�ݺ�ߣshows by User: ChiYiKuan

�ݺ�ߣshows by User: ChiYiKuan / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: ChiYiKuan / Tue, 13 Dec 2016 06:18:53 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: ChiYiKuan Understanding Voice of Members via Text Mining – How Linkedin Built a Text Analytics Engine at Scale /slideshow/understanding-voice-of-members-via-text-mining-how-linkedin-built-a-text-analytics-engine-at-scale-70082163/70082163 strata2016singaporefinal-161213061853
Presentation at Strata 2016 in Singapore, see http://conferences.oreilly.com/]]>
Presentation at Strata 2016 in Singapore, see http://conferences.oreilly.com/]]> Tue, 13 Dec 2016 06:18:53 GMT /slideshow/understanding-voice-of-members-via-text-mining-how-linkedin-built-a-text-analytics-engine-at-scale-70082163/70082163 ChiYiKuan@slideshare.net(ChiYiKuan) Understanding Voice of Members via Text Mining – How Linkedin Built a Text Analytics Engine at Scale ChiYiKuan Presentation at Strata 2016 in Singapore, see http://conferences.oreilly.com/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/strata2016singaporefinal-161213061853-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Presentation at Strata 2016 in Singapore, see http://conferences.oreilly.com/

Understanding Voice of Members via Text Mining – How Linkedin Built a Text Analytics Engine at Scale from Chi-Yi Kuan

]]> 624 2 https://cdn.slidesharecdn.com/ss_thumbnails/strata2016singaporefinal-161213061853-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 Understanding voice of the member via text mining /slideshow/understanding-voice-of-the-member-via-text-mining-54063956/54063956 textanalyticssummit2015finalslideshare-151017175908-lva1-app6892
The 14th Text Analytics Summit - June 15, 2015 in New York Today, Businesses around the world are increasingly collecting tremendous amount of unstructured data in the form of text – from multiple channels such as product reviews, market research, customer care conversations, and social media. In this talk, we will share how LinkedIn has built a text-mining platform to derive insights and create value for our members from the massive amount of data we have within our ecosystem. We will cover the following topics in our talk: 1) Topic modeling 2) Text categorization using NLP features 3) Topic-based sentiment analysis and attribution The talk will be appropriate for business leaders, researchers and practitioners.]]>
The 14th Text Analytics Summit - June 15, 2015 in New York Today, Businesses around the world are increasingly collecting tremendous amount of unstructured data in the form of text – from multiple channels such as product reviews, market research, customer care conversations, and social media. In this talk, we will share how LinkedIn has built a text-mining platform to derive insights and create value for our members from the massive amount of data we have within our ecosystem. We will cover the following topics in our talk: 1) Topic modeling 2) Text categorization using NLP features 3) Topic-based sentiment analysis and attribution The talk will be appropriate for business leaders, researchers and practitioners.]]> Sat, 17 Oct 2015 17:59:08 GMT /slideshow/understanding-voice-of-the-member-via-text-mining-54063956/54063956 ChiYiKuan@slideshare.net(ChiYiKuan) Understanding voice of the member via text mining ChiYiKuan The 14th Text Analytics Summit - June 15, 2015 in New York Today, Businesses around the world are increasingly collecting tremendous amount of unstructured data in the form of text – from multiple channels such as product reviews, market research, customer care conversations, and social media. In this talk, we will share how LinkedIn has built a text-mining platform to derive insights and create value for our members from the massive amount of data we have within our ecosystem. We will cover the following topics in our talk: 1) Topic modeling 2) Text categorization using NLP features 3) Topic-based sentiment analysis and attribution The talk will be appropriate for business leaders, researchers and practitioners. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/textanalyticssummit2015finalslideshare-151017175908-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> The 14th Text Analytics Summit - June 15, 2015 in New York Today, Businesses around the world are increasingly collecting tremendous amount of unstructured data in the form of text – from multiple channels such as product reviews, market research, customer care conversations, and social media. In this talk, we will share how LinkedIn has built a text-mining platform to derive insights and create value for our members from the massive amount of data we have within our ecosystem. We will cover the following topics in our talk: 1) Topic modeling 2) Text categorization using NLP features 3) Topic-based sentiment analysis and attribution The talk will be appropriate for business leaders, researchers and practitioners.

