Information Security Forum (ISF) Congress 2013
•
1 like•849 views
The document discusses open data in clinical research and how it relates to big data. It notes that open data means data that can be analyzed and used by anyone through linkages and evidence-based applications. The document outlines key principles for open data, including clarity of use, data quality, and managing data reuse. It describes benefits like crowd-sourcing analysis, data linkage insights, and improved data quality. Finally, it summarizes that for clinical research, open data is a way to securely analyze and apply insights from big data.
Report
Share
Report
Share
Download to read offline
Recommended
Big Data Expo 2015 - Trillium software Big Data and the Data Quality
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
CBIG Event June 20th, 2013. Presentation by Albert Khair. “Emerging Trends in...
Join Albert for his presentation which will focus on key emerging trends in Business Intelligence (BI) and Analytics. He will identify ways in which an enterprise can organize capacities for successfully leveraging continually advancing tools and technologies in the Analytics space with the goal of developing and deploying optimal business value in the most effective and efficient manner. Lexmark International achieved operational excellence and order of magnitude efficiencies in reporting performance and user satisfaction by integrating data from various functional silos with disparate BI standards into SAP HANA (High Performance ANalytic Appliance) and then leveraging BusinessObjects BI 4.0 for meeting complex BI analytics, report development, and end-user requirements.
N. Albert Khair is a Business Intelligence, Enterprise Architecture and Data Warehousing expert and has worked in Information Technology (IT) for more than 25 years and is currently employed by Lexmark International headquartered in Lexington, Kentucky. Albert’s work experience within the continental U.S. and abroad spans both public and private sectors, including government, insurance, consulting, airlines and high-tech electronics industries. Albert's functional areas of focus include: Oracle ERP, SAP ERP, SAP NetWeaver, SAP BusinessObjects BI4.0, Supply Chain, Finance, Sales and Distribution, SAP BW, SAP HANA/RDS. Albert has been published in Information Week, a magazine for business and technology managers, and has presented at SAP Insider and ASUG (Americas SAP Users Group) at their national and regional conferences.
The Economic Value of Data: A New Revenue Stream for Global Custodians
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
Data Catalog as the Platform for Data Intelligence
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Real-time Data is Changing the Face of the Insurance Industry
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
Big Data 101, What It Means for Business - BDI 12/4/13 The Future of Financia...
Big Data 101, What It Means for Business - BDI 12/4/13 The Future of Financia...Business Development Institute
Presentation: Big Data 101, What It Means for Business
Presented by: David Ray, Corporate Vice President, Corporate Internet, New York Life Insurance Company
Big Data is the latest buzzword inside the C-suite, but what does it mean, how are other industries using it to competitive advantage, and what are the real opportunities for business? Does big data require massive amounts of data to be considered or is there success to be found in unifying myriad data sources? Join us for an interesting peek.
www.bdionline.comData quality - The True Big Data Challenge
Data Quality is one of the most-overlooked key aspect in any Big Data project or approach. This talk adresses the problem from various perspectives, discusses the main challenges and identifies possible solutions.
Recommended
Big Data Expo 2015 - Trillium software Big Data and the Data Quality
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
CBIG Event June 20th, 2013. Presentation by Albert Khair. “Emerging Trends in...
Join Albert for his presentation which will focus on key emerging trends in Business Intelligence (BI) and Analytics. He will identify ways in which an enterprise can organize capacities for successfully leveraging continually advancing tools and technologies in the Analytics space with the goal of developing and deploying optimal business value in the most effective and efficient manner. Lexmark International achieved operational excellence and order of magnitude efficiencies in reporting performance and user satisfaction by integrating data from various functional silos with disparate BI standards into SAP HANA (High Performance ANalytic Appliance) and then leveraging BusinessObjects BI 4.0 for meeting complex BI analytics, report development, and end-user requirements.
N. Albert Khair is a Business Intelligence, Enterprise Architecture and Data Warehousing expert and has worked in Information Technology (IT) for more than 25 years and is currently employed by Lexmark International headquartered in Lexington, Kentucky. Albert’s work experience within the continental U.S. and abroad spans both public and private sectors, including government, insurance, consulting, airlines and high-tech electronics industries. Albert's functional areas of focus include: Oracle ERP, SAP ERP, SAP NetWeaver, SAP BusinessObjects BI4.0, Supply Chain, Finance, Sales and Distribution, SAP BW, SAP HANA/RDS. Albert has been published in Information Week, a magazine for business and technology managers, and has presented at SAP Insider and ASUG (Americas SAP Users Group) at their national and regional conferences.
The Economic Value of Data: A New Revenue Stream for Global Custodians
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
Data Catalog as the Platform for Data Intelligence
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Real-time Data is Changing the Face of the Insurance Industry
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
Big Data 101, What It Means for Business - BDI 12/4/13 The Future of Financia...
