際際滷shows by User: OReillyStrata / http://www.slideshare.net/images/logo.gif 際際滷shows by User: OReillyStrata / Fri, 15 Nov 2013 07:30:59 GMT 際際滷Share feed for 際際滷shows by User: OReillyStrata Dealing with Uncertainty: What the reverend Bayes can teach us. /slideshow/final-28280777/28280777 final-131115073059-phpapp01
By Jurgen Van Gael - http://jvangael.github.io/ - @jvangael As data scientists and decision makers, uncertainty is all around us: data is noisy, missing, wrong or inherently uncertain. Statistics offers a wide set of theories and tools to deal with this uncertainty, yet most people are unaware of a unifying theory of uncertainty. In this talk I want to introduce the audience to a branch of statistics called Bayesian reasoning which is a unifying, consistent, logical and most importantly successful way of dealing with uncertainty. Over the past two centuries there have been many proposals for dealing with uncertainty (e.g. frequentist probabilities, fuzzy logic, ...). Under the influence of early 20th century statisticians, the Bayesian formalism was somewhat pushed into the background of the statistical scene. More recently though, some to the credit of computer science, Bayesian thinking has seen a revival. So what and how much should a data scientist or decision maker know about Bayesian thinking? My talk will consist of four different parts. In the first part, I will explain the central dogma of Bayesian thinking: Bayes Rule. This simple equation (4 variables, one multiplication and one division!) describes how we should update our beliefs about the world in light of new data. I will discuss evidence from neuroscience and psychology that the brain uses Bayesian mechanism to reason about the world. Unfortunately, sometimes the brain fails miserably at taking all the variables of Bayes rule into account. This leads to the second part of the talk where I will illustrate Bayes rule as a tool for decision makers to reason about uncertainty. In the third part of the talk I will give an example of how we can build machine learning systems around Bayes rule. The key idea here is that Bayes rule allows us to keep track of uncertainty about the world. In this part I will illustrate one a Bayesian machine learning system in action. In the final part of the talk I will introduce the concept of Probabilistic Programming. Probabilistic programming is a new embryonic programming paradigm that introduces uncertain variables as a first class citizen of a programming language and then uses Bayes rule to execute the programs. When we look at machine learning conferences in the last few years, the Bayesian framework has been prominent. In this talk I want to help the audience understand how the Bayesian framework can help them in their data mining and decision making processes. If people leave the talk thinking Bayes rule is the E=MC^2 of data science, I will consider the presentation a success.]]>
By Jurgen Van Gael - http://jvangael.github.io/ - @jvangael As data scientists and decision makers, uncertainty is all around us: data is noisy, missing, wrong or inherently uncertain. Statistics offers a wide set of theories and tools to deal with this uncertainty, yet most people are unaware of a unifying theory of uncertainty. In this talk I want to introduce the audience to a branch of statistics called Bayesian reasoning which is a unifying, consistent, logical and most importantly successful way of dealing with uncertainty. Over the past two centuries there have been many proposals for dealing with uncertainty (e.g. frequentist probabilities, fuzzy logic, ...). Under the influence of early 20th century statisticians, the Bayesian formalism was somewhat pushed into the background of the statistical scene. More recently though, some to the credit of computer science, Bayesian thinking has seen a revival. So what and how much should a data scientist or decision maker know about Bayesian thinking? My talk will consist of four different parts. In the first part, I will explain the central dogma of Bayesian thinking: Bayes Rule. This simple equation (4 variables, one multiplication and one division!) describes how we should update our beliefs about the world in light of new data. I will discuss evidence from neuroscience and psychology that the brain uses Bayesian mechanism to reason about the world. Unfortunately, sometimes the brain fails miserably at taking all the variables of Bayes rule into account. This leads to the second part of the talk where I will illustrate Bayes rule as a tool for decision makers to reason about uncertainty. In the third part of the talk I will give an example of how we can build machine learning systems around Bayes rule. The key idea here is that Bayes rule allows us to keep track of uncertainty about the world. In this part I will illustrate one a Bayesian machine learning system in action. In the final part of the talk I will introduce the concept of Probabilistic Programming. Probabilistic programming is a new embryonic programming paradigm that introduces uncertain variables as a first class citizen of a programming language and then uses Bayes rule to execute the programs. When we look at machine learning conferences in the last few years, the Bayesian framework has been prominent. In this talk I want to help the audience understand how the Bayesian framework can help them in their data mining and decision making processes. If people leave the talk thinking Bayes rule is the E=MC^2 of data science, I will consider the presentation a success.]]