Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/health-insurance-predictive-analysis-with-hadoop-and-machine-learning/julien-cabot
Big Data analytics is estimated to save over $450B in healthcare costs, and there is exciting adoption of big data platforms with healthcare payers and providers. Hadoop and cloud computing have emerged as one of the most promising technologies for implementing big data at scale for healthcare workloads in production, using Hadoop as a service. Common considerations in the healthcare industry include privacy and data security, and the challenges of regulatory compliance with HIPAA and HITECH. Intel provides a security framework for Hadoop that enables enterprises to deploy big data analytics without compromising performance or security. Intel is contributing to a common security framework for Apache Hadoop, in the form of Project Rhino, which enables Hadoop to run workloads without compromising performance or security. Join this session to learn how your enterprise can take advantage of the security capabilities in the Intel Data Platform running on AWS to analyze healthcare data while ensuring technical safeguards that help you remain in compliance.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Big Data is becoming economically feasible for health care. These slides describe how the cost of sensors, data processing, data storage and data analyzing are falling, how new and better forms of storage and algorithms are being implemented, and what this means for sustainable health care. These changes are enabling a move towards personalized health care.
Hadoop and Data Virtualization - A Case Study by VHAHortonworks
VHA (Voluntary Hospitals of America) is the largest member-owned health care company in the US delivering industry-leading supply chain management services and clinical improvement services to its members. At VHA, product, supplier, and member information is siloed across multiple sources. VHA sees value in consolidating the disparate data into a Data Lake, supported by the Hortonworks Data Platform, to enable the business users to discover the related data and provide services to their members. Because of their previous success with data virtualization, powered by Denodo, VHA decided to use data virtualization to enable their business users to discover data using the familiar SQL, and thus abstract their access directly to Hadoop.
During this webinar, you will learn:
- The role, use, and benefits of Hadoop in the Modern Data Architecture.
- How Hadoop and data virtualization simplified data management and enabled faster data discovery.
- What data virtualization is and how it can simplify big data projects.
- Lessons learned from and best practices for deploying data lake and data virtualization.
Integratus solutions overview sap healthcare_in-memoryRon Lehman
Healthcare providers need a new approach to agile analytics. Old Data Warehouse technologies & approaches cant keep up with regulatory requirements. We deliver a best of both worlds, leveraging best practices for organizational roles & processes, together with best in available technology & pre-built content such as SAP HANA & Epic Cogito Data Warehouse.
Big Data analytics is estimated to save over $450B in healthcare costs, and there is exciting adoption of big data platforms with healthcare payers and providers. Hadoop and cloud computing have emerged as one of the most promising technologies for implementing big data at scale for healthcare workloads in production, using Hadoop as a service. Common considerations in the healthcare industry include privacy and data security, and the challenges of regulatory compliance with HIPAA and HITECH. Intel provides a security framework for Hadoop that enables enterprises to deploy big data analytics without compromising performance or security. Intel is contributing to a common security framework for Apache Hadoop, in the form of Project Rhino, which enables Hadoop to run workloads without compromising performance or security. Join this session to learn how your enterprise can take advantage of the security capabilities in the Intel Data Platform running on AWS to analyze healthcare data while ensuring technical safeguards that help you remain in compliance.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Big Data is becoming economically feasible for health care. These slides describe how the cost of sensors, data processing, data storage and data analyzing are falling, how new and better forms of storage and algorithms are being implemented, and what this means for sustainable health care. These changes are enabling a move towards personalized health care.
Hadoop and Data Virtualization - A Case Study by VHAHortonworks
VHA (Voluntary Hospitals of America) is the largest member-owned health care company in the US delivering industry-leading supply chain management services and clinical improvement services to its members. At VHA, product, supplier, and member information is siloed across multiple sources. VHA sees value in consolidating the disparate data into a Data Lake, supported by the Hortonworks Data Platform, to enable the business users to discover the related data and provide services to their members. Because of their previous success with data virtualization, powered by Denodo, VHA decided to use data virtualization to enable their business users to discover data using the familiar SQL, and thus abstract their access directly to Hadoop.
During this webinar, you will learn:
- The role, use, and benefits of Hadoop in the Modern Data Architecture.