Big Data 101, What It Means for Business - BDI 12/4/13 The Future of Financia...Business Development Institute
Presentation: Big Data 101, What It Means for Business
Presented by: David Ray, Corporate Vice President, Corporate Internet, New York Life Insurance Company
Big Data is the latest buzzword inside the C-suite, but what does it mean, how are other industries using it to competitive advantage, and what are the real opportunities for business? Does big data require massive amounts of data to be considered or is there success to be found in unifying myriad data sources? Join us for an interesting peek.
www.bdionline.comData quality - The True Big Data Challenge
Data Quality is one of the most-overlooked key aspect in any Big Data project or approach. This talk adresses the problem from various perspectives, discusses the main challenges and identifies possible solutions.
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
IBM Watson Content Analytics: Discover Hidden Value in Your Unstructured Data
Healthcare organizations create a massive amount of digital data. Some is stored in structured fields within electronic medical records (EMR), claims or financial systems and is readily accessible with traditional analytics. Other information, such as physician notes, patient surveys, call center recordings and diagnosis reports is often saved in a free-form text format and is rarely used for analytics. In fact, experts suggest that up to 80% of enterprise data exists in this unstructured format, which means a majority of critical data isn’t being considered or analyzed!
Our webinar demonstrated how to extract insights from unstructured data to increase the accuracy of healthcare decisions with IBM Watson Content Analytics. Leveraging years of experience from hundreds of physicians, IBM has developed tools and healthcare accelerators that allow you to quickly gain insights from this “new” data source and correlate it with the structured data to provide a more complete picture.
Birst for Recurring Revenue
In this video, Southard Jones from Birst describes the company's new Recurring Revenue Solution Accelerator, which gives subscription-based businesses the ability to maximize revenue, accelerate growth and reduce churn by putting fast, accurate and meaningful business intelligence and data analysis capabilities at their fingertips.
Learn more: http://www.birst.com/company/press/birst-helps-subscription-based-businesses-multiply-their-revenue-new-recurring-revenue
Watch the video presentation: http://wp.me/p3RLEV-1vI
Revolution In Data Governance - Transforming the customer experience
The foundation of managing data security and big data is implementing data governance. Data Owners, Metadata tagging, Customer feedback and Continuous Improvement are critical facets to provide the transparency and consistency so that customer's can trust the data, and make informed decisions.
Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytic leaders are facing unprecedented pressure as expectations from the digitization of health drives questions from every corner of the enterprise. Along with the operational and workflow changes that come with digital health, we are seeing greater demand for data to support care transformation, risk contracting and organizational performance.
The time is right to consider how analytics can support organizational strategies and how we can ensure alignment across the organization. As part of the strategic alignment exercise we often see organizations consider how to best deliver advanced analytic capabilities and then ask themselves the question “how should we organize our analytic teams?” Often, an effective way to increase that efficiency, improve morale and achieve economy of scale is to consider changes to how analytics teams are organized.
The most appropriate organizational structure will vary based on the health system size, culture, and analytics (and data) maturity. Should the analytics capabilities be centralized, decentralized, or should we consider an alternative, hybrid staffing model? Should analytics sit under IT or medical leadership?
In our Data4Decisions talk, we will review the common models employed by leaders in healthcare, and describe how they align with business strategy. Further, we will outline common challenges as well as share success secrets via case studies from across the US healthcare landscape. The goal of this presentation is to provide the audience with a strong foundation for understanding the healthcare analytics staffing models used across the industry.
Ensuring Data Quality and Lineage in Cloud Migration - Dan Power
Ensuring Data Quality and Lineage in Cloud Migration - Dan Power
Data-Driven is Passé: Transform Into An Insights-Driven Enterprise
Watch the full webinar: http://goo.gl/c5rlCM
Speakers: Holger Kisker, Ph.D., Vice President and Research Director at Forrester Research Inc.
Listen to Holger Kisker, Vice President and Research Director at Forrester Research Inc., describe the three step plan for organizations to become insights-driven rather than data-driven enterprises. Adopting systems of insight and embedding them into your organization’s systems of engagement, record, and automation allows you to turn data into action. As a final step, data virtualization can help keep all systems in synch, being a key enabler for systems of insight.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Unlocking value in your (big) data
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...
A whitepaper of TDWI checklist, drills into the data, tools, and platform requirements for machine learning to to identify goals and areas of improvement for current project
https://www.qubole.com/resources/white-papers/tdwi-checklist-the-automation-and-optimzation-of-advanced-analytics-based-on-machine-learning
Maturing Your Organization's Information Risk Management Strategy
As organizations grow, they face more risks associated with the security and protection of sensitive data. Organizations struggling to navigate the different stages of business need to be sensitive to the increasing maturity necessary to support increasing demands for data governance and information risk management.