> Fri, 15 Nov 2013 07:30:59 GMT /slideshow/final-28280777/28280777 OReillyStrata@slideshare.net(OReillyStrata) Dealing with Uncertainty: What the reverend Bayes can teach us. OReillyStrata By Jurgen Van Gael - http://jvangael.github.io/ - @jvangael As data scientists and decision makers, uncertainty is all around us: data is noisy, missing, wrong or inherently uncertain. Statistics offers a wide set of theories and tools to deal with this uncertainty, yet most people are unaware of a unifying theory of uncertainty. In this talk I want to introduce the audience to a branch of statistics called Bayesian reasoning which is a unifying, consistent, logical and most importantly successful way of dealing with uncertainty. Over the past two centuries there have been many proposals for dealing with uncertainty (e.g. frequentist probabilities, fuzzy logic, ...). Under the influence of early 20th century statisticians, the Bayesian formalism was somewhat pushed into the background of the statistical scene. More recently though, some to the credit of computer science, Bayesian thinking has seen a revival. So what and how much should a data scientist or decision maker know about Bayesian thinking? My talk will consist of four different parts. In the first part, I will explain the central dogma of Bayesian thinking: Bayes Rule. This simple equation (4 variables, one multiplication and one division!) describes how we should update our beliefs about the world in light of new data. I will discuss evidence from neuroscience and psychology that the brain uses Bayesian mechanism to reason about the world. Unfortunately, sometimes the brain fails miserably at taking all the variables of Bayes rule into account. This leads to the second part of the talk where I will illustrate Bayes rule as a tool for decision makers to reason about uncertainty. In the third part of the talk I will give an example of how we can build machine learning systems around Bayes rule. The key idea here is that Bayes rule allows us to keep track of uncertainty about the world. In this part I will illustrate one a Bayesian machine learning system in action. In the final part of the talk I will introduce the concept of Probabilistic Programming. Probabilistic programming is a new embryonic programming paradigm that introduces uncertain variables as a first class citizen of a programming language and then uses Bayes rule to execute the programs. When we look at machine learning conferences in the last few years, the Bayesian framework has been prominent. In this talk I want to help the audience understand how the Bayesian framework can help them in their data mining and decision making processes. If people leave the talk thinking Bayes rule is the E=MC^2 of data science, I will consider the presentation a success. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/final-131115073059-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> By Jurgen Van Gael - http://jvangael.github.io/ - @jvangael As data scientists and decision makers, uncertainty is all around us: data is noisy, missing, wrong or inherently uncertain. Statistics offers a wide set of theories and tools to deal with this uncertainty, yet most people are unaware of a unifying theory of uncertainty. In this talk I want to introduce the audience to a branch of statistics called Bayesian reasoning which is a unifying, consistent, logical and most importantly successful way of dealing with uncertainty. Over the past two centuries there have been many proposals for dealing with uncertainty (e.g. frequentist probabilities, fuzzy logic, ...). Under the influence of early 20th century statisticians, the Bayesian formalism was somewhat pushed into the background of the statistical scene. More recently though, some to the credit of computer science, Bayesian thinking has seen a revival. So what and how much should a data scientist or decision maker know about Bayesian thinking? My talk will consist of four different parts. In the first part, I will explain the central dogma of Bayesian thinking: Bayes Rule. This simple equation (4 variables, one multiplication and one division!) describes how we should update our beliefs about the world in light of new data. I will discuss evidence from neuroscience and psychology that the brain uses Bayesian mechanism to reason about the world. Unfortunately, sometimes the brain fails miserably at taking all the variables of Bayes rule into account. This leads to the second part of the talk where I will illustrate Bayes rule as a tool for decision makers to reason about uncertainty. In the third part of the talk I will give an example of how we can build machine learning systems around Bayes rule. The key idea here is that Bayes rule allows us to keep track of uncertainty about the world. In this part I will illustrate one a Bayesian machine learning system in action. In the final part of the talk I will introduce the concept of Probabilistic Programming. Probabilistic programming is a new embryonic programming paradigm that introduces uncertain variables as a first class citizen of a programming language and then uses Bayes rule to execute the programs. When we look at machine learning conferences in the last few years, the Bayesian framework has been prominent. In this talk I want to help the audience understand how the Bayesian framework can help them in their data mining and decision making processes. If people leave the talk thinking Bayes rule is the E=MC^2 of data science, I will consider the presentation a success.
