Alitora Innovation Networks
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Alitora Systems presents Innovation Networks for Life Sciences at Semantic Technology Conference 2009.
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Sai Paravastu discusses the benefits of using an open data platform (ODP) for enterprises. The ODP would provide a standardized core of open source Hadoop technologies like HDFS, YARN, and MapReduce. This would allow big data solution providers to build compatible solutions on a common platform, reducing costs and improving interoperability. The ODP would also simplify integration for customers and reduce fragmentation in the industry by coordinating development efforts.
Channeling insights to the right people
The document discusses transforming information into a liquid form and channeling liquid insights to the right people. It describes challenges with existing enterprise content management restricting access and increasing complexity from partnerships and information sources. The proposed approach is to create a centralized information hub and dashboard that simplifies access to information through search capabilities and links across disparate data sources using graph computing and controlled vocabularies. This will provide a 360-degree view of information and enable high accessibility, linkage, and flow of information to collaborators.
Jive Software - Clearspace Overview
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Openfabnet - A collaborative approach towards industry 4.0 based on open sour...
Openfabnet - A collaborative approach towards industry 4.0 based on open sour...Vienna Data Science Group
This document discusses connecting, sharing, and collaborating through open source tools. It introduces the Open Source Self Organisation Services (OSSOS) project, which aims to create an open platform for open innovation using existing open source frameworks. OSSOS provides infrastructure like Colibri for business intelligence, iRedMail for email and user management, and Redmine for project management. It also describes proof-of-concept projects for quality management (+GUTIST) and discusses benefits of open source business intelligence using the Colibri suite. The document promotes open collaboration to better understand and satisfy human needs.SAP Technology Services Conference 2013: Big Data and The Cloud at Yahoo!
The Hadoop project is an integral part of Yahoo!'s cloud infrastructure and is at the heart of many of Yahoo!'s important business processes. Sumeet Singh, the Head of Products for Cloud Services and Hadoop at Yahoo!, explains how Yahoo! leverages Hadoop and Cloud Platforms to process and serve Internet- scale data.
Yahoo! operates one of the world's largest private cloud infrastructures. Learn how technologies scale out for building enterprise-wide trusted platforms with tight SLAs.
URL: http://www.saptechnologyservice.com/track1.html
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FAIRy stories: the FAIR Data principles in theory and in practice
https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR
FAIRy stories: the FAIR Data principles in theory and in practice
The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”.
As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.
In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
Social World
It is not the strongest species that survives change but the most adaptable one. Cloud computing models like IaaS, PaaS, and SaaS provide infrastructure, platforms, and applications over the internet. Companies can quickly scale their computing resources up and down as needs change.
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The Ball Launch on 2013 Microsoft TechDays Part 1/2
The Ball general developer availability launch on Microsoft Finland TechDays 2013 - Part 1/2 - Architecture Perspective
Epochs Web 1 2 3 V3 0b
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Introduction to question answering for linked data & big data
This document discusses question answering (QA) systems in the context of big data and heterogeneous data scenarios. It outlines the motivation and challenges for developing natural language interfaces for databases. The document covers the basic concepts and taxonomy of QA systems, including question types, answer types, data sources, and domains. It also discusses the anatomy and components of a typical QA system.
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
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...
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.
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- 3. How can we: Surface & Distribute Insights from Unstructured Data? Or: How do we deliver to you the interesting bits of knowledge out of a lot of text?
- 4. Problem: Insights from Data Silos Case Study: Eli Lilly / Life Science
- 5. Solution: Natural Language Processing? Transform a pile of Documents into A pile of Facts . We still have a pile.
- 6. Solution: Natural Language Processing + Linked Relationships via Ontologies? What in the network is important ?
- 7. Solution: Natural Language Processing + Linked Relationships + Relevancy? Relevant to whom?
- 8. Solution: Natural Language Processing + Linked Relationships + Relevancy + Collaboration. Ah Ha! Relevant information can find me via my context. My colleagues can help.
- 9. Then we can Surface & Distribute Insights Deploy solution as a service so Insights can be embedded anywhere.
- 10. People Databases, Ontologies News, PR, Documents, Reports Innovation Network
- 11. Innovation Network - DEMO Web Application Application Plug-In
- 14. Innovation Network kHarmony™ Data Representation: Each Object has Properties “ chunks” of RDF Unique URI “ Core” RDFs Model
- 15. Innovation Network kHarmony™ HyperEdges: Statements about Statements Track Provenance Difficult in RDF alone (everything “reified”)
- 18. Semantic Knowledge Statements Domain Ontology + Instance Statements
- 19. Semantic Knowledge Statements Direct or Inferred
- 24. Innovation Network - DEMO Web Application Application Plug-In … including OpenCalais