{"id":8592,"date":"2011-05-09T14:17:12","date_gmt":"2011-05-09T20:17:12","guid":{"rendered":"https:\/\/staging.opexlearning.com\/resources\/?p=8592"},"modified":"2016-12-24T23:36:51","modified_gmt":"2016-12-25T04:36:51","slug":"who-is-panda-in-the-google-content-farm-update","status":"publish","type":"post","link":"https:\/\/staging.opexlearning.com\/resources\/who-is-panda-in-the-google-content-farm-update\/8592\/","title":{"rendered":"Who is &#8220;Navneet Panda&#8221; in The Google Panda Update?"},"content":{"rendered":"<div class=\"a296a24fa2fc69ef5487857f02f111e8\" data-index=\"9\" style=\"float: none; margin:10px 0 10px 0; text-align:center;\">\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\r\n<!-- Single Post readerboard -->\r\n<ins class=\"adsbygoogle\"\r\n     style=\"display:inline-block;width:728px;height:90px\"\r\n     data-ad-client=\"ca-pub-8207522353004717\"\r\n     data-ad-slot=\"1144967431\"><\/ins>\r\n<script>\r\n(adsbygoogle = window.adsbygoogle || []).push({});\r\n<\/script>\n<\/div>\n<p>First, a warning: To those that read my blog and are typically interested in posts on <a title=\"lean manufacturing\" href=\"https:\/\/staging.opexlearning.com\/resources\/about-peter-abilla\/what-is-lean\/\">Lean Manufacturing<\/a> and Change Management, this article is not on either of those topics. Okay, read on. I concede: I know almost nothing about SEO, but other publications I read have been discussing this &#8220;Panda&#8221; update or &#8220;Farmer&#8221; update that Google rolled out recently. So, I decided to read-up and learn about what&#8217;s going on. Furthermore, I don&#8217;t know jack about Google. Other than my 2006 <a title=\"google job interview\" href=\"https:\/\/staging.opexlearning.com\/resources\/my-interview-job-offer-from-google\/31\/\">job interview with Google<\/a>, I am simply a user of Google search. I don&#8217;t have any insider dealings. I&#8217;m just another guy. When the first major algorithm update came out, many in the SEO world dubbed the update the &#8220;Farmer&#8221; update because the aim of the update was to devalue content farms and, by doing so, increase the value of high quality sites by reducing the value of low quality sites &#8211; pretty much sites that are like a <a title=\"why do neighbor dogs poop in my yard?\" href=\"https:\/\/staging.opexlearning.com\/resources\/neighbor-dog-pooping-on-my-lawn\/174\/\">neighborhood of dog poopy<\/a>.<\/p>\n<p>I guess a bunch of websites got affected by the update &#8211; big sites too. That&#8217;s pretty much what I know. But, what got lost in all the debate was this important question: <strong>Who the heck is Panda?<\/strong><\/p>\n<h2>Who is Panda?<\/h2>\n<p>Well, we know that Panda is a Google engineer, as explained by Matt Cutts in this interview with <a title=\"panda update, google farmer update\" href=\"http:\/\/www.wired.com\/epicenter\/2011\/03\/the-panda-that-hates-farms\/\" class=\"broken_link\">Wired Magazine<\/a>:<\/p>\n<blockquote><p><strong>Wired.com<\/strong>: What&#8217;s the code name of this update? Danny Sullivan of Search Engine Land has been calling it Farmer\u009d because its apparent target is content farms. <strong>Amit Singhal<\/strong>: Well, we named it internally after an engineer, and <em>his name is <strong>Panda<\/strong><\/em>. So internally we called a big Panda. He was one of the key guys. He basically came up with the breakthrough a few months back that made it possible.<\/p><\/blockquote>\n<p>Here, Amit Singhal, who I guess is an important person in the SEO world, verifies that &#8220;Panda&#8221; is a person &#8211; yeah, a real human being with a pretty cool name. And, the update was based on his breakthrough. So, if Panda is a person, whose recent breakthrough led to a massive change in how websites are valued in the eyes of Google, what we can know about him might help the largely confused world regarding the Panda or Farmer or whatever update. So, what do we know about him? And, can some knowledge of his background, research interest, or whatever give us a hint as to how one can survive the dreaded Panda or Farmer update? Can our knowledge of Panda&#8217;s background help Black Hat SEOs better game Google? Obviously I&#8217;m not the best person to answer those questions, but here&#8217;s what we know about Panda, taken from a simple search on Google, Linkedin, Facebook, and Twitter.