Dealing with Uncertainty: What the reverend Bayes can teach us. from OReillyStrata
]]> 3368 6 https://cdn.slidesharecdn.com/ss_thumbnails/final-131115073059-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SapientNitro Strata_presentation_upload /slideshow/sapientnitro-stratapresentationupload/28245695 sapientnitrostratapresentationupload-131114094519-phpapp01
PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience.]]>
PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience.]]> Thu, 14 Nov 2013 09:45:19 GMT /slideshow/sapientnitro-stratapresentationupload/28245695 OReillyStrata@slideshare.net(OReillyStrata) SapientNitro Strata_presentation_upload OReillyStrata PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sapientnitrostratapresentationupload-131114094519-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience.
SapientNitro Strata_presentation_upload from OReillyStrata
]]> 8251 6 https://cdn.slidesharecdn.com/ss_thumbnails/sapientnitrostratapresentationupload-131114094519-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Digital analytics & privacy: it's not the end of the world /slideshow/strata-uk-aureliepolsprivacy/28195852 strataukaureliepolsprivacy-131113055548-phpapp01
This presentation starts by revisiting the common best practices related to digital analytics in order to measure digital assets effectiveness to increase conversion, common data feeds between tools and possibly data flows between continents for analysis. These practices are then put in parallel with legal requirements, showing which steps need to be undertaken to assure legal compliance of said practices, how digital responsibles should be trained in data protection matters and what contracts are needed with both data providers & collectors so as to assure minimal liability for these routinely undertaken tasks. This presentation is NOT about security and goes beyond the over-blown cookie debate in order to highlight how the upcoming EU Personal Data Protection Regulation will influence digital analytics to hopefully start embracing Privacy by Design ways of working.]]>
This presentation starts by revisiting the common best practices related to digital analytics in order to measure digital assets effectiveness to increase conversion, common data feeds between tools and possibly data flows between continents for analysis. These practices are then put in parallel with legal requirements, showing which steps need to be undertaken to assure legal compliance of said practices, how digital responsibles should be trained in data protection matters and what contracts are needed with both data providers & collectors so as to assure minimal liability for these routinely undertaken tasks. This presentation is NOT about security and goes beyond the over-blown cookie debate in order to highlight how the upcoming EU Personal Data Protection Regulation will influence digital analytics to hopefully start embracing Privacy by Design ways of working.]]> Wed, 13 Nov 2013 05:55:48 GMT /slideshow/strata-uk-aureliepolsprivacy/28195852 OReillyStrata@slideshare.net(OReillyStrata) Digital analytics & privacy: it's not the end of the world OReillyStrata This presentation starts by revisiting the common best practices related to digital analytics in order to measure digital assets effectiveness to increase conversion, common data feeds between tools and possibly data flows between continents for analysis. These practices are then put in parallel with legal requirements, showing which steps need to be undertaken to assure legal compliance of said practices, how digital responsibles should be trained in data protection matters and what contracts are needed with both data providers & collectors so as to assure minimal liability for these routinely undertaken tasks. This presentation is NOT about security and goes beyond the over-blown cookie debate in order to highlight how the upcoming EU Personal Data Protection Regulation will influence digital analytics to hopefully start embracing Privacy by Design ways of working. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/strataukaureliepolsprivacy-131113055548-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation starts by revisiting the common best practices related to digital analytics in order to measure digital assets effectiveness to increase conversion, common data feeds between tools and possibly data flows between continents for analysis. These practices are then put in parallel with legal requirements, showing which steps need to be undertaken to assure legal compliance of said practices, how digital responsibles should be trained in data protection matters and what contracts are needed with both data providers &amp; collectors so as to assure minimal liability for these routinely undertaken tasks. This presentation is NOT about security and goes beyond the over-blown cookie debate in order to highlight how the upcoming EU Personal Data Protection Regulation will influence digital analytics to hopefully start embracing Privacy by Design ways of working.