- How Hadoop and data virtualization simplified data management and enabled faster data discovery.
- What data virtualization is and how it can simplify big data projects.
- Lessons learned from and best practices for deploying data lake and data virtualization.
Integratus solutions overview sap healthcare_in-memoryRon Lehman
Healthcare providers need a new approach to agile analytics. Old Data Warehouse technologies & approaches cant keep up with regulatory requirements. We deliver a best of both worlds, leveraging best practices for organizational roles & processes, together with best in available technology & pre-built content such as SAP HANA & Epic Cogito Data Warehouse.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
A Modern Data Strategy for Precision MedicineCloudera, Inc.
Genomics is upon us, made possible by big data and the technologies designed to support it. Doctors, who historically used clinical data, and researchers, who historically used genomic data, are now increasingly focused on analyzing the same single data set: introducing the opportunity to share bodies of knowledge, fostering collaborative innovation, and driving toward higher standards of care.
However, this data is enormous – volumes of genomic data are expected to reach two to four exabytes per year by 2025, yet the cost of genetic sequencing has decreased 100-fold over the past 10 years.
Cloudera is helping solve the big data problem with its Apache Hadoop-based platform for large-scale data processing, discovery, and analytics; putting precision medicine within reach.
The Convergence of Data & Digital: Mapping Out a Cohesive Strategy for Maximu...Remy Rosenbaum
Slides from Joe Caserta's Keynote at MIT CDOIQ Symposium 2018
As we continue to shift into a data-driven digital society, it’s crucial to ensure a cohesive strategy
between the chief data officer and chief digital officer. In this talk, Joe Caserta will discuss the
convergence between data and digital, addressing the interdependencies, ambiguities, and
complications between the two. Joe will outline a cohesive strategy to enhance enterprise operations
and improve your bottom line.
General Data Protection Regulation (GDPR) which will be in effect in 2018, brings newer requirements for managing personal and sensitive data of European Union subjects. The recently enacted Privacy Shield directive from 2016 now regulates the movement of data between EU and the US. Together, both regulations are impacting how CXOs are thinking about procuring, storing and processing personal and sensitive data.
Over the last few years, open-source projects such as Apache Ranger and Apache Atlas have been driving comprehensive security and governance within Hadoop and the big data ecosystem. Solution vendors such as Privacera are leveraging the power of Hadoop and Apache projects such as Atlas, Ranger to help security and compliance teams within enterprises easily identify and protect data that are subject to the privacy regulations and monitor the use of such data.
This talk will walk through the current regulatory climate in Europe and how it can impact big data implementations. We will specifically walk through a business framework that enterprises can use to build a strategy to manage GDPR, Privacy Shield, and other regulations. We will use a live demonstration to show how projects such as Apache Ranger, Apache Atlas and solutions such as Privacera can be used effectively to address specific requirements of these regulations.
It is almost impossible to escape the topic of Data Science. While the core of Data Science has remained the same over the last decade, it’s emergence to the forefront is spurred by both the availability of new data types and a true realization of the value that it delivers. In this session, we will provide an overview of data science, the different classes of machine learning algorithm and deliver an end-to-end demonstration of performing Machine Learning Using Hadoop. Audience: Developers, Data Scientist Architects and System Engineers.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=4175a7421d00257f33df146f50c41af8
How to Become an Analytics Ready Insurer - with Informatica and HortonworksHortonworks
Whether you are an insurer, reinsurer, broker or insurance service provider; everything you do is based on analytics. From underwriting to claims to agency and marketing, the smartest and most streamlined business operations at insurance companies are driven by advanced and intelligent analytics. But is your data ready? Are you an “Analytics Ready” insurer? Great analytics starts with great data management. Join us as industry experts from Informatica and Hortonworks share industry trends and best practices to show you how to become an “Analytics Ready” insurer.
Making Big Data Analytics with Hadoop fast & easy (webinar slides)Yellowfin
Looking to analyze your Big Data assets to unlock real business benefits today? But, are you sick of all the theories, hype and whoopla?
View these slides from Actian and Yellowfin’s "Big Data Analytics with Hadoop" Webinar to discover how we’re making Big Data Analytics fast and easy.