<\/p>\n<h2>Who is Navneet Panda?<\/h2>\n<p>Based on his <a title=\"navneet panda homepage\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/\">homepage<\/a>, his <a title=\"navneet panda resume\" href=\"https:\/\/staging.opexlearning.com\/resources\/wp-content\/uploads\/2011\/05\/navneet-panda-google-pandaresume.pdf\">resume<\/a>, his <a title=\"navneet panda\" href=\"http:\/\/www.facebook.com\/people\/Navneet-Panda\/100000282376531\" class=\"broken_link\">facebook profile<\/a>, google buzz, and his <a title=\"navneet panda\" href=\"https:\/\/www.linkedin.com\/in\/navneet-panda-9052ba\">linkedin profile<\/a>\u00a0we know a few things:<\/p>\n<ul>\n<li>Navneet Panda studied at the Indian Institute of Technology in Kharagpur in the Department of Mathematics and earned a MSc in Mathematics and Computing ( Integrated 5-year course )<\/li>\n<li>Navneet Panda then went on to the University of California Santa Barbara, where he earned a Ph.D in Computer Science. His advisor was Edward Y. Chang.<\/li>\n<\/ul>\n<p>It appears that before he worked for Google in 2007, he did a summer internship at Intel and at the IBM T. J. Watson Research Center in New York. Navneet Panda has filed 2 patents, and they are described below:<\/p>\n<ul>\n<li>Learning Concept Templates from Web Images to Query Personal Image Databases,\u00a0Navneet Panda, Yi Y. Wu, Jean-Yves Bougueti, Ara Ne\u00ef\u00ac\u0081an (Filed with Intel, June 2007)<\/li>\n<li>Fast Approximate SVM Classi\u00ef\u00ac\u0081cation for Large-Scale Stream Filtering,\u00a0Navneet Panda, Ching-Yung Lin and Lisa D. Amini (Filed with IBM, Sep 2005)<\/li>\n<\/ul>\n<p>Below are a list of his publications followed by a short abstract, which might give us a sense of what might have been behind the Google Panda Update:<\/p>\n<ul>\n<li><strong>Efficient Top-k Hyperplane Query Processing for Multimedia Information RetrievalAbstract<\/strong>: A query can be answered by a binary classifier, which separates the instances that are relevant to the query from the\u00a0ones that are not. When kernel methods are employed to\u00a0train such a classifier, the class boundary is represented as\u00a0a hyperplane in a projected space. Data instances that are\u00a0farthest from the hyperplane are deemed to be most relevant\u00a0to the query, and that are nearest to the hyperplane to be\u00a0most uncertain to the query. In this paper, we address the\u00a0twin problems of efficient retrieval of the approximate set of\u00a0instances (a) farthest from and (b) nearest to a query hyperplane. Retrieval of instances for this hyperplane-based query\u00a0scenario is mapped to the range-query problem allowing for\u00a0the reuse of existing index structures. Empirical evaluation\u00a0on large image datasets confirms the effectiveness of our approach (<a title=\"efficient top k hyperplane query processing\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/acmmm_06.pdf\">link<\/a>).<\/li>\n<li><strong>Concept Boundary Detection for Speeding up SVMs<\/strong>: Support Vector Machines (SVMs) suffer from\u00a0an O(n2) training cost, where n denotes the\u00a0number of training instances. In this paper,\u00a0we propose an algorithm to select boundary\u00a0instances as training data to substantially reduce\u00a0n. Our proposed algorithm is motivated\u00a0by the result of (Burges, 1999) that, removing\u00a0non-support vectors from the training set\u00a0does not change SVM training results. Our\u00a0algorithm eliminates instances that are likely\u00a0to be non-support vectors. In the concept independent preprocessing step of our algorithm,\u00a0we prepare nearest-neighbor lists for\u00a0training instances. In the concept-specied\u00a0sampling step, we can then effectively select\u00a0useful training data for each target concept.\u00a0Empirical studies show our algorithm to be\u00a0effective in reducing n, outperforming other\u00a0competing downsampling algorithms without\u00a0signicantly compromising testing accuracy (<a title=\"concept boundary detection for speeding up svm\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/icml06.pdf\">link<\/a>).