�ݺ�ߣshows by User: tumra

�ݺ�ߣshows by User: tumra / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: tumra / Thu, 29 Jan 2015 09:40:33 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: tumra Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, London 2015-01-29) /slideshow/continuous-analytics-optimisation-using-apache-spark-big-data-analytics-london-20150129/44038242 2015-01-29continuousanalyticsoptimisationusingapachesparkv1-150129094034-conversion-gate02
Continuous Analytics & Optimisation using Apache Spark (Real-Time Analytics, London 2015-01-29) #UNICOMRTA]]>
Continuous Analytics & Optimisation using Apache Spark (Real-Time Analytics, London 2015-01-29) #UNICOMRTA]]> Thu, 29 Jan 2015 09:40:33 GMT /slideshow/continuous-analytics-optimisation-using-apache-spark-big-data-analytics-london-20150129/44038242 tumra@slideshare.net(tumra) Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, London 2015-01-29) tumra Continuous Analytics & Optimisation using Apache Spark (Real-Time Analytics, London 2015-01-29) #UNICOMRTA <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2015-01-29continuousanalyticsoptimisationusingapachesparkv1-150129094034-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Continuous Analytics & Optimisation using Apache Spark (Real-Time Analytics, London 2015-01-29) #UNICOMRTA

Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, London 2015-01-29) from TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science

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1 Clickstream & Social Media Analysis using Apache Spark /slideshow/clickstream-social-media-analysis-use-cases-and-examples-using-apache-spark/41791923 2014-11-17clickstreamsocialmediaanalysiswithapachespark-141120023949-conversion-gate01
Use cases and examples using Apache Spark, presented at the Hadoop User Group (UK) November 2014 Hadoop Meetup http://www.meetup.com/hadoop-users-group-uk/events/217791892/]]>
Use cases and examples using Apache Spark, presented at the Hadoop User Group (UK) November 2014 Hadoop Meetup http://www.meetup.com/hadoop-users-group-uk/events/217791892/]]> Thu, 20 Nov 2014 02:39:48 GMT /slideshow/clickstream-social-media-analysis-use-cases-and-examples-using-apache-spark/41791923 tumra@slideshare.net(tumra) Clickstream & Social Media Analysis using Apache Spark tumra Use cases and examples using Apache Spark, presented at the Hadoop User Group (UK) November 2014 Hadoop Meetup http://www.meetup.com/hadoop-users-group-uk/events/217791892/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2014-11-17clickstreamsocialmediaanalysiswithapachespark-141120023949-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Use cases and examples using Apache Spark, presented at the Hadoop User Group (UK) November 2014 Hadoop Meetup http://www.meetup.com/hadoop-users-group-uk/events/217791892/

Clickstream & Social Media Analysis using Apache Spark from TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science

