, we focus on extracting relevance information from one source of user interactions, i.e., user click data, which recor

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sigirfp185-dou.pdf
rawwell 2010/02/06
"As a good complement to page content, anchor texts have been extensively used, and proven to be useful, in commer- cial search engines. However, anchor texts have been as- sumed to be independent, whether they come from the same Web site or not. Intuitively, an anchor text from unrelated Web sites

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https://docs.google.com/viewer?url=http://research.yahoo.com/files/ataggregation.pdf
rawwell 2010/02/06
"It is well known that anchor text plays a critical role in a va- riety of search tasks performed over hypertextual domains, including enterprise search, wiki search, and web search. It is common practice to enrich a documentâ€™s standard textual representation with all of the anchor text associated

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Query Recommendation Using Large-Scale Web Access Logs and Web Page Archive
rawwell 2009/06/24
"Query recommendation suggests related queries for search engine users when they are not satisfied with the results of an initial input query, thus assisting users in improving search quality. Conventional approaches to query recommendation have been focused on expanding a query by terms extracted f

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Kitsuregawa, Toyoda Lab. -
rawwell 2009/06/24
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Kitsuregawa, Toyoda Lab. -
rawwell 2009/06/24
"Query-URL Bipartite Based Approach to Personalized Query Recommendation Lin Li, Zhenglu Yang, Ling Liu, and Masaru Kitsuregawa the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, , , 2008.07, Chicago, USA"

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Kitsuregawa, Toyoda Lab. -
rawwell 2009/06/24
"Rank Optimization of Personalized Search Lin Liï¼ŒZhenglu Yangï¼ŒMasaru Kitsuregawa ç¬¬1å›žãƒ‡ãƒ¼ã‚¿å·¥å¦ã¨æƒ…å ±ãƒžãƒã‚¸ãƒ¡ãƒ³ãƒˆã«é–¢ã™ã‚‹ãƒ•ã‚©ãƒ¼ãƒ©ãƒ ã€€Data Engineering and Information Management (DEIM2009)äºˆç¨¿é›†, , A9-5, 2009.03.09-10,"

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Kitsuregawa, Toyoda Lab. -
rawwell 2009/06/24
"""Using Ontology-Based User Preferences to Aggregate Rank Lists in Web Search Lin Li, Zhenglu Yang, and Masaru Kitsuregawa Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, , , 923-931"

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Skip graphs
Skip Graphs James Aspnes, Gauri Shah Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), January 2003, pp. 384-393. Submitted to Journal of Algorithms. Abstract Skip graphs are a novel distributed data structure, based on skip lists, that provide the full functionality of a balanced tree in a distributed system where elements are stored in separate nodes that may fail at any time.
rawwell 2009/06/18
"Skip graphs are a novel distributed data structure, based on skip lists, that provide the full functionality of a balanced tree in a distributed system where elements are stored in separate nodes that may fail at any time. They are designed for use in searching peer-to-peer networks, and improve on