<\/li>\n<li><strong>KDX: An Indexer for Support Vector Machines<\/strong>: Support Vector Machines (SVMs) have been adopted by many data-mining and information-retrieval applications\u00a0for learning a mining or query concept, and then retrieving the top-k\u009d best matches to the concept. However,\u00a0when the dataset is large, naively scanning the entire dataset to find the top matches is not scalable. In this work,\u00a0we propose a kernel indexing strategy to substantially prune the search space and thus improve the performance\u00a0of top-k queries. Our kernel indexer (KDX) takes advantage of the underlying geometric properties and quickly\u00a0converges on an approximate set of top-k instances of interest. More importantly, once the kernel (e.g., Gaussian\u00a0kernel) has been selected and the indexer has been constructed, the indexer can work with different kernel-parameter\u00a0settings (e.g.,\u00a0and \u001b) without performance compromise. Through theoretical analysis, and empirical studies on a\u00a0wide variety of datasets, we demonstrate KDX to be very effective (<a title=\"indexer for vector machines\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/kdx-tkde.pdf\">link<\/a>).<\/li>\n<li><strong>Exploiting Geometry for Support Vector Machine Indexing<\/strong>: Support Vector Machines (SVMs) have been adopted by\u00a0many data-mining and information-retrieval applications for\u00a0learning a mining or query concept, and then retrieving\u00a0the top-k\u009d best matches to the concept. However, when\u00a0the dataset is large, naively scanning the entire dataset\u00a0to find the top matches is not scalable. In this work, we\u00a0propose a kernel indexing strategy to substantially prune\u00a0the search space and thus improve the performance of top-k\u00a0queries. Our kernel indexer (KDX) takes advantage of the\u00a0underlying geometric properties and quickly converges on\u00a0an approximate set of top-k instances of interest. More\u00a0importantly, once the kernel (e.g., Gaussian kernel) has\u00a0been selected and the indexer has been constructed, the\u00a0indexer can work with different kernel-parameter settings\u00a0without performance compromise. Through\u00a0theoretical analysis, and empirical studies on a wide variety\u00a0of datasets, we demonstrate KDX to be very effective (<a title=\"vector machine indexing\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/1_sdm.pdf\">link<\/a>).<\/li>\n<li><strong>Hypersphere Indexer<\/strong>: Indexing high-dimensional data for efficient nearest-neighbor searches\u00a0poses interesting research challenges. It is well known that when data dimension\u00a0is high, the search time can exceed the time required for performing a linear scan\u00a0on the entire dataset. To alleviate this dimensionality curse, indexing schemes\u00a0such as locality sensitive hashing (LSH) and M-trees were proposed to perform\u00a0approximate searches. In this paper, we propose a hypersphere indexer, named\u00a0Hydex, to perform such searches. Hydex partitions the data space using concentric\u00a0hyperspheres. By exploiting geometric properties, Hydex can perform effective\u00a0pruning. Our empirical study shows that Hydex enjoys three advantages over\u00a0competing schemes for achieving the same level of search accuracy. First, Hydex\u00a0requires fewer seek operations. Second, Hydex can maintain sequential disk accesses\u00a0most of the time. And third, it requires fewer distance computations (<a title=\"hypersphere indexer\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/indexing.pdf\">link<\/a>).<\/li>\n<li><strong>Active Learning in Very Large Databases<\/strong>: Query-by-example and query-by-keyword both suffer from the problem of aliasing,\u009d\u00a0meaning that example-images and keywords potentially have variable interpretations or\u00a0multiple semantics. For discerning which semantic is appropriate for a given query, we have\u00a0established that combining active learning with kernel methods is a very effective approach.\u00a0In this work, we first examine active-learning strategies, and then focus on addressing the\u00a0challenges of two scalability issues: scalability in concept complexity and in dataset size. We\u00a0present remedies, explain limitations, and discuss future directions that research might take (<a title=\"active learning in large databases\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/mtap.pdf\">link<\/a>).