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1 What's next for Big Data? -- Apache Spark /slideshow/whats-next-for-big-data-apache-spark/34412156 tumra-spark2014-05-07-140507181320-phpapp01
Michael Cutler (CTO cofounder of TUMRA) provides a high-level introduction to Apache Spark in a presentation given at ‘Big Data Week 2014’ #BDW14 held at University College London. TUMRA were early adopters of Spark after a brief PoC in Dec ‘12 and took it to production just a few months later. The main motivation to do so was the inflexibility and high-latency of Hadoop Map/Reduce jobs and the knock-on effect for technology that utilises it (Mahout machine learning, Hive data warehousing, Cascading). With two primary uses case ‘Ecommerce Personalisation’ and ‘Marketing Automation’ TUMRA are currently flowing around 29 million ‘user engagement events’ (JSON) each day through Apache Kafka and Spark Streaming at peak rates of up to 800 events per second. TUMRA use Apache Spark on Amazon Web Services (EC2) in production for a mix of machine learning model building, graph analytics and near-real-time reporting. To learn more about how we use Spark and the services we can deliver through our Platform please contact: hello@tumra.com ]]>
Michael Cutler (CTO cofounder of TUMRA) provides a high-level introduction to Apache Spark in a presentation given at ‘Big Data Week 2014’ #BDW14 held at University College London. TUMRA were early adopters of Spark after a brief PoC in Dec ‘12 and took it to production just a few months later. The main motivation to do so was the inflexibility and high-latency of Hadoop Map/Reduce jobs and the knock-on effect for technology that utilises it (Mahout machine learning, Hive data warehousing, Cascading). With two primary uses case ‘Ecommerce Personalisation’ and ‘Marketing Automation’ TUMRA are currently flowing around 29 million ‘user engagement events’ (JSON) each day through Apache Kafka and Spark Streaming at peak rates of up to 800 events per second. TUMRA use Apache Spark on Amazon Web Services (EC2) in production for a mix of machine learning model building, graph analytics and near-real-time reporting. To learn more about how we use Spark and the services we can deliver through our Platform please contact: hello@tumra.com ]]> Wed, 07 May 2014 18:13:20 GMT /slideshow/whats-next-for-big-data-apache-spark/34412156 tumra@slideshare.net(tumra) What's next for Big Data? -- Apache Spark tumra Michael Cutler (CTO cofounder of TUMRA) provides a high-level introduction to Apache Spark in a presentation given at ‘Big Data Week 2014’ #BDW14 held at University College London. TUMRA were early adopters of Spark after a brief PoC in Dec ‘12 and took it to production just a few months later. The main motivation to do so was the inflexibility and high-latency of Hadoop Map/Reduce jobs and the knock-on effect for technology that utilises it (Mahout machine learning, Hive data warehousing, Cascading). With two primary uses case ‘Ecommerce Personalisation’ and ‘Marketing Automation’ TUMRA are currently flowing around 29 million ‘user engagement events’ (JSON) each day through Apache Kafka and Spark Streaming at peak rates of up to 800 events per second. TUMRA use Apache Spark on Amazon Web Services (EC2) in production for a mix of machine learning model building, graph analytics and near-real-time reporting. To learn more about how we use Spark and the services we can deliver through our Platform please contact: hello@tumra.com <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tumra-spark2014-05-07-140507181320-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Michael Cutler (CTO cofounder of TUMRA) provides a high-level introduction to Apache Spark in a presentation given at ‘Big Data Week 2014’ #BDW14 held at University College London. TUMRA were early adopters of Spark after a brief PoC in Dec ‘12 and took it to production just a few months later. The main motivation to do so was the inflexibility and high-latency of Hadoop Map/Reduce jobs and the knock-on effect for technology that utilises it (Mahout machine learning, Hive data warehousing, Cascading). With two primary uses case ‘Ecommerce Personalisation’ and ‘Marketing Automation’ TUMRA are currently flowing around 29 million ‘user engagement events’ (JSON) each day through Apache Kafka and Spark Streaming at peak rates of up to 800 events per second. TUMRA use Apache Spark on Amazon Web Services (EC2) in production for a mix of machine learning model building, graph analytics and near-real-time reporting. To learn more about how we use Spark and the services we can deliver through our Platform please contact: hello@tumra.com

What's next for Big Data? -- Apache Spark from TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science