Digital analytics & privacy: it's not the end of the world from OReillyStrata
]]> 3491 4 https://cdn.slidesharecdn.com/ss_thumbnails/strataukaureliepolsprivacy-131113055548-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Giving Organisations new capabilities to ask the right business questions 1.7 /slideshow/giving-organisations-new-capabilities-to-ask-the-right-business-questions-17/28120155 askingtherightbusinessquestion1-131111093534-phpapp01
This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises]]>
This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises]]> Mon, 11 Nov 2013 09:35:33 GMT /slideshow/giving-organisations-new-capabilities-to-ask-the-right-business-questions-17/28120155 OReillyStrata@slideshare.net(OReillyStrata) Giving Organisations new capabilities to ask the right business questions 1.7 OReillyStrata This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/askingtherightbusinessquestion1-131111093534-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises
Giving Organisations new capabilities to ask the right business questions 1.7 from OReillyStrata
]]> 1351 4 https://cdn.slidesharecdn.com/ss_thumbnails/askingtherightbusinessquestion1-131111093534-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data as an Art Material. Case study: The Open Data Institute /slideshow/data-as-an-art-material-case-study-the-open-data-institute/28120123 jfreeman201310-131111093446-phpapp01
The Open Data Institute (ODI) sees the creative use of data as an intrinsic and essential part of our cultural landscape. As part of its ongoing operations, the it has an Art Programme committed to facilitating artists in the exhibition and creation of works which translate data into something that is meaningful to peoples lives. Artists use data as an art material in many ways: materialising them physically, sonifying them to amplify natural phenomena, coalescing them to create new realities. They question how objective the treatment of data is, and how much truth do we expect from an artwork with statistical roots? And we are asked to consider whether it matters. If we accept that there is dogma in the artists code, do we accept that it plays a part in other code too? Often at the critical edge of technological debate, artists are redefining how we perceive data and how it affects and reflects our lives. This presentation will showcase art curated for the on-going Data as Culture programme, from concept through the development process to the final work, and present findings on how the art programme has impacted the ODI, its visitors and its staff. By Julie Freeman]]>
The Open Data Institute (ODI) sees the creative use of data as an intrinsic and essential part of our cultural landscape. As part of its ongoing operations, the it has an Art Programme committed to facilitating artists in the exhibition and creation of works which translate data into something that is meaningful to peoples lives. Artists use data as an art material in many ways: materialising them physically, sonifying them to amplify natural phenomena, coalescing them to create new realities. They question how objective the treatment of data is, and how much truth do we expect from an artwork with statistical roots? And we are asked to consider whether it matters. If we accept that there is dogma in the artists code, do we accept that it plays a part in other code too? Often at the critical edge of technological debate, artists are redefining how we perceive data and how it affects and reflects our lives. This presentation will showcase art curated for the on-going Data as Culture programme, from concept through the development process to the final work, and present findings on how the art programme has impacted the ODI, its visitors and its staff. By Julie Freeman]]> Mon, 11 Nov 2013 09:34:46 GMT /slideshow/data-as-an-art-material-case-study-the-open-data-institute/28120123 OReillyStrata@slideshare.net(OReillyStrata) Data as an Art Material. Case study: The Open Data Institute OReillyStrata The Open Data Institute (ODI) sees the creative use of data as an intrinsic and essential part of our cultural landscape. As part of its ongoing operations, the it has an Art Programme committed to facilitating artists in the exhibition and creation of works which translate data into something that is meaningful to peoples lives. Artists use data as an art material in many ways: materialising them physically, sonifying them to amplify natural phenomena, coalescing them to create new realities. They question how objective the treatment of data is, and how much truth do we expect from an artwork with statistical roots? And we are asked to consider whether it matters. If we accept that there is dogma in the artists code, do we accept that it plays a part in other code