Learn about:
▪ Four different stages of the maturity curve
▪ Assessing data sensitivity and classifying data assets
▪ Access controls and data protection
▪ Interpreting policies and determining their impact on information management
▪ Determining the impact of data protection policies on information management practices
▪ Automating policy compliance auditing
▪ Maintaining governance consistency across the hybrid data enterprise
Watch the on-demand webinar here: https://tdwi.org/webcasts/2021/03/arch-all-maturing-your-organizations-information-risk-management-strategy.aspx with TDWI Speaker: David Loshin, President of Knowledge Integrity and guest speaker Bill Brooks, Director of Solutions Engineering, Privacera (www.privacera.com)
Bundled Payment Changes: Learn What’s New and How to Succeed
In January, CMS announced the Bundled Payment for Care Improvement Advanced “BPCI Advanced” program, initiating renewed interest in a total cost of care payment model for specific episodes of care. Regardless of your organization’s current decision to participate, it’s important to understand how bundled payment programs have the ability to significantly decrease your internal costs, broaden your revenue opportunities, and improve patient outcomes across specific populations. The Center for Medicare and Medicaid Innovation’s newest iteration of bundled payments provides another tightly-defined program that allows organizations to scale Population Health Management. Best practice suggests that tactical interventions to assess clinical variation, implement strategic care redesign programs, and to adjust care management-facilitated patient stratification models are important to be successful with bundled payments – so knowing how to implement them is crucial. One organization’s savings is another’s income and without making overhead allocation changes, bundled payments may reduce revenue that has been critically important to maintain hospital profitability. Join this webinar to learn:
* What is new with bundled payments.
* The ramifications bundles can have across organizations.
* Leveraging data and strategic analysis to identify opportunities for bundled payment success.
* Operationalizing successful care program tactics to be successful in bundled payment contracts.
Data quality management Basic
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
Why You Need to Govern Big Data
Originally Published: Jan 21, 2015
The size and complexity of data make it difficult for companies to unlock the true value of their data. IBM Information Integration Governance can improve data quality, protect sensitive data, and reduce cost and risk. Free up your resources and get more out of your data.
The Evolution of Data and New Opportunities for Analytics
BIG DATA IS EVERYWHERE!
Today we produce around five Exabyte every two days … and this is accelerating.
The intelligent devices, what we call the internet of things, promise to be the next big explosion.
Explore evolution of data and new opportunities for analytics.
www.sas.com
Data Virtualization Modernizes Biobanking
Access the webinar: http://goo.gl/p08pTz
These slides were presented in a webinar by Denodo in collaboration with BioStorage Technologies and Indiana Clinical and Translational Sciences Institute and Regenstrief Institute.
BioStorage Technologies, Inc., Indiana Clinical and Translational Sciences Institute, and Regenstrief Institute (CTSI) have joined Denodo to talk about the important role of technological advancements, such as data virtualization, in advancing biospecimen research.
By watching this webinar, you can gain insight into best practices around the integration of biospecimen and research data as well as technology solutions that provide consolidated views and rapid conversions of this data into valuable business insights. You will also learn how data virtualization can assist with the integration of data residing in heterogeneous repositories and can securely deliver aggregated data in real-time.
Applying Data Quality Best Practices at Big Data Scale
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Curlew Research Brussels 2014 Electronic Data & Knowledge Management
Life Science externalisation and collaboration overview and the challenges that Life Science companies face in delivering successful data sharing with their partners in either Open Innovation or pre-competitive workflows
The Role of Data Lakes in Healthcare
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
More Related Content
What's hot
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
IBM Watson Content Analytics: Discover Hidden Value in Your Unstructured Data
Healthcare organizations create a massive amount of digital data. Some is stored in structured fields within electronic medical records (EMR), claims or financial systems and is readily accessible with traditional analytics. Other information, such as physician notes, patient surveys, call center recordings and diagnosis reports is often saved in a free-form text format and is rarely used for analytics. In fact, experts suggest that up to 80% of enterprise data exists in this unstructured format, which means a majority of critical data isn’t being considered or analyzed!
Our webinar demonstrated how to extract insights from unstructured data to increase the accuracy of healthcare decisions with IBM Watson Content Analytics. Leveraging years of experience from hundreds of physicians, IBM has developed tools and healthcare accelerators that allow you to quickly gain insights from this “new” data source and correlate it with the structured data to provide a more complete picture.
Birst for Recurring Revenue
In this video, Southard Jones from Birst describes the company's new Recurring Revenue Solution Accelerator, which gives subscription-based businesses the ability to maximize revenue, accelerate growth and reduce churn by putting fast, accurate and meaningful business intelligence and data analysis capabilities at their fingertips.