<\/li>\n<li><strong>Formulating Context-dependent Similarity<\/strong>: Tasks of information retrieval depend on a good distance function\u00a0for measuring similarity between data instances. The most effective\u00a0distance function must be formulated in a context-dependent\u00a0(also application-, data-, and user-dependent) way. In this paper, we\u00a0present a novel method, which learns a distance function by capturing\u00a0the nonlinear relationships among contextual information provided\u00a0by the application, data, or user. We show that through a process\u00a0called the kernel trick,\u009d such nonlinear relationships can be\u00a0learned efficiently in a projected space. In addition to using the kernel\u00a0trick, we propose two algorithms to further enhance efficiency\u00a0and effectiveness of function learning. For efficiency, we propose\u00a0a SMO-like solver to achieve O(N2) learning performance. For\u00a0effectiveness, we propose using unsupervised learning in an innovative\u00a0way to address the challenge of lack of labeled data (contextual\u00a0information). Theoretically, we substantiate that our method\u00a0is both sound and optimal. Empirically, we demonstrate that our\u00a0method is effective and useful (<a title=\"context dependent similarity\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/mm05-f464-wu.pdf\">link<\/a>).<\/li>\n<li><strong>Formulating Distance Functions via the Kernel Trick<\/strong>: Tasks of data mining and information retrieval depend on a good\u00a0distance function for measuring similarity between data instances.\u00a0The most effective distance function must be formulated in a context dependent\u00a0(also application-, data-, and user-dependent) way. In\u00a0this paper, we propose to learn a distance function by capturing the\u00a0nonlinear relationships among contextual information provided by\u00a0the application, data, or user. We show that through a process called\u00a0the kernel trick,\u009d such nonlinear relationships can be learned efficiently\u00a0in a projected space. Theoretically, we substantiate that\u00a0our method is both sound and optimal. Empirically, using several\u00a0datasets and applications, we demonstrate that our method is effective\u00a0and useful (<a title=\"formulating distance functions via kernel trick\" href=\"http:\/\/sites.google.com\/site\/navneetpanda\/mm05-f464-wu.pdf\">link<\/a>).<\/li>\n<li>Speeding up Approximate SVM Classification for Data Streams<\/li>\n<li>Improving Accuracy of SVMs by Allowing Support Vector Control<\/li>\n<\/ul>\n<p>Here are what he lists as Research Projects on his resume: <strong>Machine Learning<\/strong>:<\/p>\n<ul>\n<li>Development of indexing structures for support vector machines to enable relevant\u00a0instance search in high-dimensional datasets<\/li>\n<li>Speeding up SVM training in multi-category large dataset scenarios<\/li>\n<li>Speeding up approximate SVM classi\u00ef\u00ac\u0081cation of data-streams<\/li>\n<li>Improving concept identi\u00ef\u00ac\u0081cation and classi\u00ef\u00ac\u0081cation for personal image retrieval<\/li>\n<li>Using idealizing kernels to develop distance metrics incorporating user preferences\u00a0for high-dimensional data<\/li>\n<li>Design of a real time web page classi\u00ef\u00ac\u0081er for text and image data<\/li>\n<\/ul>\n<p><strong>Grid Computing and Distributed Systems<\/strong>:<\/p>\n<ul>\n<li>Development of scheduling strategies for numerous large jobs in a grid environment\u00a0under heavy load conditions using the Network Weather Service and Globus<\/li>\n<li>Development of scheduling strategies for executing compute-intensive jobs in a\u00a0dynamically evolving simulated market of servers providing priced slots of CPU\u00a0time for process execution<\/li>\n<li>Development of a distributed dictionary enforcing causal ordering<\/li>\n<li>Development of dynamic peer to peer system with query lookup modeling the CAN\u00a0architecture<\/li>\n<\/ul>\n<p><strong>Computer Architecture<\/strong>:<\/p>\n<ul>\n<li>Design of a snoopy cache for a multiprocessor system<\/li>\n<li>Design of a superscalar instruction dispatch unit<\/li>\n<\/ul>\n<h2>Now What?