too? Often at the critical edge of technological debate, artists are redefining how we perceive data and how it affects and reflects our lives. This presentation will showcase art curated for the on-going Data as Culture programme, from concept through the development process to the final work, and present findings on how the art programme has impacted the ODI, its visitors and its staff. By Julie Freeman <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jfreeman201310-131111093446-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The Open Data Institute (ODI) sees the creative use of data as an intrinsic and essential part of our cultural landscape. As part of its ongoing operations, the it has an Art Programme committed to facilitating artists in the exhibition and creation of works which translate data into something that is meaningful to peoples lives. Artists use data as an art material in many ways: materialising them physically, sonifying them to amplify natural phenomena, coalescing them to create new realities. They question how objective the treatment of data is, and how much truth do we expect from an artwork with statistical roots? And we are asked to consider whether it matters. If we accept that there is dogma in the artists code, do we accept that it plays a part in other code too? Often at the critical edge of technological debate, artists are redefining how we perceive data and how it affects and reflects our lives. This presentation will showcase art curated for the on-going Data as Culture programme, from concept through the development process to the final work, and present findings on how the art programme has impacted the ODI, its visitors and its staff. By Julie Freeman
Data as an Art Material. Case study: The Open Data Institute from OReillyStrata
]]> 1996 3 https://cdn.slidesharecdn.com/ss_thumbnails/jfreeman201310-131111093446-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Giving Organisations new Capabilities 鐃to ask the Right 鐃Business Questions /slideshow/giving-organisations-new-capabilities-to-ask-the-right-business-questions/28092834 askingtherightbusinessquestion1-131110143341-phpapp02
Strata London presentation on the use of Structured Analytic Techniques in business.]]>
Strata London presentation on the use of Structured Analytic Techniques in business.]]> Sun, 10 Nov 2013 14:33:41 GMT /slideshow/giving-organisations-new-capabilities-to-ask-the-right-business-questions/28092834 OReillyStrata@slideshare.net(OReillyStrata) Giving Organisations new Capabilities 鐃to ask the Right 鐃Business Questions OReillyStrata Strata London presentation on the use of Structured Analytic Techniques in business. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/askingtherightbusinessquestion1-131110143341-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Strata London presentation on the use of Structured Analytic Techniques in business.
Giving Organisations new Capabilities to ask the Right Business Questions from OReillyStrata
]]> 1272 5 https://cdn.slidesharecdn.com/ss_thumbnails/askingtherightbusinessquestion1-131110143341-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big Data for Big Power: How smart is the grid if the infrastructure is stupid? /slideshow/big-data-for-big-power-how-smart-is-the-grid-if-the-infrastructure-is-stupid/27934958 stratalondon2013-smartgrid-131105131721-phpapp01
Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley.]]>
Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley.]]> Tue, 05 Nov 2013 13:17:21 GMT /slideshow/big-data-for-big-power-how-smart-is-the-grid-if-the-infrastructure-is-stupid/27934958 OReillyStrata@slideshare.net(OReillyStrata) Big Data for Big Power: How smart is the grid if the infrastructure is stupid? OReillyStrata Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratalondon2013-smartgrid-131105131721-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley.
Big Data for Big Power: How smart is the grid if the infrastructure is stupid? from OReillyStrata
]]> 2325 3 https://cdn.slidesharecdn.com/ss_thumbnails/stratalondon2013-smartgrid-131105131721-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The Workflow Abstraction /slideshow/the-workflow-abstraction/16872228 strata2013-130301210944-phpapp01
Strata 2013 talk, "The Workflow Abstraction" by Paco Nathan.]]>
Strata 2013 talk, "The Workflow Abstraction" by Paco Nathan.]]> Fri, 01 Mar 2013 21:09:44 GMT /slideshow/the-workflow-abstraction/16872228 OReillyStrata@slideshare.net(OReillyStrata) The Workflow Abstraction OReillyStrata Strata 2013 talk, "The Workflow Abstraction" by Paco Nathan. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/strata2013-130301210944-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Strata 2013 talk, &quot;The Workflow Abstraction&quot; by Paco Nathan.