Learn more: http://www.birst.com/company/press/birst-helps-subscription-based-businesses-multiply-their-revenue-new-recurring-revenue
Watch the video presentation: http://wp.me/p3RLEV-1vI
Revolution In Data Governance - Transforming the customer experience
The foundation of managing data security and big data is implementing data governance. Data Owners, Metadata tagging, Customer feedback and Continuous Improvement are critical facets to provide the transparency and consistency so that customer's can trust the data, and make informed decisions.
Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytic leaders are facing unprecedented pressure as expectations from the digitization of health drives questions from every corner of the enterprise. Along with the operational and workflow changes that come with digital health, we are seeing greater demand for data to support care transformation, risk contracting and organizational performance.
The time is right to consider how analytics can support organizational strategies and how we can ensure alignment across the organization. As part of the strategic alignment exercise we often see organizations consider how to best deliver advanced analytic capabilities and then ask themselves the question “how should we organize our analytic teams?” Often, an effective way to increase that efficiency, improve morale and achieve economy of scale is to consider changes to how analytics teams are organized.
The most appropriate organizational structure will vary based on the health system size, culture, and analytics (and data) maturity. Should the analytics capabilities be centralized, decentralized, or should we consider an alternative, hybrid staffing model? Should analytics sit under IT or medical leadership?
In our Data4Decisions talk, we will review the common models employed by leaders in healthcare, and describe how they align with business strategy. Further, we will outline common challenges as well as share success secrets via case studies from across the US healthcare landscape. The goal of this presentation is to provide the audience with a strong foundation for understanding the healthcare analytics staffing models used across the industry.
Ensuring Data Quality and Lineage in Cloud Migration - Dan Power
Ensuring Data Quality and Lineage in Cloud Migration - Dan Power
Data-Driven is Passé: Transform Into An Insights-Driven Enterprise
Watch the full webinar: http://goo.gl/c5rlCM
Speakers: Holger Kisker, Ph.D., Vice President and Research Director at Forrester Research Inc.
Listen to Holger Kisker, Vice President and Research Director at Forrester Research Inc., describe the three step plan for organizations to become insights-driven rather than data-driven enterprises. Adopting systems of insight and embedding them into your organization’s systems of engagement, record, and automation allows you to turn data into action. As a final step, data virtualization can help keep all systems in synch, being a key enabler for systems of insight.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Unlocking value in your (big) data
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...
A whitepaper of TDWI checklist, drills into the data, tools, and platform requirements for machine learning to to identify goals and areas of improvement for current project
https://www.qubole.com/resources/white-papers/tdwi-checklist-the-automation-and-optimzation-of-advanced-analytics-based-on-machine-learning
Maturing Your Organization's Information Risk Management Strategy
As organizations grow, they face more risks associated with the security and protection of sensitive data. Organizations struggling to navigate the different stages of business need to be sensitive to the increasing maturity necessary to support increasing demands for data governance and information risk management.
Learn about:
▪ Four different stages of the maturity curve
▪ Assessing data sensitivity and classifying data assets
▪ Access controls and data protection
▪ Interpreting policies and determining their impact on information management
▪ Determining the impact of data protection policies on information management practices
▪ Automating policy compliance auditing
▪ Maintaining governance consistency across the hybrid data enterprise
Watch the on-demand webinar here: https://tdwi.org/webcasts/2021/03/arch-all-maturing-your-organizations-information-risk-management-strategy.aspx with TDWI Speaker: David Loshin, President of Knowledge Integrity and guest speaker Bill Brooks, Director of Solutions Engineering, Privacera (www.privacera.com)
Bundled Payment Changes: Learn What’s New and How to Succeed
In January, CMS announced the Bundled Payment for Care Improvement Advanced “BPCI Advanced” program, initiating renewed interest in a total cost of care payment model for specific episodes of care. Regardless of your organization’s current decision to participate, it’s important to understand how bundled payment programs have the ability to significantly decrease your internal costs, broaden your revenue opportunities, and improve patient outcomes across specific populations. The Center for Medicare and Medicaid Innovation’s newest iteration of bundled payments provides another tightly-defined program that allows organizations to scale Population Health Management. Best practice suggests that tactical interventions to assess clinical variation, implement strategic care redesign programs, and to adjust care management-facilitated patient stratification models are important to be successful with bundled payments – so knowing how to implement them is crucial. One organization’s savings is another’s income and without making overhead allocation changes, bundled payments may reduce revenue that has been critically important to maintain hospital profitability. Join this webinar to learn:
* What is new with bundled payments.
* The ramifications bundles can have across organizations.