Understanding voice of the member via text mining from Chi-Yi Kuan

]]> 1467 9 https://cdn.slidesharecdn.com/ss_thumbnails/textanalyticssummit2015finalslideshare-151017175908-lva1-app6892-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 How LinkedIn leverages data to build scalable payments strategy /slideshow/how-linkedin-leverages-data-to-build-scalable-payments-strategy/46432325 mrcvegas15linkedinvslideshare-150329233059-conversion-gate01
The presentation will discuss how investing in data technologies can help organizations measure actions, derive insights, and make better decisions. Attendees will learn how to leverage internal & external data sources to build a data driven and holistic payments strategy. Customers don’t care about organizational barriers, so break them down ASAP. It is important to focus on "Outside-in" and "Customer First" in all you do, and the metrics will follow! #MRCVegas15]]>
The presentation will discuss how investing in data technologies can help organizations measure actions, derive insights, and make better decisions. Attendees will learn how to leverage internal & external data sources to build a data driven and holistic payments strategy. Customers don’t care about organizational barriers, so break them down ASAP. It is important to focus on "Outside-in" and "Customer First" in all you do, and the metrics will follow! #MRCVegas15]]> Sun, 29 Mar 2015 23:30:59 GMT /slideshow/how-linkedin-leverages-data-to-build-scalable-payments-strategy/46432325 ChiYiKuan@slideshare.net(ChiYiKuan) How LinkedIn leverages data to build scalable payments strategy ChiYiKuan The presentation will discuss how investing in data technologies can help organizations measure actions, derive insights, and make better decisions. Attendees will learn how to leverage internal & external data sources to build a data driven and holistic payments strategy. Customers don’t care about organizational barriers, so break them down ASAP. It is important to focus on "Outside-in" and "Customer First" in all you do, and the metrics will follow! #MRCVegas15 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mrcvegas15linkedinvslideshare-150329233059-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> The presentation will discuss how investing in data technologies can help organizations measure actions, derive insights, and make better decisions. Attendees will learn how to leverage internal & external data sources to build a data driven and holistic payments strategy. Customers don’t care about organizational barriers, so break them down ASAP. It is important to focus on "Outside-in" and "Customer First" in all you do, and the metrics will follow! #MRCVegas15

How LinkedIn leverages data to build scalable payments strategy from Chi-Yi Kuan

]]> 651 7 https://cdn.slidesharecdn.com/ss_thumbnails/mrcvegas15linkedinvslideshare-150329233059-conversion-gate01-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 How LinkedIn Democratizes Big Data Visualization /slideshow/how-linkedin-democratizes-big-data-visualization/40556494 stratafinalslideshare-141021131235-conversion-gate01
Speakers: Jonathan Wu (LinkedIn), Praveen Neppalli Naga (LinkedIn), Chi-Yi Kuan (LinkedIn) Category: Hadoop in Action LinkedIn processes enormous amounts of events each day. This data is of critical importance for data analysts, engineers, business experts, and data scientists that seek deep understanding of the interactions within LinkedIn’s professional social graph. They use this data to derive insights and performance metrics, which lead to better business decisions on products, marketing, sales, and other functional areas. Areas of interest include Email, Growth, Engagement, and Trending metrics. Development of internal tools has traditionally been based on specific need, optimized for the business use case, and non-interoperable. The engineering challenge is to allow business users to easily access and organize huge amounts of data in a comprehensive way and to be able to flexible and quickly get to the insights through graphs and charts that they need. The data needs to be sufficiently granular to work for different needs, the interface needs to be intuitive and simple, and the infrastructure needs to be high performance allowing users to manipulate large amounts of data quickly. The solution to this challenge was realized by the LinkedIn Business Analytics and Data Analytics Infrastructure teams utilizing an integrated stack that includes an interactive analytics infrastructure and a self-serve data visualization front-end solution. The user interface provides a customizable ability to build charts, tables, and queries to suit highly customized reporting needs on any devices. The back-end infrastructure is based on Hadoop; which leverages LinkedIn’s investment in high scalable, data rich systems. The combined solution brings the ability to visualize, slice, dice, and drill through billions of records and hundreds of dimensions at fast scale. In this talk, you will learn the background of the data challenges that LinkedIn faced, how the teams came together to construct the solution, and the underlying stack structure powering this solution.]]>
Speakers: Jonathan Wu (LinkedIn), Praveen Neppalli Naga (LinkedIn), Chi-Yi Kuan (LinkedIn) Category: Hadoop in Action LinkedIn processes enormous amounts of events each day. This data is of critical importance for data analysts, engineers, business experts, and data scientists that seek deep understanding of the interactions within LinkedIn’s professional social graph. They use this data to derive insights and performance metrics, which lead to better business decisions on products, marketing, sales, and other functional areas. Areas of interest include Email, Growth, Engagement, and Trending metrics. Development of internal tools has traditionally been based on specific need, optimized for the business use case, and non-interoperable. The engineering challenge is to allow business users to easily access and organize huge amounts of data in a comprehensive way and to be able to flexible and quickly get to the insights through graphs and charts that they need. The data needs to be sufficiently granular to work for different needs, the interface needs to be intuitive and simple, and the infrastructure needs to be high performance allowing users to manipulate large amounts of data quickly. The solution to this challenge was realized by the LinkedIn Business Analytics and Data Analytics Infrastructure teams utilizing an integrated stack that includes an interactive analytics infrastructure and a self-serve data visualization front-end solution. The user interface provides a customizable ability to build charts, tables, and queries to suit highly customized reporting needs on any devices. The back-end infrastructure is based on Hadoop; which leverages LinkedIn’s investment in high scalable, data rich systems. The combined solution brings the ability to visualize, slice, dice, and drill through billions of records and hundreds of dimensions at fast scale. In this talk, you will learn the background of the data challenges that LinkedIn faced, how the teams came together to construct the solution, and the underlying stack structure powering this solution.]]> Tue, 21 Oct 2014 13:12:35 GMT /slideshow/how-linkedin-democratizes-big-data-visualization/40556494 ChiYiKuan@slideshare.net(ChiYiKuan) How LinkedIn Democratizes Big Data Visualization ChiYiKuan Speakers: Jonathan Wu (LinkedIn), Praveen Neppalli Naga (LinkedIn), Chi-Yi Kuan (LinkedIn) Category: Hadoop in Action LinkedIn processes enormous amounts of events each day. This data is of critical importance for data analysts, engineers, business experts, and data scientists that seek deep understanding of the interactions within LinkedIn’s professional social graph. They use this data to derive insights and performance metrics, which lead to better business decisions on products, marketing, sales, and other functional areas. Areas of interest include Email, Growth, Engagement, and Trending metrics. Development of internal tools has traditionally been based on specific need, optimized for the business use case, and non-interoperable. The engineering challenge is to allow business users to easily access and organize huge amounts of data in a comprehensive way and to be able to flexible and quickly get to the insights through graphs and charts that they need. The data needs to be sufficiently granular to work for different needs, the interface needs to be intuitive and simple, and the infrastructure needs to be high performance allowing users to manipulate large amounts of data quickly. The solution to this challenge was realized by the LinkedIn Business Analytics and Data Analytics Infrastructure teams utilizing an integrated stack that includes an interactive analytics infrastructure and a self-serve data visualization front-end solution. The user interface provides a customizable ability to build charts, tables, and queries to suit highly customized reporting needs on any devices. The back-end infrastructure is based on Hadoop; which leverages LinkedIn’s investment in high scalable, data rich systems. The combined solution brings the ability to visualize, slice, dice, and drill through billions of records and hundreds of dimensions at fast scale. In this talk, you will learn the background of the data challenges that LinkedIn faced, how the teams came together to construct the solution, and the underlying stack structure powering this solution. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratafinalslideshare-141021131235-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Speakers: Jonathan Wu (LinkedIn), Praveen Neppalli Naga (LinkedIn), Chi-Yi Kuan (LinkedIn) Category: Hadoop in Action LinkedIn processes enormous amounts of events each day. This data is of critical importance for data analysts, engineers, business experts, and data scientists that seek deep understanding of the interactions within LinkedIn’s professional social graph. They use this data to derive insights and performance metrics, which lead to better business decisions on products, marketing, sales, and other functional areas. Areas of interest include Email, Growth, Engagement, and Trending metrics. Development of internal tools has traditionally been based on specific need, optimized for the business use case, and non-interoperable. The engineering challenge is to allow business users to easily access and organize huge amounts of data in a comprehensive way and to be able to flexible and quickly get to the insights through graphs and charts that they need. The data needs to be sufficiently granular to work for different needs, the interface needs to be intuitive and simple, and the infrastructure needs to be high performance allowing users to manipulate large amounts of data quickly. The solution to this challenge was realized by the LinkedIn Business Analytics and Data Analytics Infrastructure teams utilizing an integrated stack that includes an interactive analytics infrastructure and a self-serve data visualization front-end solution. The user interface provides a customizable ability to build charts, tables, and queries to suit highly customized reporting needs on any devices. The back-end infrastructure is based on Hadoop; which leverages LinkedIn’s investment in high scalable, data rich systems. The combined solution brings the ability to visualize, slice, dice, and drill through billions of records and hundreds of dimensions at fast scale. In this talk, you will learn the background of the data challenges that LinkedIn faced, how the teams came together to construct the solution, and the underlying stack structure powering this solution.