The Workflow Abstraction from OReillyStrata
]]> 8947 6 https://cdn.slidesharecdn.com/ss_thumbnails/strata2013-130301210944-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SQL on Hadoop: Defining the New Generation of Analytic SQL Databases /slideshow/sql-on-hadoop-defining-the-new-generation-of-analytic-databases/16869777 sqlonhadoop-definingthenewgenerationofanalyticdatabases-130301173658-phpapp01
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]]> Fri, 01 Mar 2013 17:36:58 GMT /slideshow/sql-on-hadoop-defining-the-new-generation-of-analytic-databases/16869777 OReillyStrata@slideshare.net(OReillyStrata) SQL on Hadoop: Defining the New Generation of Analytic SQL Databases OReillyStrata <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sqlonhadoop-definingthenewgenerationofanalyticdatabases-130301173658-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
SQL on Hadoop: Defining the New Generation of Analytic SQL Databases from OReillyStrata
]]> 3938 7 https://cdn.slidesharecdn.com/ss_thumbnails/sqlonhadoop-definingthenewgenerationofanalyticdatabases-130301173658-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The Future of Big Data is Relational (or why you can't escape SQL) /slideshow/the-future-of-big-data-is-relational-or-why-you-cant-escape-sql/16835215 futurebigdatarelational-130228093624-phpapp02
]]>
]]> Thu, 28 Feb 2013 09:36:24 GMT /slideshow/the-future-of-big-data-is-relational-or-why-you-cant-escape-sql/16835215 OReillyStrata@slideshare.net(OReillyStrata) The Future of Big Data is Relational (or why you can't escape SQL) OReillyStrata <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/futurebigdatarelational-130228093624-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
The Future of Big Data is Relational (or why you can't escape SQL) from OReillyStrata
]]> 4033 28 https://cdn.slidesharecdn.com/ss_thumbnails/futurebigdatarelational-130228093624-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Large scale ETL with Hadoop /slideshow/large-scale-etl-with-hadoop/14940437 largescaleetlwithhadoop-esammer-121029183210-phpapp01
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.]]>
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.]]> Mon, 29 Oct 2012 18:32:07 GMT /slideshow/large-scale-etl-with-hadoop/14940437 OReillyStrata@slideshare.net(OReillyStrata) Large scale ETL with Hadoop OReillyStrata Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/largescaleetlwithhadoop-esammer-121029183210-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.
Large scale ETL with Hadoop from OReillyStrata
]]> 32754 11 https://cdn.slidesharecdn.com/ss_thumbnails/largescaleetlwithhadoop-esammer-121029183210-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Visualizing Networks: Beyond the Hairball /slideshow/visualizing-networks-beyond-the-hairball/14913445 stratanetworksvis-121027141807-phpapp02
Strata NYC 2012 slides by Lynn Cherny; layout and analysis strategies for network data.]]>
Strata NYC 2012 slides by Lynn Cherny; layout and analysis strategies for network data.]]> Sat, 27 Oct 2012 14:18:04 GMT /slideshow/visualizing-networks-beyond-the-hairball/14913445 OReillyStrata@slideshare.net(OReillyStrata) Visualizing Networks: Beyond the Hairball OReillyStrata Strata NYC 2012 slides by Lynn Cherny; layout and analysis strategies for network data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratanetworksvis-121027141807-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Strata NYC 2012 slides by Lynn Cherny; layout and analysis strategies for network data.