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MeCabã®è¾žæ›¸ã«ã¯ã¦ãªã‚ãƒ¼ãƒ¯ãƒ¼ãƒ‰ã‚’è¿½åŠ ã—ã‚ˆã† - ä¸å¯è¦–ç‚¹
MeCabã¯å½¢æ…‹ç´ è§£æžã®ãŸã‚ã®ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã™ã€‚æ—¥æœ¬èªžã‚’åˆ†ã‹ã¡æ›¸ãã™ã‚‹ãŸã‚ã«ä½¿ã‚ã‚Œã‚‹ã‚‚ã®ã¨ã—ã¦ã¯æœ€ã‚‚äººæ°—ã®é«˜ã„ã‚‚ã®ã ã¨æ€ã‚ã‚Œã¾ã™ãŒã€ãƒãƒ£ãƒƒãƒˆã‚„æŽ²ç¤ºæ¿ã«æ›¸ãè¾¼ã¾ã‚Œã‚‹ã‚ˆã†ãªå´©ã—ãŸæ—¥æœ¬èªžã‚„ã€æ£ã—ãæ›¸ã‹ã‚ŒãŸæ—¥æœ¬èªžã§ã‚‚æ–°èªžã‚’æœŸå¾…ã—ãŸé€šã‚Šã«åˆ†ã‹ã¡æ›¸ãã—ã¦ãã‚Œãªã„ã“ã¨ãŒã‚ã‚Šã¾ã™ã€‚ã“ã‚Œã¯MeCabã®å†…éƒ¨ã§ä½¿ã‚ã‚Œã¦ã„ã‚‹è¾žæ›¸ãŒä¸€èˆ¬çš„ãªè¨€è‘‰ã‚’æƒ…å ±æºã¨ã—ã¦ã„ã‚‹ã‚ã‘ã§ã¯ãªã„ã“ã¨ã«é–¢ä¿‚ãŒã‚ã‚Šã¾ã™ã€‚MeCabã¨ã„ã†ã‹ã€ã‚ˆã‚Šä¸€èˆ¬çš„ãªè©±ã§ã™ãŒä»¥ä¸‹ã®ã‚ˆã†ãªèªè˜ãŒä¸€èˆ¬çš„ã‹ã¨æ€ã‚ã‚Œã¾ã™ã€‚ ã¨ã„ã†ã®ã‚‚ã€ä¸€ç•ªåºƒãä½¿ã‚ã‚Œã¦ã„ã‚‹ã§ã‚ã‚ã†è‡ªç„¶è¨€èªžå‡¦ç†æŠ€è¡“ã¯å½¢æ…‹ç´ è§£æž(å˜èªžåˆ†ã‹ã¡æ›¸ãã€ã¨è¨€ã£ãŸã»ã†ãŒã„ã„ã®ã‹ã‚‚ã—ã‚Œãªã„)ã§ã‚ã‚ã†ãŒã€ã“ã‚Œã¯ç¾åœ¨99%ãã‚‰ã„ã®ç²¾åº¦ã«ãªã£ã¦ã„ã¦ã€ã™ã§ã«äººé–“ãŒã‚„ã‚‹ã‚ˆã‚Šé™ã‹ã«é«˜ç²¾åº¦ã§è¡Œãˆã‚‹ã®ã ãŒã€ã“ã‚Œã ã‘é«˜ã„ç²¾åº¦ãŒå‡ºã›ã‚‹ã®ã¯æ–°èžè¨˜äº‹ã‚’ç›¸æ‰‹ã«ã—ãŸã¨ãã ã‘ã§ã‚ã£ã¦ã€ãã‚Œã¯æ–°èžè¨˜äº‹ã‚’ã‚³ãƒ¼ãƒ‘ã‚¹ã¨ã—ã¦ç”¨ã„ã‚‹æ©Ÿæ¢°å¦ç¿’ã«ã‚ˆã‚Šå½¢æ…‹ç´ è§£
rawwell 2009/06/01
"MeCabã¯ãƒ¦ãƒ¼ã‚¶ãƒ¼ãŒè¾žæ›¸ã‚’æ›´æ–°ã™ã‚‹ã“ã¨ãŒå‡ºæ¥ã¾ã™ã€‚MeCab: å˜èªžã®è¿½åŠ æ–¹æ³•ã‚’å‚è€ƒã«ã¯ã¦ãªã‚ãƒ¼ãƒ¯ãƒ¼ãƒ‰ã‚’è¿½åŠ ã—ã¾ã™ã€‚"

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Brown University - Data Management Group Â» A Comparison of Approaches to Large-Scale Data Analysis: MapReduce vs. DBMS Benchmarks
Overview The following information is meant to provide documentation on how others can recreate the benchmark trials used in our SIGMOD 2009 paper. Our experiments were conducted on a 100-node cluster at the University of Wisconsin-Madison; each node had a single 2.40 GHz Intel Core 2 Duo processor running 64-bit Red Hat Enterprise Linux 5 (kernel version 2.6.18) with 4GB RAM and two 250GB SATA-I
rawwell 2009/05/28
"The following information is meant to provide documentation on how others can recreate the benchmark trials used in our SIGMOD 2009 paper. Our experiments were conducted on a 100-node cluster at the University of Wisconsin-Madison; each node had a single 2.40 GHz Intel Core 2 Duo processor running

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database
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è²ªæ¬²ãªå¤‰æ•°é¸æŠžã«ã‚ˆã‚‹æœ€é©åŒ– - DO++
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rawwell 2009/05/27
"ãƒ»ç‰¹å¾´ï¼ˆå¤‰æ•°ï¼‰ãŒæ–‡æ›¸ä¸ã®å…¨ã¦ã®éƒ¨åˆ†æ–‡å—åˆ—ã«å¯¾å¿œã™ã‚‹å ´åˆ "Text Categorization with All Substring Features", SDM 2009, D. Okanohara, J. Tsujii [poster(ppt)] ãƒ»ç‰¹å¾´ãŒåŸºæœ¬ç´ æ€§ã®å…¨ã¦ã®çµ„ã¿åˆã‚ã›ã®å ´åˆ "Learning Combination Features with L1 Regularization", N

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