* Leveraging data and strategic analysis to identify opportunities for bundled payment success.
* Operationalizing successful care program tactics to be successful in bundled payment contracts.
Data quality management Basic
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
Why You Need to Govern Big Data
Originally Published: Jan 21, 2015
The size and complexity of data make it difficult for companies to unlock the true value of their data. IBM Information Integration Governance can improve data quality, protect sensitive data, and reduce cost and risk. Free up your resources and get more out of your data.
The Evolution of Data and New Opportunities for Analytics
BIG DATA IS EVERYWHERE!
Today we produce around five Exabyte every two days … and this is accelerating.
The intelligent devices, what we call the internet of things, promise to be the next big explosion.
Explore evolution of data and new opportunities for analytics.
www.sas.com
Data Virtualization Modernizes Biobanking
Access the webinar: http://goo.gl/p08pTz
These slides were presented in a webinar by Denodo in collaboration with BioStorage Technologies and Indiana Clinical and Translational Sciences Institute and Regenstrief Institute.
BioStorage Technologies, Inc., Indiana Clinical and Translational Sciences Institute, and Regenstrief Institute (CTSI) have joined Denodo to talk about the important role of technological advancements, such as data virtualization, in advancing biospecimen research.
By watching this webinar, you can gain insight into best practices around the integration of biospecimen and research data as well as technology solutions that provide consolidated views and rapid conversions of this data into valuable business insights. You will also learn how data virtualization can assist with the integration of data residing in heterogeneous repositories and can securely deliver aggregated data in real-time.
Applying Data Quality Best Practices at Big Data Scale
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
What's hot (20)
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
IBM Watson Content Analytics: Discover Hidden Value in Your Unstructured Data
IBM Watson Content Analytics: Discover Hidden Value in Your Unstructured Data
Revolution In Data Governance - Transforming the customer experience
Revolution In Data Governance - Transforming the customer experience
Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytics Staffing Models of Health Systems That Compete Well Using Data
Ensuring Data Quality and Lineage in Cloud Migration - Dan Power
Ensuring Data Quality and Lineage in Cloud Migration - Dan Power
Data-Driven is Passé: Transform Into An Insights-Driven Enterprise
Data-Driven is Passé: Transform Into An Insights-Driven Enterprise
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...
Maturing Your Organization's Information Risk Management Strategy
Maturing Your Organization's Information Risk Management Strategy
Bundled Payment Changes: Learn What’s New and How to Succeed
Bundled Payment Changes: Learn What’s New and How to Succeed
The Evolution of Data and New Opportunities for Analytics
The Evolution of Data and New Opportunities for Analytics
Applying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data Scale
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...
Similar to Information Security Forum (ISF) Congress 2013
Curlew Research Brussels 2014 Electronic Data & Knowledge Management
Life Science externalisation and collaboration overview and the challenges that Life Science companies face in delivering successful data sharing with their partners in either Open Innovation or pre-competitive workflows
The Role of Data Lakes in Healthcare
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
The art of depositing social science data: maximising quality and ensuring go...
The art of depositing social science data: maximising quality and ensuring good governance; experiences from the UK Data Service
Microsoft: A Waking Giant in Healthcare Analytics and Big Data
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
Digital transformation to enable a FAIR approach for health data science
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Lessons from the UK: Data access, patient trust & real-world impact with heal...
Slides supporting presentation given at the virtual Beilstein Open Science Symposium in October 2021.
Abstract:
Health Data Research UK’s mission is to unite the UK’s health data to enable discoveries that improve people’s lives. Our 20-year vision is for large scale data and advanced analytics to benefit every patient interaction, clinical trial, biomedical discovery and enhance public health. A key part of HDR UK’s vision is our data portal, the Innovation Gateway. The Gateway facilitates discovery of healthcare data and simplifies data request procedures across multiple data custodians. The Gateway contains metadata on a variety of datasets, including those related to COVID-19, cardiovascular, maternal health, emergency care, primary care, secondary care, acute care, palliative care, biobanks, research cohorts and deeply phenotyped patient cohorts.
From the outset HDR UK has sought the voices, views and experiences of patient and lay-public groups to ensure there is transparency and clear public benefit in the use of the UK’s health data. Patient and public involvement is key to making the Gateway accessible, transparent and to ensure public confidence in research access to health data. The importance of public outreach combined with providing research access to data is illustrated with HDR UK’s contribution to the UK’s coronavirus pandemic response. HDR UK was tasked by the UK’s Chief Scientific Office to build and facilitate the infrastructure to support the National Core Studies, providing key insights on the evolving situation to UK policy makers during the course of the pandemic.