How LinkedIn Democratizes Big Data Visualization from Chi-Yi Kuan

]]> 1212 1 https://cdn.slidesharecdn.com/ss_thumbnails/stratafinalslideshare-141021131235-conversion-gate01-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-ChiYiKuan-48x48.jpg?cb=1645199373 Chi-Yi Kuan is an enthusiastic analytics leader with 18 years of industry experience. He has been applying the-state-of-the-art big data analytics, data science, global risk & fraud management, and marketing effectiveness on solving challenging large-scale problems in corporate and startup settings to make data science with significant impact. Chi-Yi is a data science visionary & inventor with 10+ patents to bring "Analytics to Impact" disruptive innovation to the data industry and share strong knowledge and experience of cutting edge tools and methodologies for sophisticated quantitative, data mining, predictive modeling, and decision optimization. An inspirational seasoned leader w... https://cdn.slidesharecdn.com/ss_thumbnails/strata2016singaporefinal-161213061853-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/understanding-voice-of-members-via-text-mining-how-linkedin-built-a-text-analytics-engine-at-scale-70082163/70082163 Understanding Voice of... https://cdn.slidesharecdn.com/ss_thumbnails/textanalyticssummit2015finalslideshare-151017175908-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/understanding-voice-of-the-member-via-text-mining-54063956/54063956 Understanding voice of... https://cdn.slidesharecdn.com/ss_thumbnails/mrcvegas15linkedinvslideshare-150329233059-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-linkedin-leverages-data-to-build-scalable-payments-strategy/46432325 How LinkedIn leverages...