Hold on as we go from data in Hadoop to dashboard in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
Watch as we use live CDR data stored in Hadoop – quickly connecting, preparing, optimizing and analyzing this data in a tangible real-world use case from the telecommunications industry – to easily deliver actionable insights to anyone, anywhere, anytime.
To learn more about Yellowfin, and to try its intuitive Business Intelligence platform today, go here: http://www.yellowfinbi.com
To learn more about Actian, and its next generation suite of Big Data technologies, go here: http://www.actian.com/
BDaas- BigData as a service by "Sherya Pal" from "Saama". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
IT @ Intel: Preparing the Future Enterprise with the Internet of ThingsIntel IT Center
The Internet of Things (IoT) is the concept of diverse machines, devices, and technologies connecting, interacting, and negotiating with each other to help improve and enrich our lives. No longer is this limited to just computer or smart phone technology. Everyday items such as household appliance, cars and even toys can connect to the internet to integrate with other computing things, processes and services. This new paradigm is changing how data is used and collected, and introducing new challenges for enterprises.
Contexti / Oracle - Big Data : From Pilot to ProductionContexti
Big Data is moving from hype to reality for many organisations. The value proposition is clear and sponsorship is high, but how do organisations execute?
Join Oracle and Contexti to discuss the typical journey of a big data project from concept to pilot to production.
• Discuss our experience with a regional Telco
• Common Use Cases across key verticals
• Defining and prioritising use cases
• The challenge of moving from Pilot to Production
• Common Operating Models for Big Data
• Funding a Big Data Capability going forward
• Pilots - common mistakes; challenges; success criteria
Reverse aging has been a subject of ambiguity and curiosity amongst Hollywood and in the flights of fantasies of Fitzgerald. Hadoop at Verizon Wireless has been a interesting case study, both from a scale and adoption perspective. Technology adoption typically follows a linear progressive curve with time comprising of feature additions, bug fixes, upgrades, etc. In this case study we examine a case of Hadoop adoption that oscillates in a space-time continuum exhibiting characteristics of traditional growth patterns in addition to reverse aging.
The use case highlights the factors, causes, and impacts that can cause such a extraordinary phenomenon to be commonplace in any environment. The conditions leading to this phenomena might vary for different use cases, industries, and environments. This use case discusses and highlights the technical aspects leading to the ultimate path to technical redemption, which in turn engineers a well designed and performance tuned infrastructure for continuous productivity. SHIVINDER SINGH, Distinguished Member Technical Staff, Verizon
4 Essential Lessons for Adopting Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include
1) confusing data with insight
2) confusing insight with value
3) overestimating the ability to interpret the data
4) underestimating the challenge of implementation.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
A Modern Data Strategy for Precision MedicineCloudera, Inc.
Genomics is upon us, made possible by big data and the technologies designed to support it. Doctors, who historically used clinical data, and researchers, who historically used genomic data, are now increasingly focused on analyzing the same single data set: introducing the opportunity to share bodies of knowledge, fostering collaborative innovation, and driving toward higher standards of care.
However, this data is enormous – volumes of genomic data are expected to reach two to four exabytes per year by 2025, yet the cost of genetic sequencing has decreased 100-fold over the past 10 years.
Cloudera is helping solve the big data problem with its Apache Hadoop-based platform for large-scale data processing, discovery, and analytics; putting precision medicine within reach.
The Convergence of Data & Digital: Mapping Out a Cohesive Strategy for Maximu...Remy Rosenbaum
Slides from Joe Caserta's Keynote at MIT CDOIQ Symposium 2018
As we continue to shift into a data-driven digital society, it’s crucial to ensure a cohesive strategy
between the chief data officer and chief digital officer. In this talk, Joe Caserta will discuss the
convergence between data and digital, addressing the interdependencies, ambiguities, and
complications between the two. Joe will outline a cohesive strategy to enhance enterprise operations
and improve your bottom line.
General Data Protection Regulation (GDPR) which will be in effect in 2018, brings newer requirements for managing personal and sensitive data of European Union subjects. The recently enacted Privacy Shield directive from 2016 now regulates the movement of data between EU and the US. Together, both regulations are impacting how CXOs are thinking about procuring, storing and processing personal and sensitive data.