<\/h2>\n<p>I don&#8217;t know. I&#8217;ll leave it to the SEO people to decide. I just write and don&#8217;t pay much attention to SEO because I don&#8217;t know much about SEO. But, at least now we can put a face to a generically named Google algorithm update called &#8220;Panda&#8221;. Now, when someone references a Google algorithm update as &#8220;Panda&#8221;, we can all, under our breath say, &#8220;Yeah, that Navneet Panda guy&#8221;. <img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-8593 aligncenter\" title=\"google-panda-navneet-panda-seo\" src=\"https:\/\/staging.opexlearning.com\/resources\/wp-content\/uploads\/2011\/05\/google-panda-navneet-panda-seo.jpg\" alt=\"\" width=\"487\" height=\"645\" srcset=\"https:\/\/staging.opexlearning.com\/resources\/wp-content\/uploads\/2011\/05\/google-panda-navneet-panda-seo.jpg 487w, https:\/\/staging.opexlearning.com\/resources\/wp-content\/uploads\/2011\/05\/google-panda-navneet-panda-seo-226x300.jpg 226w\" sizes=\"(max-width: 487px) 100vw, 487px\" \/><\/p>\n<!--CusAds0-->\n<div style=\"font-size: 0px; height: 0px; line-height: 0px; margin: 0; padding: 0; clear: both;\"><\/div>","protected":false},"excerpt":{"rendered":"<p>First, a warning: To those that read my blog and are typically interested in posts on Lean Manufacturing and Change Management, this article is not on either of those topics. Okay, read on. I concede: I know almost nothing about SEO, but other publications I read have been discussing this &#8220;Panda&#8221; update or &#8220;Farmer&#8221; update [&hellip;]<\/p>\n","protected":false},"author":12327,"featured_media":8594,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[7],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Who is Panda in the Google Panda Update?<\/title>\n<meta name=\"description\" content=\"Who is Panda in the Google Panda Update? Navneet Panda is a Google employee and the guy who the Google Panda Update for SEO is named for.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Who is Panda in the Google Panda Update?\" \/>\n<meta property=\"og:description\" content=\"Who is Panda in the Google Panda Update? Navneet Panda is a Google employee and the guy who the Google Panda Update for SEO is named for.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/staging.opexlearning.com\/resources\/who-is-panda-in-the-google-content-farm-update\/8592\/\" \/>\n<meta property=\"og:site_name\" content=\"OpEx Learning\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/get.shmula\" \/>\n<meta property=\"article:published_time\" content=\"2011-05-09T20:17:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2016-12-25T04:36:51+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/staging.opexlearning.com\/resources\/wp-content\/uploads\/2011\/05\/navneet-panda-google-farmer-panda-update-thumb.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"363\" \/>\n\t<meta property=\"og:image:height\" content=\"194\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Uday Kawar\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@shmula\" \/>\n<meta name=\"twitter:site\" content=\"@shmula\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Uday Kawar\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/staging.opexlearning.com\/resources\/who-is-panda-in-the-google-content-farm-update\/8592\/\",\"url\":\"https:\/\/staging.opexlearning.com\/resources\/who-is-panda-in-the-google-content-farm-update\/8592\/\",\"name\":\"Who is Panda in the Google Panda Update?\",\"isPartOf\":{\"@id\":\"https:\/\/staging.opexlearning.com\/resources\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/staging.opexlearning.com\/resources\/who-is-panda-in-the-google-content-farm-update\/8592\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/staging.opexlearning.com\/resources\/who-is-panda-in-the-google-content-farm-update\/8592\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/staging.opexlearning.com\/resources\/wp-content\/uploads\/2011\/05\/navneet-panda-google-farmer-panda-update-thumb.jpg\",\"datePublished\":\"2011-05-09T20:17:12+00:00\",\"dateModified\":\"2016-12-25T04:36:51+00:00\",\"author\":{\"@id\":\"https:\/\/staging.opexlearning.com\/resources\/#\/schema\/person\/9335b5223b67189b35bda7d6be11c3fd\"},\"description\":\"Who is Panda in the Google Panda Update? 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