�ݺ�ߣshows by User: j

�ݺ�ߣshows by User: j_singh / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: j_singh / Mon, 16 Mar 2015 18:30:35 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: j_singh OpenLSH - a framework for locality sensitive hashing /slideshow/open-lsh-a-framework-for-locality-sensitive-hashing-45912645/45912645 openlsh-aframeworkforlocalitysensitivehashing-150316183035-conversion-gate01
Updated presentation on OpenLSH, an open source framework for Locality Sensitive Hashing.]]>
Updated presentation on OpenLSH, an open source framework for Locality Sensitive Hashing.]]> Mon, 16 Mar 2015 18:30:35 GMT /slideshow/open-lsh-a-framework-for-locality-sensitive-hashing-45912645/45912645 j_singh@slideshare.net(j_singh) OpenLSH - a framework for locality sensitive hashing j_singh Updated presentation on OpenLSH, an open source framework for Locality Sensitive Hashing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/openlsh-aframeworkforlocalitysensitivehashing-150316183035-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updated presentation on OpenLSH, an open source framework for Locality Sensitive Hashing.

OpenLSH - a framework for locality sensitive hashing from J Singh

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0 Designing analytics for big data /slideshow/designing-analytics-for-big-data/41278731 designinganalyticsforbigdata-141107183845-conversion-gate01
Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing]]>
Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing]]> Fri, 07 Nov 2014 18:38:45 GMT /slideshow/designing-analytics-for-big-data/41278731 j_singh@slideshare.net(j_singh) Designing analytics for big data j_singh Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/designinganalyticsforbigdata-141107183845-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing

Designing analytics for big data from J Singh

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0 Open LSH - september 2014 update /slideshow/open-lsh-september-2014-update-39101547/39101547 openlsh-september2014update-140915093044-phpapp01
We describe OpenLSH, an open source framework for Locality Sensitive Hashing.]]>
We describe OpenLSH, an open source framework for Locality Sensitive Hashing.]]> Mon, 15 Sep 2014 09:30:44 GMT /slideshow/open-lsh-september-2014-update-39101547/39101547 j_singh@slideshare.net(j_singh) Open LSH - september 2014 update j_singh We describe OpenLSH, an open source framework for Locality Sensitive Hashing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/openlsh-september2014update-140915093044-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> We describe OpenLSH, an open source framework for Locality Sensitive Hashing.

Open LSH - september 2014 update from J Singh

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0 PaaS - google app engine /slideshow/paa-s-google-app-engine/30015802 paas-googleappengine-140114151216-phpapp01
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PaaS - google app engine from J Singh

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0 Mining of massive datasets using locality sensitive hashing (LSH) /slideshow/mining-of-massive-datasets-using-locality-sensitive-hashing-lsh/29855502 miningofmassivedatasetsusinglocalitysensitivehashing-140109141824-phpapp01
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]]> Thu, 09 Jan 2014 14:18:24 GMT /slideshow/mining-of-massive-datasets-using-locality-sensitive-hashing-lsh/29855502 j_singh@slideshare.net(j_singh) Mining of massive datasets using locality sensitive hashing (LSH) j_singh <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/miningofmassivedatasetsusinglocalitysensitivehashing-140109141824-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Mining of massive datasets using locality sensitive hashing (LSH) from J Singh

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0 Data Analytic Technology Platforms: Options and Tradeoffs /slideshow/data-analytic-technology-platform-options-and-tradeoffs/29255822 dataanalytictechnologyplatformoptionsandtradeoffs-131216114837-phpapp02
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Data Analytic Technology Platforms: Options and Tradeoffs from J Singh

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0 Facebook Analytics with Elastic Map/Reduce /slideshow/map-reduce-workshop/15124731 mapreduceworkshop-121111105222-phpapp01
A workshop on analyzing data about Facebook likes of a set of people]]>
A workshop on analyzing data about Facebook likes of a set of people]]> Sun, 11 Nov 2012 10:52:21 GMT /slideshow/map-reduce-workshop/15124731 j_singh@slideshare.net(j_singh) Facebook Analytics with Elastic Map/Reduce j_singh A workshop on analyzing data about Facebook likes of a set of people <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mapreduceworkshop-121111105222-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> A workshop on analyzing data about Facebook likes of a set of people