Over the last few years, open-source projects such as Apache Ranger and Apache Atlas have been driving comprehensive security and governance within Hadoop and the big data ecosystem. Solution vendors such as Privacera are leveraging the power of Hadoop and Apache projects such as Atlas, Ranger to help security and compliance teams within enterprises easily identify and protect data that are subject to the privacy regulations and monitor the use of such data.
This talk will walk through the current regulatory climate in Europe and how it can impact big data implementations. We will specifically walk through a business framework that enterprises can use to build a strategy to manage GDPR, Privacy Shield, and other regulations. We will use a live demonstration to show how projects such as Apache Ranger, Apache Atlas and solutions such as Privacera can be used effectively to address specific requirements of these regulations.
It is almost impossible to escape the topic of Data Science. While the core of Data Science has remained the same over the last decade, it’s emergence to the forefront is spurred by both the availability of new data types and a true realization of the value that it delivers. In this session, we will provide an overview of data science, the different classes of machine learning algorithm and deliver an end-to-end demonstration of performing Machine Learning Using Hadoop. Audience: Developers, Data Scientist Architects and System Engineers.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=4175a7421d00257f33df146f50c41af8
How to Become an Analytics Ready Insurer - with Informatica and HortonworksHortonworks
Whether you are an insurer, reinsurer, broker or insurance service provider; everything you do is based on analytics. From underwriting to claims to agency and marketing, the smartest and most streamlined business operations at insurance companies are driven by advanced and intelligent analytics. But is your data ready? Are you an “Analytics Ready” insurer? Great analytics starts with great data management. Join us as industry experts from Informatica and Hortonworks share industry trends and best practices to show you how to become an “Analytics Ready” insurer.
Making Big Data Analytics with Hadoop fast & easy (webinar slides)Yellowfin
Looking to analyze your Big Data assets to unlock real business benefits today? But, are you sick of all the theories, hype and whoopla?
View these slides from Actian and Yellowfin’s "Big Data Analytics with Hadoop" Webinar to discover how we’re making Big Data Analytics fast and easy.
Hold on as we go from data in Hadoop to dashboard in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
Watch as we use live CDR data stored in Hadoop – quickly connecting, preparing, optimizing and analyzing this data in a tangible real-world use case from the telecommunications industry – to easily deliver actionable insights to anyone, anywhere, anytime.
To learn more about Yellowfin, and to try its intuitive Business Intelligence platform today, go here: http://www.yellowfinbi.com
To learn more about Actian, and its next generation suite of Big Data technologies, go here: http://www.actian.com/
BDaas- BigData as a service by "Sherya Pal" from "Saama". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
IT @ Intel: Preparing the Future Enterprise with the Internet of ThingsIntel IT Center
The Internet of Things (IoT) is the concept of diverse machines, devices, and technologies connecting, interacting, and negotiating with each other to help improve and enrich our lives. No longer is this limited to just computer or smart phone technology. Everyday items such as household appliance, cars and even toys can connect to the internet to integrate with other computing things, processes and services. This new paradigm is changing how data is used and collected, and introducing new challenges for enterprises.
Contexti / Oracle - Big Data : From Pilot to ProductionContexti
Big Data is moving from hype to reality for many organisations. The value proposition is clear and sponsorship is high, but how do organisations execute?
Join Oracle and Contexti to discuss the typical journey of a big data project from concept to pilot to production.
• Discuss our experience with a regional Telco
• Common Use Cases across key verticals
• Defining and prioritising use cases
• The challenge of moving from Pilot to Production
• Common Operating Models for Big Data
• Funding a Big Data Capability going forward
• Pilots - common mistakes; challenges; success criteria
Reverse aging has been a subject of ambiguity and curiosity amongst Hollywood and in the flights of fantasies of Fitzgerald. Hadoop at Verizon Wireless has been a interesting case study, both from a scale and adoption perspective. Technology adoption typically follows a linear progressive curve with time comprising of feature additions, bug fixes, upgrades, etc. In this case study we examine a case of Hadoop adoption that oscillates in a space-time continuum exhibiting characteristics of traditional growth patterns in addition to reverse aging.