In this talk, I will show how HDR UK is enabling open science by facilitating the discovery of health data, and simplifying the process of requesting access to multiple datasets. I’ll discuss HDR UK’s approach to embedding transparency on research data usage for patients and public, and summarise some of the key ways in which HDR UK has contributed to the coronavirus pandemic.
Health tech slides 12 june 2019
Browne Jacobson, Deloitte and DoctorLink are pleased to invite you to our first joint health tech seminar with leading industry thought leaders. This will be a practical session, sharing experience from across the NHS and beyond to inform options on how to improve services, break down silos and focus on population health outcomes.
This event is exclusively for Commissioners, GPs, and Policymakers keen to understand how new integrated care systems and models of care can meet the needs of their local population and can be implemented pragmatically and affordably to drive improvement goals and achieve better health, better care and better value.
Microsoft: A Waking Giant In Healthcare Analytics and Big Data
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
DAMA Webinar - Big and Little Data Quality
While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.
Sharing and standards christopher hart - clinical innovation and partnering...
Acknowledging the increasing need for cooperation and collaboration in data sharing and access. Describing the complexity that this can bring. Then describing some of the ways to simplify that.
Originally presented at Terrapin's Clinical innovation and partnering world March 8-9 2017.
http://www.terrapinn.com/conference/innovation-and-partnering/index.stm
Edge Informatics and FAIR (Findable, Accessible, Interoperable and Reusable) ...
Edge Informatics is an approach to accelerate collaboration in the BioPharma pipeline. By combining technical and social solutions knowledge can be shared and leveraged across the multiple internal and external silos participating in the drug development process. This is accomplished by making data assets findable, accessible, interoperable and reusable (FAIR). Public consortia and internal efforts embracing FAIR data and Edge Informatics are highlighted, in both preclinical and clinical domains.
This talk was presented at the Molecular Medicine Tri-Conference in San Francisco, CA on February 20, 2017
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
National Workshop to Advance Use of Electronic Data
The webinar slide presentations from the July 2-3, 2012 National Workshop to Advance Use of Electronic Data.
ODI Node Vienna: Best Practise Beispiele für: Open Innovation mittels Open Data
Vortrag im Rahmen des Data Pioneers Workshop am 10.10.2016 am BMVIT zum Thema Open Innovation und Open Data (Open Innovation mittels Open Data) seitens Elmar Kiesling (TU Wien) und Martin Kaltenböck (SWC) für den ODI (Open Data Institute) Node Vienna.
Discover how Covid-19 is accelerating the need for healthcare interoperabilit...
Watch full webinar here: https://bit.ly/3cZDAvo
As COVID -19 continues to challenge entire healthcare ecosystems and forcing healthcare organizations to pivot without much notice, patient information interoperability and data transparency are increasingly taking center stage among healthcare stakeholders. This year, a new set of federal guidelines giving patients more access to their data goes into effect, improving interoperability. Even without this, most healthcare stakeholders would agree that better, and mobile, access, and interoperability of information could improve care and save time and lives.
Watch on-demand this webinar to learn:
- How health IT interoperability can help your healthcare organization move forward to better health reporting, patient matching and care coordination in 2021 and beyond.
- How to set up your healthcare organization for success through more information transparency, how this can help your healthcare stakeholders.
- COVID 19 has given federal agencies a lot of momentum to move even more quickly regarding implementing interoperability rules, what does this mean for your organization?
Data Governance in two different data archives: When is a federal data reposi...
Talk from Dr Greg Farber at the Human Brain Project conference 2018 on data governance in international neuro-ICT collaborations
Neo4j GraphDay Munich - Life & Health Sciences Intro to Graphs
Neo4j GraphDay Munich Life & Health Sciences
Bruno Ungermann, Neo4j
Similar to Information Security Forum (ISF) Congress 2013 (20)
Curlew Research Brussels 2014 Electronic Data & Knowledge Management
Curlew Research Brussels 2014 Electronic Data & Knowledge Management
The art of depositing social science data: maximising quality and ensuring go...
The art of depositing social science data: maximising quality and ensuring go...
Microsoft: A Waking Giant in Healthcare Analytics and Big Data
Microsoft: A Waking Giant in Healthcare Analytics and Big Data
Digital transformation to enable a FAIR approach for health data science
Digital transformation to enable a FAIR approach for health data science
Lessons from the UK: Data access, patient trust & real-world impact with heal...
Lessons from the UK: Data access, patient trust & real-world impact with heal...
Microsoft: A Waking Giant In Healthcare Analytics and Big Data
Microsoft: A Waking Giant In Healthcare Analytics and Big Data
Dama webinar-little-data-in-a-big-data-world-160217211043
Dama webinar-little-data-in-a-big-data-world-160217211043
Sharing and standards christopher hart - clinical innovation and partnering...
Sharing and standards christopher hart - clinical innovation and partnering...