Visualizing Networks: Beyond the Hairball from OReillyStrata
]]> 113198 60 https://cdn.slidesharecdn.com/ss_thumbnails/stratanetworksvis-121027141807-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Designing Big Data Interactions: The Language of Discovery /slideshow/designing-big-data-interactions-the-language-of-discovery/14900642 stratalanguagediscovery3short-121026103136-phpapp01
Looking deeper than the celebratory rhetoric of information quantity, at its core, Big Data makes possible unprecedented awareness and insight into every sphere of life; from business and politics, to the environment, arts and society. In this coming Age of Insight, discovery is not only the purview of specialized Data Scientists who create exotic visualizations of massive data sets, it is a fundamental category of human activity that is essential to everyday interactions between people, resources, and environments. To provide architects and designers with an effective starting point for creating satisfying and relevant user experiences that rely on discovery interactions, this session presents a simple analytical and generative toolkit for understanding how people conduct the broad range of discovery activities necessary in the information-permeated world. Specifically, this session will present: A simple, research-derived language for describing discovery needs and activities that spans domains, environments, media, and personas Observed and reusable patterns of discovery activities in individual and collaborative settings Examples of the architecture of successful discovery experiences at small and large scales A vocabulary and perspective for discovery as a critical individual and organizational capability Leading edge examples from the rapidly emerging space of applied discovery Design futures and concepts exploring the possible evolution paths of discovery interactions ]]>
Looking deeper than the celebratory rhetoric of information quantity, at its core, Big Data makes possible unprecedented awareness and insight into every sphere of life; from business and politics, to the environment, arts and society. In this coming Age of Insight, discovery is not only the purview of specialized Data Scientists who create exotic visualizations of massive data sets, it is a fundamental category of human activity that is essential to everyday interactions between people, resources, and environments. To provide architects and designers with an effective starting point for creating satisfying and relevant user experiences that rely on discovery interactions, this session presents a simple analytical and generative toolkit for understanding how people conduct the broad range of discovery activities necessary in the information-permeated world. Specifically, this session will present: A simple, research-derived language for describing discovery needs and activities that spans domains, environments, media, and personas Observed and reusable patterns of discovery activities in individual and collaborative settings Examples of the architecture of successful discovery experiences at small and large scales A vocabulary and perspective for discovery as a critical individual and organizational capability Leading edge examples from the rapidly emerging space of applied discovery Design futures and concepts exploring the possible evolution paths of discovery interactions ]]> Fri, 26 Oct 2012 10:31:31 GMT /slideshow/designing-big-data-interactions-the-language-of-discovery/14900642 OReillyStrata@slideshare.net(OReillyStrata) Designing Big Data Interactions: The Language of Discovery OReillyStrata Looking deeper than the celebratory rhetoric of information quantity, at its core, Big Data makes possible unprecedented awareness and insight into every sphere of life; from business and politics, to the environment, arts and society. In this coming Age of Insight, discovery is not only the purview of specialized Data Scientists who create exotic visualizations of massive data sets, it is a fundamental category of human activity that is essential to everyday interactions between people, resources, and environments. To provide architects and designers with an effective starting point for creating satisfying and relevant user experiences that rely on discovery interactions, this session presents a simple analytical and generative toolkit for understanding how people conduct the broad range of discovery activities necessary in the information-permeated world. Specifically, this session will present: A simple, research-derived language for describing discovery needs and activities that spans domains, environments, media, and personas Observed and reusable patterns of discovery activities in individual and collaborative settings Examples of the architecture of successful discovery experiences at small and large scales A vocabulary and perspective for discovery as a critical individual and organizational capability Leading edge examples from the rapidly emerging space of applied discovery Design futures and concepts exploring the possible evolution paths of discovery interactions <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratalanguagediscovery3short-121026103136-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Looking deeper than the celebratory rhetoric of information quantity, at its core, Big Data