The use case highlights the factors, causes, and impacts that can cause such a extraordinary phenomenon to be commonplace in any environment. The conditions leading to this phenomena might vary for different use cases, industries, and environments. This use case discusses and highlights the technical aspects leading to the ultimate path to technical redemption, which in turn engineers a well designed and performance tuned infrastructure for continuous productivity. SHIVINDER SINGH, Distinguished Member Technical Staff, Verizon
4 Essential Lessons for Adopting Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include
1) confusing data with insight
2) confusing insight with value
3) overestimating the ability to interpret the data
4) underestimating the challenge of implementation.
Automation in the Bug Flow - Machine Learning for Triaging and TracingMarkus Borg
Issue management is a costly part of software development. In large projects, the continuous inflow of issue reports contributes to the information overload in a project, i.e., "a state where individuals do not have time or capacity to process all available information". In issue triaging, an initial step in issue management, a developer must be able to overview existing issue reports and easily navigate the software engineering project landscape. In this presentation, we present support for two work tasks involved in issue management: 1) issue assignment and 2) change impact analysis. We use machine learning to harness the ever-growing number of issue reports, by training recommendation systems on previous issues. Our industrial evaluations on 50,000+ issue reports in two large software development organizations indicate that automated issue assignment performs in line with current manual work. Moreover, we present how traceability from already resolved issue reports to various artifacts can be reused to jump start change impact analyses for newly submitted issues. Finally, we speculate on future ways to tame information overload into helpful software engineering recommendations.
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
AI&BigData Lab.Руденко Петр. Automation and optimisation of machine learning ...GeeksLab Odessa
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Руденко Петр (Инженер-программист, Datarobot) Automation and optimisation of machine learning pipelines on top of Apache Spark
В компании Datarobot мы занимаемся автоматизированным построением точных предсказательных моделей. Помимо непосредственного обучения модели, важную роль во всем процессе играет препроцессинг данных (feature selection/normalization/transformation). В своем докладе я поделюсь нашим опытом использования платформы Apache Spark и в частности новыми ml API, которые предоставляют функционал для построения пайплайнов (Pipeline), поиска оптимальных значений гиперпараметров моделей (Crossvalidation).
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
Machine Learning: Artificial Intelligence isn't just a Science Fiction topicRaúl Garreta
In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.
Evolved from the study of pattern recognition and computational learning theory in AI, It gives computers the ability to learn without being explicitly programmed
To know more, do more: Contact us
http://www.extentia.com/contact-us
BigData in Health Care Systems with IOTFaimin Khan
Nowadays Big Data is playing very important role in Day-to-Day life from Social Network to Educaion,From Banking to Business then Why not in healthcare.
Mobile phones, sensors, patients, hospitals, researchers, providers and organizations are nowadays generating huge amounts of healthcare data. The real challenge in healthcare systems is how to find, collect, analyze and manage information to make people's lives healthier and easier
By contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions.
Lower Total Cost of Care and Gain Valuable Patient Insights through Predictiv...Perficient, Inc.
Learn how predictive analytics for healthcare can enable your organization to make proactive decisions that can have a profound impact for both patients and care providers. We discuss current and emerging healthcare trends and the positive impact that predictive analytics can have on your organization by:
Optimizing Resource Utilization: Better allocate nurses, clinicians, diagnostic machinery and other resources by predicting future admission volumes
Enhancing Patient Care: Proactively treat patients by more accurately predicting the chance of a chronic condition or the response to medications and therapies
Improving Clinical Outcomes: Analyze treatment success rates to improve treatment plans, minimizing complications and readmissions
Increasing Income and Revenue: Prevent fraudulent behavior and identify opportunities to collect missing income
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
Why You Should Care about Machine Learning And Artificial Intelligence
Lighting Talk at Business of Software Conference Europe 2015
Richard Edwards IBM Watson
Presentation given by Appistry's Vice President of Product Strategy, Sultan Meghi at the World Genome Data Analysis Summit. Meghi presented about the big data challenges facing labs as they strive to manage the flow of genetic data from sequencer to the clinic.