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1 Using Graph theory to understand Intent & Concepts - Neo4j User Group (January 2013) /slideshow/using-graph-theory-to-understand-intent-concepts-neo4j-user-group-january-2013/16263363 2013-01-30usinggraphtheorytounderstandintentconcepts-neo4jusergroup-130130163557-phpapp01
Title: Using Graph Theory to understand User Intent Subtitle: Graph-based Natural Language Processing applied to real-time Machine Learning Abstract: We are in a Graph Renaissance period. The advent of high-performance free/open-source software combined with inexpensive Cloud computing platforms enable graphs of information to be manipulated and utilised at scales never before seen. While use-cases like mining social and web data with graphs are common-place, their use in Natural Language Processing has largely been overlooked. In this presentation Michael Cutler will describe how TUMRA have used graph-based NLP algorithms as a core component of their upcoming digital marketing product TUMRA Optimize. Presenter: Michael Cutler Bio: Michael is the CTO co-founder of TUMRA, a Data Science startup based in Chiswick, West London. First discovering Hadoop back in 2008, Michael has been following the bleeding edge of ‘Big Data’ technology since before it was called ‘Big Data’ and has applied it to solve real-world problems. Before starting TUMRA, Michael was a senior researcher in the R&D labs for British Sky Broadcasting, inventing new technologies and solutions for everything from Satellite, Video and Network systems through to Web and Mobile-based applications. Website: http://tumra.com http://cotdp.com Twitter: @tumra @cotdp]]>
Title: Using Graph Theory to understand User Intent Subtitle: Graph-based Natural Language Processing applied to real-time Machine Learning Abstract: We are in a Graph Renaissance period. The advent of high-performance free/open-source software combined with inexpensive Cloud computing platforms enable graphs of information to be manipulated and utilised at scales never before seen. While use-cases like mining social and web data with graphs are common-place, their use in Natural Language Processing has largely been overlooked. In this presentation Michael Cutler will describe how TUMRA have used graph-based NLP algorithms as a core component of their upcoming digital marketing product TUMRA Optimize. Presenter: Michael Cutler Bio: Michael is the CTO co-founder of TUMRA, a Data Science startup based in Chiswick, West London. First discovering Hadoop back in 2008, Michael has been following the bleeding edge of ‘Big Data’ technology since before it was called ‘Big Data’ and has applied it to solve real-world problems. Before starting TUMRA, Michael was a senior researcher in the R&D labs for British Sky Broadcasting, inventing new technologies and solutions for everything from Satellite, Video and Network systems through to Web and Mobile-based applications. Website: http://tumra.com http://cotdp.com Twitter: @tumra @cotdp]]> Wed, 30 Jan 2013 16:35:57 GMT /slideshow/using-graph-theory-to-understand-intent-concepts-neo4j-user-group-january-2013/16263363 tumra@slideshare.net(tumra) Using Graph theory to understand Intent & Concepts - Neo4j User Group (January 2013) tumra Title: Using Graph Theory to understand User Intent Subtitle: Graph-based Natural Language Processing applied to real-time Machine Learning Abstract: We are in a Graph Renaissance period. The advent of high-performance free/open-source software combined with inexpensive Cloud computing platforms enable graphs of information to be manipulated and utilised at scales never before seen. While use-cases like mining social and web data with graphs are common-place, their use in Natural Language Processing has largely been overlooked. In this presentation Michael Cutler will describe how TUMRA have used graph-based NLP algorithms as a core component of their upcoming digital marketing product TUMRA Optimize. Presenter: Michael Cutler Bio: Michael is the CTO co-founder of TUMRA, a Data Science startup based in Chiswick, West London. First discovering Hadoop back in 2008, Michael has been following the bleeding edge of ‘Big Data’ technology since before it was called ‘Big Data’ and has applied it to solve real-world problems. Before starting TUMRA, Michael was a senior researcher in the R&D labs for British Sky Broadcasting, inventing new technologies and solutions for everything from Satellite, Video and Network systems through to Web and Mobile-based applications. Website: http://tumra.com http://cotdp.com Twitter: @tumra @cotdp <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2013-01-30usinggraphtheorytounderstandintentconcepts-neo4jusergroup-130130163557-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Title: Using Graph Theory to understand User Intent Subtitle: Graph-based Natural Language Processing applied to real-time Machine Learning Abstract: We are in a Graph Renaissance period. The advent of high-performance free/open-source software combined with inexpensive Cloud computing platforms enable graphs of information to be manipulated and utilised at scales never before seen. While use-cases like mining social and web data with graphs are common-place, their use in Natural Language Processing has largely been overlooked. In this presentation Michael Cutler will describe how TUMRA have used graph-based NLP algorithms as a core component of their upcoming digital marketing product TUMRA Optimize. Presenter: Michael Cutler Bio: Michael is the CTO co-founder of TUMRA, a Data Science startup based in Chiswick, West London. First discovering Hadoop back in 2008, Michael has been following the bleeding edge of ‘Big Data’ technology since before it was called ‘Big Data’ and has applied it to solve real-world problems. Before starting TUMRA, Michael was a senior researcher in the R&D labs for British Sky Broadcasting, inventing new technologies and solutions for everything from Satellite, Video and Network systems through to Web and Mobile-based applications. Website: http://tumra.com http://cotdp.com Twitter: @tumra @cotdp

Using Graph theory to understand Intent & Concepts - Neo4j User Group (January 2013) from TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science

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1 Surviving & Monetising the Data Armageddon (Total Telecom World 2012 - 13th Nov 2012) /slideshow/surviving-monetising-the-data-armageddon-total-telecom-world-2012-13th-nov-2012/15176434 survivingmonetisingthedataarmageddontotaltelecomworld2012-13thnov2012-121114110942-phpapp01
Total Telecom World 2012 - London How can operators capitalise on the subscriber information they hold? They know who their customers are, where they are and what they are doing, so how can they make money from this? In this presentation Paul Kullich (CEO @ TUMRA) describes some of the challenges around the handling of customer data, as well as the possibilities of using new technologies like Big Data and Data Science to unlock new sources of revenue from your existing subscribers.]]>
Total Telecom World 2012 - London How can operators capitalise on the subscriber information they hold? They know who their customers are, where they are and what they are doing, so how can they make money from this? In this presentation Paul Kullich (CEO @ TUMRA) describes some of the challenges around the handling of customer data, as well as the possibilities of using new technologies like Big Data and Data Science to unlock new sources of revenue from your existing subscribers.]]> Wed, 14 Nov 2012 11:09:41 GMT /slideshow/surviving-monetising-the-data-armageddon-total-telecom-world-2012-13th-nov-2012/15176434 tumra@slideshare.net(tumra) Surviving & Monetising the Data Armageddon (Total Telecom World 2012 - 13th Nov 2012) tumra Total Telecom World 2012 - London How can operators capitalise on the subscriber information they hold? They know who their customers are, where they are and what they are doing, so how can they make money from this? In this presentation Paul Kullich (CEO @ TUMRA) describes some of the challenges around the handling of customer data, as well as the possibilities of using new technologies like Big Data and Data Science to unlock new sources of revenue from your existing subscribers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/survivingmonetisingthedataarmageddontotaltelecomworld2012-13thnov2012-121114110942-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Total Telecom World 2012 - London How can operators capitalise on the subscriber information they hold? They know who their customers are, where they are and what they are doing, so how can they make money from this? In this presentation Paul Kullich (CEO @ TUMRA) describes some of the challenges around the handling of customer data, as well as the possibilities of using new technologies like Big Data and Data Science to unlock new sources of revenue from your existing subscribers.