Managing Your Research Data for Maximum Impact -Rob Daley 300616_Shared
Managing Your Research Data for Maximum Impact -Rob Daley 300616_Shared
Edge Informatics and FAIR (Findable, Accessible, Interoperable and Reusable) ...
Edge Informatics and FAIR (Findable, Accessible, Interoperable and Reusable) ...
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)
National Workshop to Advance Use of Electronic Data
National Workshop to Advance Use of Electronic Data
ODI Node Vienna: Best Practise Beispiele für: Open Innovation mittels Open Data
ODI Node Vienna: Best Practise Beispiele für: Open Innovation mittels Open Data
Discover how Covid-19 is accelerating the need for healthcare interoperabilit...
Discover how Covid-19 is accelerating the need for healthcare interoperabilit...
Data Governance in two different data archives: When is a federal data reposi...
Data Governance in two different data archives: When is a federal data reposi...
Neo4j GraphDay Munich - Life & Health Sciences Intro to Graphs
Neo4j GraphDay Munich - Life & Health Sciences Intro to Graphs
More from NIHR Clinical Research Network
Richard Corbridge itSMF Keynote
Richard Corbridge, the dynamic and innovative CIO for the NIHR Clinical Research Network, has brought significant and successful business change across health and clinical research.
He has strong beliefs that technology and
information can have a significant positive impact on the way healthcare is delivered.
Richard will be reaching for his crystal ball and talking about service management in 2030!
Embedding research in health care more quickly. AHSN 2014
‘Embedding research in health care
more quickly.’
Insight and business discovery. The right type of fans and how to get them. q...
Insight and business discovery. The right type of fans and how to get them. q...NIHR Clinical Research Network
Insight and business discovery. The right
type of fans and how to get them.Secur-a-Topia; ISF Chapter Summer Meeting 2014
Secur-a-Topia;
“The last train to Data-topia – A Crowd
Sourced Analysis”
Health insights
TECHNOLOGY and INFORMATION are now more than
ever ingrained in all that is done throughout the world and
we must adopt all elements of them or risk falling behind the
KNOWLEDGE curve.
Clinical Research Network – January 2013
- Approximately 55 different systems in place
- Around 35 different ways of collating reports
- 16 different topic-wide databases
- Significant bespoke reporting
- Data debate
-System based and system led change
The EDGE 2014 User Conference
Supporting the implementation of LPMS for the good of research information... A National Perspective
More from NIHR Clinical Research Network (6)
Embedding research in health care more quickly. AHSN 2014
Embedding research in health care more quickly. AHSN 2014
Insight and business discovery. The right type of fans and how to get them. q...
Insight and business discovery. The right type of fans and how to get them. q...
Recently uploaded
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
CBU毕业证offer【微信95270640】《卡普顿大学毕业证书》《QQ微信95270640》学位证书电子版：在线制作卡普顿大学毕业证成绩单GPA修改（制作CBU毕业证成绩单CBU文凭证书样本）、卡普顿大学毕业证书与成绩单样本图片、《CBU学历证书学位证书》、卡普顿大学毕业证案例毕业证书制作軟體、在线制作加拿大硕士学历证书真实可查.
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到卡普顿大学卡普顿大学毕业证成绩单；
3、不清楚流程以及材料该如何准备卡普顿大学卡普顿大学毕业证成绩单；
4、回国时间很长，忘记办理；
5、回国马上就要找工作，办给用人单位看；
6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★卡普顿大学卡普顿大学毕业证成绩单毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
【★真实使馆认证（留学人员回国证明），使馆存档可通过大使馆查询确认】
【★真实教育部认证，教育部存档，教育部留服网站可查】
【★真实留信认证，留信网入库存档，可查卡普顿大学卡普顿大学毕业证成绩单】
我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助卡普顿大学同学朋友
你做代理，可以拯救卡普顿大学失足青年
你做代理，可以挽救卡普顿大学一个个人才
你做代理，你将是别人人生卡普顿大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会道银边山娃摸索着扯了扯灯绳小屋顿时一片刺眼的亮瞅瞅床头的诺基亚山娃苦笑着摇了摇头连他自己都感到奇怪居然又睡到上午点半掐指算算随父亲进城已一个多星期了山娃几乎天天起得这么迟在乡下老家暑假五点多山娃就醒来在爷爷奶奶嘁嘁喳喳的忙碌声中一骨碌爬起把牛驱到后龙山再从莲塘里采回一蛇皮袋湿漉漉的莲蓬也才点多点半早就吃过早餐玩耍去了山娃的家在闽西山区依山傍水山清水秀门前潺潺流淌的蜿蜒小溪一直都是山娃和小伙伴们盛试
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
原版定制【微信:41543339】【(Bradford毕业证书)布拉德福德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Adjusting OpenMP PageRank : SHORT REPORT / NOTES
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(Deakin毕业证书)迪肯大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
原版定制【微信:41543339】【(Deakin毕业证书)迪肯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(swinburne毕业证书)斯威本科技大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Malana- Gimlet Market Analysis (Portfolio 2)
A market analysys on Spotify's parent Podcast company Gimlet.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
原版定制【Q微信:741003700】《(usq毕业证书)南昆士兰大学毕业证研究生文凭证书》【Q微信:741003700】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Recently uploaded (20)
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...
Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Information Security Forum (ISF) Congress 2013
- 1. Delivering clinical research to make patients, and the NHS, better Is ‘Open Data’ the next ‘Big Data’ and if it is, how do we do it securely? Richard Corbridge – CIO, NIHR CRN 1 richardcorbridge.com | @r1chardatron
- 2. • Developing Research Culture • Access to Research • Increased Participation (Patient and Clinician) • Increase industry investment • NHS as an international leader • Pride in the NHS • Health and healthcare improved by research • NHS Constitution Organisation: Background Commitment 2
- 3. 637,974 new participants in research in 2012/13 1,592 new studies 2012/13 744 Life Science Industry partners to date 10,000 funded NHS staff work on research 55 Informatics staff and 35 Business Intelligence staff 1.5 Information Security and Records Management Resources Organisation: Background Scores on the doors 3
- 4. TECHNOLOGY and INFORMATION are now more than ever ingrained in all that is done throughout the world and we must adopt all elements of them or risk falling behind the KNOWLEDGE curve. Clinical Research Network - Today • Approximately 55 different systems currently in place • Around 35 different ways of collating reports • 6 different topic wide databases • Significant bespoke reporting • Data debate • System based change Organisation: Background Current landscape 4
- 5. What is OPEN DATA? The comedy answer is DATA that is OPEN… But it IS as simple as that… Open data - data that can be made use of by anyone through analysis, linkages, and evidence based deliveries. Real World Data – the Big Data of 2013! What is Open Data? Clear Business Intelligence 5
- 6. Stakeholders Lifespan Scope Content Open Data Policing Who? When? Why? What? Rules? Before broadcasting the data ensure answers to all these questions are in place. What is Open Data? Key principles 6
- 7. § Clarity of data – Ensure the clarity of use. § Quality of data – Accuracy of data to be used by others. § Commercial nature of data – Can all data be used by all ‘customers’? § ‘Small Numbers’ – Can linking open data in small numbers break confidentiality? § Veracity of Open Data – The speed at which open data is created. Security of Open Data Key principles 7
- 8. § Managing the re-use of data. § The implications of reputational damage from the reuse of data. § Corporate responsibility for Open Data § Policing a code of conduct. § Enforcing a results of non-compliance. § Trigger for review of Open nature of data. Security of Open Data Governance aspects 8
- 9. § Mechanisms for Opening Data are different. § Service Management Process in place. § Access policies agreed and in statute. § Catalogue of users and uses agreed. § Understanding of the organisations re-used data may pass through. § Final destination known. Security of Open Data Open by design 9
- 10. § Crowd source analysis of Big Data. § Data linkage between other Open Data enabling new insight. § Transparency to stakeholders. § Data re-use to create information eco system. § Improved data quality through exposure. Benefits of Open Data Open by design 10
- 11. For a Rubik’s Cube There are 43,252,003,274,489,856,000 different arrangements! From only 6 sides and 3x3 dimensions But just one that is right! 11 Cubes of data Think about it?
- 12. 12 Open Data Platform Managing the Business Intelligence
- 13. Open Data Platform Managing the Business Intelligence 13
- 14. Planned Apps • Industry Tracker • UKCTG • Call-4-Participants • Track My Study • NHS Confed Data • FeAt • HLO Reporting • Map My Study • CSP Database • TrialViz • Trial Reach • DH Dashboard • RDS • LPMS SoC Future development “App Centre” 14
- 15. Improving the health and wealth of the nation by… • Tablet Development • Opening up our data through collaboration with Cancer UK • Raising awareness of the rich information we hold 15 Future development Collaboration
- 16. 16 What is Open Data? Summary For us we have put in place… § Security of data volumes. § Governance of big data analysis. § Management of Data reputation. § Ownership of the derived insight. Driving the nation to… § Be the catalyst for national Open Data standards. § Agree Open Data licencing. § Create the Open Data Institute. § Add to the Open Source cultural shift.
- 17. 17 For clinical research Open Data is the answer to the query about securing Big Data. What is Open Data? Summary Is ‘Open Data’ the next ‘Big Data’ and if it is, how do we do it securely?
- 18. Questions ? 18