Facebook Analytics with Elastic Map/Reduce from J Singh

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0 Big Data Laboratory /slideshow/big-data-laboratory/12046660 bigdatalaboratory-120317091029-phpapp01
Does it make sense to use Google App Engine as a quick prototyping environment for Big Data use cases? It would avoid all the hassles of setting up Hadoop and its bestiary. The answer is a definite "maybe".]]>
Does it make sense to use Google App Engine as a quick prototyping environment for Big Data use cases? It would avoid all the hassles of setting up Hadoop and its bestiary. The answer is a definite "maybe".]]> Sat, 17 Mar 2012 09:10:28 GMT /slideshow/big-data-laboratory/12046660 j_singh@slideshare.net(j_singh) Big Data Laboratory j_singh Does it make sense to use Google App Engine as a quick prototyping environment for Big Data use cases? It would avoid all the hassles of setting up Hadoop and its bestiary. The answer is a definite "maybe". <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatalaboratory-120317091029-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Does it make sense to use Google App Engine as a quick prototyping environment for Big Data use cases? It would avoid all the hassles of setting up Hadoop and its bestiary. The answer is a definite "maybe".

Big Data Laboratory from J Singh

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0 The Hadoop Ecosystem /j_singh/the-hadoop-ecosystem-11971869 hadoopecosystem-120312074148-phpapp02
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]]> Mon, 12 Mar 2012 07:41:48 GMT /j_singh/the-hadoop-ecosystem-11971869 j_singh@slideshare.net(j_singh) The Hadoop Ecosystem j_singh <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hadoopecosystem-120312074148-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

The Hadoop Ecosystem from J Singh

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0 Social Media Mining using GAE Map Reduce /slideshow/social-media-mining-using-gae-map-reduce/9775952 mapreducegae-111019150041-phpapp02
Map Reduce under Google App Engine offers capabilities to analyze and draw conclusions about data residing in social networks.]]>
Map Reduce under Google App Engine offers capabilities to analyze and draw conclusions about data residing in social networks.]]> Wed, 19 Oct 2011 15:00:36 GMT /slideshow/social-media-mining-using-gae-map-reduce/9775952 j_singh@slideshare.net(j_singh) Social Media Mining using GAE Map Reduce j_singh Map Reduce under Google App Engine offers capabilities to analyze and draw conclusions about data residing in social networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mapreducegae-111019150041-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Map Reduce under Google App Engine offers capabilities to analyze and draw conclusions about data residing in social networks.

Social Media Mining using GAE Map Reduce from J Singh

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0 High Throughput Data Analysis /slideshow/high-throughput-data-analysis/9542175 singhconfpresentationboston2011mongodb-111004085320-phpapp01
Receiving data from a source that produces 5-10 GBytes per hour, and presenting analysis results as the data streams in has some interesting challenges. We used MongoDB running on Amazon EC2 to house the data, map reduce to analyze it and Django-non-rel to present the results in near-real-time. (�ݺ�ߣs from my presentation at MongoDB Boston)]]>
Receiving data from a source that produces 5-10 GBytes per hour, and presenting analysis results as the data streams in has some interesting challenges. We used MongoDB running on Amazon EC2 to house the data, map reduce to analyze it and Django-non-rel to present the results in near-real-time. (�ݺ�ߣs from my presentation at MongoDB Boston)]]> Tue, 04 Oct 2011 08:53:17 GMT /slideshow/high-throughput-data-analysis/9542175 j_singh@slideshare.net(j_singh) High Throughput Data Analysis j_singh Receiving data from a source that produces 5-10 GBytes per hour, and presenting analysis results as the data streams in has some interesting challenges. We used MongoDB running on Amazon EC2 to house the data, map reduce to analyze it and Django-non-rel to present the results in near-real-time. (�ݺ�ߣs from my presentation at MongoDB Boston) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/singhconfpresentationboston2011mongodb-111004085320-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Receiving data from a source that produces 5-10 GBytes per hour, and presenting analysis results as the data streams in has some interesting challenges. We used MongoDB running on Amazon EC2 to house the data, map reduce to analyze it and Django-non-rel to present the results in near-real-time. (�ݺ�ߣs from my presentation at MongoDB Boston)

