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                    <span style="font-size:14px;">Query Directed Data Mining</span><br/>
                    From: <a href="http://www.slideshare.net/dcancel/">dcancel</a>, 6 months ago<br /><br />
                    <div style="width:425px;text-align:left" id="__ss_347919"><a style="font:14px Helvetica,Arial,Sans-serif;display:block;margin:12px 0 3px 0;text-decoration:underline;" href="http://www.slideshare.net/dcancel/query-directed-data-mining?type=powerpoint" title="Query Directed Data Mining">Query Directed Data Mining</a><object style="margin:0px" width="425" height="355"><param name="movie" value="http://static.slideshare.net/swf/ssplayer2.swf?doc=querydirecteddatamining-1207924893182616-9&stripped_title=query-directed-data-mining" /><param name="allowFullScreen" value="true"/><param name="allowScriptAccess" value="always"/><embed src="http://static.slideshare.net/swf/ssplayer2.swf?doc=querydirecteddatamining-1207924893182616-9&stripped_title=query-directed-data-mining" type="application/x-shockwave-flash" allowscriptaccess="always" allowfullscreen="true" width="425" height="355"></embed></object><div style="font-size:11px;font-family:tahoma,arial;height:26px;padding-top:2px;">View SlideShare <a style="text-decoration:underline;" href="http://www.slideshare.net/dcancel/query-directed-data-mining?type=powerpoint" title="View Query Directed Data Mining on SlideShare">presentation</a> or <a style="text-decoration:underline;" href="http://www.slideshare.net/upload?type=powerpoint">Upload</a> your own. (tags: <a style="text-decoration:underline;" href="http://slideshare.net/tag/python">python</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/pycon2005">pycon2005</a>)</div></div><br /><br />
                    Techniques using Python & Parallel Processing<br /><br />
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Query Directed Data Mining

From dcancel, 6 months ago

Techniques using Python & Parallel Processing

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Slide 2:Background Compete, Inc. analyzes large amounts of data (gigabytes per day), and accrues terabytes of data every year supporting its predictive analysis business • Tera-scale storage requirements • Massive data processing needs • Problem: Ad-hoc research against these large data sources Technology platform is Unix clump/cluster/grid of 60+ machines • Job level parallelism managed using Portable Batch System • Storage is NFS with dedicated NFS servers • Storage managed by Compete File System (CFS – presented earlier at PyCon 2005). • Application code written in a variety of languages: C, C++, Java, Python, etc.

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