Surviving & Monetising the Data Armageddon (Total Telecom World 2012 - 13th Nov 2012) from TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science

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1 Real-Time Machine Learning at Industrial scale (University of Oxford, 9th Oct 2012) /slideshow/real-time-machine-learning-at-industrial-scale-9th-oct/14655020 real-timemachinelearningatindustrialscale9thoct-121009122436-phpapp02
Right now in institutions around the world, some of the greatest minds in computer science and statistics are coming up with amazing new algorithms and mathematically beautiful solutions. However it's entirely possible that the solutions they conceive will be impracticable in industry. The reason is simple; "the best answer is useless if it arrives too late to do anything with it". The key principle here is the compromise between 'accuracy' and 'latency'. In this talk I will describe examples where this holds true, and how I am using real-time machine learning models to solve challenges in eCommerce, Financial Services and Media companies. http://tumra.com/blog/real-time-machine-learning-at-industrial-scale]]>
Right now in institutions around the world, some of the greatest minds in computer science and statistics are coming up with amazing new algorithms and mathematically beautiful solutions. However it's entirely possible that the solutions they conceive will be impracticable in industry. The reason is simple; "the best answer is useless if it arrives too late to do anything with it". The key principle here is the compromise between 'accuracy' and 'latency'. In this talk I will describe examples where this holds true, and how I am using real-time machine learning models to solve challenges in eCommerce, Financial Services and Media companies. http://tumra.com/blog/real-time-machine-learning-at-industrial-scale]]> Tue, 09 Oct 2012 12:24:33 GMT /slideshow/real-time-machine-learning-at-industrial-scale-9th-oct/14655020 tumra@slideshare.net(tumra) Real-Time Machine Learning at Industrial scale (University of Oxford, 9th Oct 2012) tumra Right now in institutions around the world, some of the greatest minds in computer science and statistics are coming up with amazing new algorithms and mathematically beautiful solutions. However it's entirely possible that the solutions they conceive will be impracticable in industry. The reason is simple; "the best answer is useless if it arrives too late to do anything with it". The key principle here is the compromise between 'accuracy' and 'latency'. In this talk I will describe examples where this holds true, and how I am using real-time machine learning models to solve challenges in eCommerce, Financial Services and Media companies. http://tumra.com/blog/real-time-machine-learning-at-industrial-scale <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/real-timemachinelearningatindustrialscale9thoct-121009122436-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Right now in institutions around the world, some of the greatest minds in computer science and statistics are coming up with amazing new algorithms and mathematically beautiful solutions. However it's entirely possible that the solutions they conceive will be impracticable in industry. The reason is simple; "the best answer is useless if it arrives too late to do anything with it". The key principle here is the compromise between 'accuracy' and 'latency'. In this talk I will describe examples where this holds true, and how I am using real-time machine learning models to solve challenges in eCommerce, Financial Services and Media companies. http://tumra.com/blog/real-time-machine-learning-at-industrial-scale

Real-Time Machine Learning at Industrial scale (University of Oxford, 9th Oct 2012) from TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science

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1 https://cdn.slidesharecdn.com/profile-photo-tumra-48x48.jpg?cb=1523444525 We combine our business experience with specialist expertise in large scale data analysis, machine learning and visualisation to solve complex problems and support our clients in making better data driven decisions. tumra.com https://cdn.slidesharecdn.com/ss_thumbnails/2015-01-29continuousanalyticsoptimisationusingapachesparkv1-150129094034-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/continuous-analytics-optimisation-using-apache-spark-big-data-analytics-london-20150129/44038242 Continuous Analytics &... https://cdn.slidesharecdn.com/ss_thumbnails/2014-11-17clickstreamsocialmediaanalysiswithapachespark-141120023949-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/clickstream-social-media-analysis-use-cases-and-examples-using-apache-spark/41791923 Clickstream & Social M... https://cdn.slidesharecdn.com/ss_thumbnails/tumra-spark2014-05-07-140507181320-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/whats-next-for-big-data-apache-spark/34412156 What's next for Big Da...