Linked Data and Semantic Technologies can support a next generation of science. This talk shows examples of discovery, access, integration, analysis, and shows directions towards prediction and vision.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
This talk introduces Linked Data and Semantic Web by using two examples - population sciences grid and semantAqua - a semantically enabled environmental monitoring. It shows a few tools and the semantic methodology and opens discussion for LOD and team science
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
WuXi NextCODE Scales up Genomic Sequencing on AWS (ANT210-S) - AWS re:Invent ...Amazon Web Services
"Genomic sequencing is growing at a rate of 100 million sequences a year, translating into 40 exabytes by the year 2025. Handling this level of growth and performing big data analytics is a massive challenge in scalability, flexibility, and speed. In this session, learn from pioneering genomic sequencing company WuXi NextCODE, which handles complex and performance-heavy database and genomic sequencing workloads, about moving from on premises to all-in on the public cloud. Discover how WuXi NextCODE was able to achieve the performance that its workloads demand and surpass the limits of what it was able to achieve previously in genomic sequencing. This session is brought to you by AWS partner, NetApp, Inc.
Cyberinfrastructure Day 2010: Applications in BiocomputingJeremy Yang
UNM Cyberinfrastructure Day 2010 presentation: Applications in Biocomputing, biomedical and cheminformatics research computing cyberinfrastructure issues.
The digital universe is booming, especially metadata and user-generated data. This raises strong challenges in order to identify the relevant portions of data which are relevant for a particular problem and to deal with the lifecycle of data. Finer grain problems include data evolution and the potential impact of change in the applications relying on the data, causing decay. The management of scientific data is especially sensitive to this. We present the Research Objects concept as the means to indentify and structure relevant data in scientific domains, addressing data as first-class citizens. We also identify and formally represent the main reasons for decay in this domain and propose methods and tools for their diagnosis and repair, based on provenance information. Finally, we discuss on the application of these concepts to the broader domain of the Web of Data: Data with a Purpose.
Similar to Health Insurance Predictive Analysis with Hadoop and Machine Learning. JULIEN CABOT at Big Data Spain 2012 (20)
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Insights can only be as good as the data. The data quality domain is enormously large, so you need to understand your company pain points to know what to focus on first.
https://www.bigdataspain.org/2017/talk/big-data-big-quality
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
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https://www.bigdataspain.org/2017/talk/scaling-a-backend-for-a-big-data-and-blockchain-environment
Big Data Spain 2017
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All modern Big Data solutions, like Hadoop, Kafka or the rest of the ecosystem tools, are designed as distributed processes and as such include some sort of redundancy for High Availability.
https://www.bigdataspain.org/2017/talk/disaster-recovery-for-big-data
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Presentation: Boost Hadoop and Spark with in-memory technologies by Akmal Cha...Big Data Spain
In this presentation, attendees will see how to speed up existing Hadoop and Spark deployments by just making Apache Ignite responsible for RAM utilization. No code modifications, no new architecture from scratch!
https://www.bigdataspain.org/2017/talk/boost-hadoop-and-spark-with-in-memory-technologies
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Data science for lazy people, Automated Machine Learning by Diego Hueltes at ...Big Data Spain
The power of this new set of tools for Data Science. Is really easy to start applying these technics in your current workflow.
https://www.bigdataspain.org/2017/talk/data-science-for-lazy-people-automated-machine-learning
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Training Deep Learning Models on Multiple GPUs in the Cloud by Enrique Otero ...Big Data Spain
GPUs on the cloud as Infrastructure as a Service (IaaS) seem a commodity. However to efficiently distribute deep learning tasks on several GPUs is challenging.
https://www.bigdataspain.org/2017/talk/training-deep-learning-models-on-multiple-gpus-in-the-cloud
Big Data Spain 2017
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Unbalanced data: Same algorithms different techniques by Eric Martín at Big D...Big Data Spain
Unbalanced data is a specific data configuration that appears commonly in nature. Applying machine learning techniques to this kind of data is a difficult process, usually addressed by unbalanced reduction techniques.