makes possible unprecedented awareness and insight into every sphere of life; from business and politics, to the environment, arts and society. In this coming Age of Insight, discovery is not only the purview of specialized Data Scientists who create exotic visualizations of massive data sets, it is a fundamental category of human activity that is essential to everyday interactions between people, resources, and environments. To provide architects and designers with an effective starting point for creating satisfying and relevant user experiences that rely on discovery interactions, this session presents a simple analytical and generative toolkit for understanding how people conduct the broad range of discovery activities necessary in the information-permeated world. Specifically, this session will present: A simple, research-derived language for describing discovery needs and activities that spans domains, environments, media, and personas Observed and reusable patterns of discovery activities in individual and collaborative settings Examples of the architecture of successful discovery experiences at small and large scales A vocabulary and perspective for discovery as a critical individual and organizational capability Leading edge examples from the rapidly emerging space of applied discovery Design futures and concepts exploring the possible evolution paths of discovery interactions
Designing Big Data Interactions: The Language of Discovery from OReillyStrata
]]> 1049 7 https://cdn.slidesharecdn.com/ss_thumbnails/stratalanguagediscovery3short-121026103136-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Digital Reasoning_Tim Estes_Strata NYC 2012 /slideshow/digital-reasoningtim-estesstrata-nyc-2012-14842044/14842044 digitalreasoningtimestesstratahdpublicshare-121022193515-phpapp02
This is Tim Estes', CEO of Digital Reasoning, keynote speech for Strata NYC 2012. In this presentation, Tim makes the case that Big Data is less about data and more about people. It is about positively affecting the lives of those around us through the moral application of technology.]]>
This is Tim Estes', CEO of Digital Reasoning, keynote speech for Strata NYC 2012. In this presentation, Tim makes the case that Big Data is less about data and more about people. It is about positively affecting the lives of those around us through the moral application of technology.]]> Mon, 22 Oct 2012 19:35:13 GMT /slideshow/digital-reasoningtim-estesstrata-nyc-2012-14842044/14842044 OReillyStrata@slideshare.net(OReillyStrata) Digital Reasoning_Tim Estes_Strata NYC 2012 OReillyStrata This is Tim Estes', CEO of Digital Reasoning, keynote speech for Strata NYC 2012. In this presentation, Tim makes the case that Big Data is less about data and more about people. It is about positively affecting the lives of those around us through the moral application of technology. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/digitalreasoningtimestesstratahdpublicshare-121022193515-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is Tim Estes&#39;, CEO of Digital Reasoning, keynote speech for Strata NYC 2012. In this presentation, Tim makes the case that Big Data is less about data and more about people. It is about positively affecting the lives of those around us through the moral application of technology.
Digital Reasoning_Tim Estes_Strata NYC 2012 from OReillyStrata
]]> 887 5 https://cdn.slidesharecdn.com/ss_thumbnails/digitalreasoningtimestesstratahdpublicshare-121022193515-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 clearScienceStrataRx2012 /slideshow/clearsciencestratarx2012/14761145 stratarxerichhuangcurrent-121017005347-phpapp01
The impetus behind Sage Bionetworks' clearScience project and how it effaces the boundary between doing science and communicating science.]]>
The impetus behind Sage Bionetworks' clearScience project and how it effaces the boundary between doing science and communicating science.]]> Wed, 17 Oct 2012 00:53:47 GMT /slideshow/clearsciencestratarx2012/14761145 OReillyStrata@slideshare.net(OReillyStrata) clearScienceStrataRx2012 OReillyStrata The impetus behind Sage Bionetworks' clearScience project and how it effaces the boundary between doing science and communicating science. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratarxerichhuangcurrent-121017005347-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The impetus behind Sage Bionetworks&#39; clearScience project and how it effaces the boundary between doing science and communicating science.
clearScienceStrataRx2012 from OReillyStrata
]]> 916 3 https://cdn.slidesharecdn.com/ss_thumbnails/stratarxerichhuangcurrent-121017005347-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-OReillyStrata-48x48.jpg?cb=1404648020 https://cdn.slidesharecdn.com/ss_thumbnails/final-131115073059-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/final-28280777/28280777 Dealing with Uncertain... https://cdn.slidesharecdn.com/ss_thumbnails/sapientnitrostratapresentationupload-131114094519-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/sapientnitro-stratapresentationupload/28245695 SapientNitro Strata_pr... https://cdn.slidesharecdn.com/ss_thumbnails/strataukaureliepolsprivacy-131113055548-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/strata-uk-aureliepolsprivacy/28195852 Digital analytics &amp; pr...