High Throughput Data Analysis from J Singh

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0 NoSQL and MapReduce /slideshow/nosql-and-mapreduce/8174831 nosql-110601101336-phpapp01
My presentation for TSSG, Chelmsford, MA]]>
My presentation for TSSG, Chelmsford, MA]]> Wed, 01 Jun 2011 10:13:30 GMT /slideshow/nosql-and-mapreduce/8174831 j_singh@slideshare.net(j_singh) NoSQL and MapReduce j_singh My presentation for TSSG, Chelmsford, MA <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosql-110601101336-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> My presentation for TSSG, Chelmsford, MA

NoSQL and MapReduce from J Singh

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0 CS 542 -- Concurrency Control, Distributed Commit /slideshow/cs-542-concurrency-control-distributed-commit/7587772 2011-04-11-110411083726-phpapp01
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CS 542 -- Concurrency Control, Distributed Commit from J Singh

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0 CS 542 -- Failure Recovery, Concurrency Control /j_singh/cs-542-failure-recovery-concurrency-control 2011-04-04-110404094215-phpapp02
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CS 542 -- Failure Recovery, Concurrency Control from J Singh

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0 CS 542 -- Query Optimization /slideshow/cs-542-query-optimization/7419114 2011-03-28-110328124145-phpapp01
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CS 542 -- Query Optimization from J Singh

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0 CS 542 -- Query Execution /slideshow/cs-542-query-execution/7336266 2011-03-21-110321121706-phpapp02
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CS 542 -- Query Execution from J Singh

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0 CS 542 Putting it all together -- Storage Management /slideshow/2011-0314-7258842/7258842 2011-03-14-110314084415-phpapp02
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CS 542 Putting it all together -- Storage Management from J Singh

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0 CS 542 Parallel DBs, NoSQL, MapReduce /slideshow/cs-542-parallel-dbs-nosql-mapreduce-6920451/6920451 2011-02-14-110214091750-phpapp02
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CS 542 Parallel DBs, NoSQL, MapReduce from J Singh

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0 CS 542 Database Index Structures /slideshow/database-index-structures/6842723 2011-02-07-110207150545-phpapp02
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CS 542 Database Index Structures from J Singh

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0 CS 542 Controlling Database Integrity and Performance /slideshow/cs-542-controlling-database-integrity-and-performance/6764941 2011-01-31-110131122629-phpapp01
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]]> Mon, 31 Jan 2011 12:24:25 GMT /slideshow/cs-542-controlling-database-integrity-and-performance/6764941 j_singh@slideshare.net(j_singh) CS 542 Controlling Database Integrity and Performance j_singh <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2011-01-31-110131122629-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

CS 542 Controlling Database Integrity and Performance from J Singh

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0 https://cdn.slidesharecdn.com/profile-photo-j_singh-48x48.jpg?cb=1727962217 Google App Engine and Amazon Web Services (and all-things-cloud) enthusiast involved in starting ultralight companies. Seasoned technology manager with a strong results orientation, specializing in Financial Services. History of building strong teams, delivering projects with high quality. PhD. in Electrical Engineering with 30 years experience. www.datathinks.org https://cdn.slidesharecdn.com/ss_thumbnails/openlsh-aframeworkforlocalitysensitivehashing-150316183035-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/open-lsh-a-framework-for-locality-sensitive-hashing-45912645/45912645 OpenLSH - a framework... https://cdn.slidesharecdn.com/ss_thumbnails/designinganalyticsforbigdata-141107183845-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/designing-analytics-for-big-data/41278731 Designing analytics fo... https://cdn.slidesharecdn.com/ss_thumbnails/openlsh-september2014update-140915093044-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/open-lsh-september-2014-update-39101547/39101547 Open LSH - september ...