This document discusses Scrazzl, a company that connects scientists, suppliers, publishers, and partners through their content delivery platform. Scrazzl provides leads, widgets, and analytics to help users, customers, and partners. It aims to address challenges around data size, analysis, accuracy, and multiple languages by leveraging its content analysis capabilities and new partnerships.
Crossmark, is a service from Crossref, which gives readers quick and easy access to the current status of a piece of content. With one click, you can see if content has been updated, corrected or retracted and access valuable additional metadata provided by the publisher.
This webinar will offer a brief introduction to Crossmark, an overview of how publishers can participate, and a look at the latest updates and changes:
-Crossmark 2.0
-The look and functionality of the pop-up box
(Webinar was held on February 23, 2017)
Crossmark, is a service from Crossref, which gives readers quick and easy access to the current status of a piece of content. With one click, you can see if content has been updated, corrected or retracted and access valuable additional metadata provided by the publisher.
This webinar will offer a brief introduction to Crossmark, an overview of how publishers can participate, and a look at the latest updates and changes:
-Crossmark 2.0
-The look and functionality of the pop-up box
(Webinar was held on February 23, 2017)
Building Rich, Interactive E-commerce Applications Using ASP.NET and Silverlightgoodfriday
Come get a sneak preview of the direction that Microsoft is taking for building Rich Interactive applications. In this session, we focus on e-commerce scenarios enabling developer and designers to create easily extensible and customizable applications that use .NET and Silverlight. See some of the initial concepts currently being developed and find out how you can be part of the community that shapes future extensions to ASP.NET.
BioCatalogue talk by Carole Goble. She outlines in these slides the reasons behind the BioCatalogue project. And present the BioCatalogue and its goals.
MeshLabs is a pure-play developer of text analytics software. Our core product is a hybrid text analytics engine, that combines linguistic (NLP), statistic, and semantic approaches to process large volumes of unstructured and structured content. Built to enterprise performance standards, the engine offers flexible integration capabilities including content connectors and APIs. We are a team of information retrieval professionals who are passionate about solving complex unstructured data processing problems for a variety of industries. Our product is deployed at large enterprises globally. We specialize in developing products using emerging content processing technologies to solve complex customer experience management problems. I can discuss with you specific ideas, best practices, and case studies.
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Yael Garten
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047
Description:
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
The global need to securely derive (instant) insights, have motivated data architectures from distributed storage, to data lakes, data warehouses and lake-houses. In this talk we describe Tag.bio, a next generation data mesh platform that embeds vital elements such as domain centricity/ownership, Data as Products, Self-serve architecture, with a federated computational layer. Tag.bio data products combine data sets, smart APIs, statistical and machine learning algorithms into decentralized data products for users to discover insights using FAIR Principles. Researchers can use its point and click (no-code) system to instantly perform analysis and share versioned, reproducible results. The platform combines a dynamic cohort builder with analysis protocols and applications (low-code) to drive complex analysis workflows. Applications within data products are fully customizable via R and Python plugins (pro-code), and the platform supports notebook based developer environments with individual workspaces.
Join us for a talk/demo session on Tag.bio data mesh platform and learn how major pharma industries and university health systems are using this technology to promote value based healthcare, precision healthcare, find cures for disease, and promote collaboration (without explicitly moving data around). The talk also outlines Tag.bio secure data exchange features for real world evidence datasets, privacy centric data products (confidential computing) as well as integration with cloud services
Recording: http://pan.news/ZSKo30liS3u
Abstract: panagenda ConnectionsExpert Version 2.0 takes analytics and monitoring of your IBM Connections deployment to a new level. Senior Technical Consultant, Terri Warren introduces the version's new features and improvements.
Highlighting:
- Analytics API: access for Excel and business analytics tools
- Community Widgets: analytics for community owners inside Connections
- Notifications and Alerts for administrators and community managers
Keynote at Open Data Science Conference, San Francisco, Nov 2015, outlines the evolution of Data Science akin to evolution of alchemy to chemistry; Intel's motivations for releasing Trusted Analytics Platform to open source.
Building Rich, Interactive E-commerce Applications Using ASP.NET and Silverlightgoodfriday
Come get a sneak preview of the direction that Microsoft is taking for building Rich Interactive applications. In this session, we focus on e-commerce scenarios enabling developer and designers to create easily extensible and customizable applications that use .NET and Silverlight. See some of the initial concepts currently being developed and find out how you can be part of the community that shapes future extensions to ASP.NET.
BioCatalogue talk by Carole Goble. She outlines in these slides the reasons behind the BioCatalogue project. And present the BioCatalogue and its goals.
MeshLabs is a pure-play developer of text analytics software. Our core product is a hybrid text analytics engine, that combines linguistic (NLP), statistic, and semantic approaches to process large volumes of unstructured and structured content. Built to enterprise performance standards, the engine offers flexible integration capabilities including content connectors and APIs. We are a team of information retrieval professionals who are passionate about solving complex unstructured data processing problems for a variety of industries. Our product is deployed at large enterprises globally. We specialize in developing products using emerging content processing technologies to solve complex customer experience management problems. I can discuss with you specific ideas, best practices, and case studies.
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Yael Garten
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047
Description:
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
The global need to securely derive (instant) insights, have motivated data architectures from distributed storage, to data lakes, data warehouses and lake-houses. In this talk we describe Tag.bio, a next generation data mesh platform that embeds vital elements such as domain centricity/ownership, Data as Products, Self-serve architecture, with a federated computational layer. Tag.bio data products combine data sets, smart APIs, statistical and machine learning algorithms into decentralized data products for users to discover insights using FAIR Principles. Researchers can use its point and click (no-code) system to instantly perform analysis and share versioned, reproducible results. The platform combines a dynamic cohort builder with analysis protocols and applications (low-code) to drive complex analysis workflows. Applications within data products are fully customizable via R and Python plugins (pro-code), and the platform supports notebook based developer environments with individual workspaces.
Join us for a talk/demo session on Tag.bio data mesh platform and learn how major pharma industries and university health systems are using this technology to promote value based healthcare, precision healthcare, find cures for disease, and promote collaboration (without explicitly moving data around). The talk also outlines Tag.bio secure data exchange features for real world evidence datasets, privacy centric data products (confidential computing) as well as integration with cloud services
Recording: http://pan.news/ZSKo30liS3u
Abstract: panagenda ConnectionsExpert Version 2.0 takes analytics and monitoring of your IBM Connections deployment to a new level. Senior Technical Consultant, Terri Warren introduces the version's new features and improvements.
Highlighting:
- Analytics API: access for Excel and business analytics tools
- Community Widgets: analytics for community owners inside Connections
- Notifications and Alerts for administrators and community managers
Keynote at Open Data Science Conference, San Francisco, Nov 2015, outlines the evolution of Data Science akin to evolution of alchemy to chemistry; Intel's motivations for releasing Trusted Analytics Platform to open source.
3. Scientist Supplier Publisher
User Customer Partner
4. Scientist Supplier Publisher
No “TripAdvisor” Marketing Lots of traffic and
for experimental channels limited. content but
materials Market limited capacity
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