https://www.bigdataspain.org/2017/talk/unbalanced-data-same-algorithms-different-techniques
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
State of the art time-series analysis with deep learning by Javier Ordóñez at...Big Data Spain
Time series related problems have traditionally been solved using engineered features obtained by heuristic processes.
https://www.bigdataspain.org/2017/talk/state-of-the-art-time-series-analysis-with-deep-learning
Big Data Spain 2017
November 16th - 17th
Trading at market speed with the latest Kafka features by Iñigo González at B...Big Data Spain
Not long ago only banks and hedge funds could afford doing automated and High Frequency Trading, that is, the ability to send buy commodities in microseconds intervals.
https://www.bigdataspain.org/2017/talk/trading-at-market-speed-with-the-latest-kafka-features
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...Big Data Spain
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day.
https://www.bigdataspain.org/2017/talk/apache-samza-jake-maes
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
The Analytic Platform behind IBM’s Watson Data Platform by Luciano Resende a...Big Data Spain
IBM has built a “Data Science Experience” cloud service that exposes Notebook services at web scale.
https://www.bigdataspain.org/2017/talk/the-analytic-platform-behind-ibms-watson-data-platform
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...Big Data Spain
Artificial Intelligence and Data-centric businesses.
https://www.bigdataspain.org/2017/talk/tbc
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Big Data Spain
Ten years ago there were rumours of the death of causal inference. Big data was supposed to enable us to rely on purely correlational data to predict and control the world.
https://www.bigdataspain.org/2017/talk/why-big-data-didnt-end-causal-inference
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Meme Index. Analyzing fads and sensations on the Internet by Miguel Romero at...Big Data Spain
The Meme of the Internet Index will be the new normal to analyze and predict facts and sensations which go around the Internet.
https://www.bigdataspain.org/2017/talk/meme-index-analyzing-fads-and-sensations-on-the-internet
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Vehicle Big Data that Drives Smart City Advancement by Mike Branch at Big Dat...Big Data Spain
Geotab is a leader in the expanding world of Internet of Things (IoT) and telematics industry with Big Data.
https://www.bigdataspain.org/2017/talk/vehicle-big-data-that-drives-smart-city-advancement
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
End of the Myth: Ultra-Scalable Transactional Management by Ricardo Jiménez-P...Big Data Spain
The talk will focus on explaining why operational databases do not scale due to limitations in legacy transactional management.
https://www.bigdataspain.org/2017/talk/end-of-the-myth-ultra-scalable-transactional-management
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Attacking Machine Learning used in AntiVirus with Reinforcement by Rubén Mart...Big Data Spain
In recent years Machine Learning (ML) and especially Deep Learning (DL) have achieved great success in many areas such as visual recognition, NLP or even aiding in medical research.
https://www.bigdataspain.org/2017/talk/attacking-machine-learning-used-in-antivirus-with-reinforcement
Big Data Spain 2017
16th - 17th Kinépolis Madrid
More people, less banking: Blockchain by Salvador Casquero at Big Data Spain ...Big Data Spain
Primary function of banking sector is promoting economic activity; which means “commerce”, exchanging what someone produces-has for something that someone consumes-desires.
https://www.bigdataspain.org/2017/talk/more-people-less-banking-blockchain
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Make the elephant fly, once again by Sourygna Luangsay at Big Data Spain 2017Big Data Spain
Bol.com has been an early Hadoop user: since 2008 where it was first built for a recommendation algorithm.
https://www.bigdataspain.org/2017/talk/make-the-elephant-fly-once-again
Big Data Spain 2017
16th - 17th Kinépolis Madrid
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
4. Benefits for Insurance Company?
Understand the subject of interest of the
patient to design customer-centric products
and marketing actions
Anticipate the psycho-social effect due to
Internet to prevent excessive consultations
(and reimbursements)
Predict the claims while monitoring the
request about symptoms and drugs
4
6. The data problem
Understand the semantic field of
Healthcare…used on Internet
Find correlation between the evolution of
claims and … many millions of unidentified
external variables
Find correlated variables… anticipating the
claims
We need some help from Machine Learning !
6
7. Correlation search in external datasets
Automated tokenization of Google search Socio-economical
message per posted date volume of symptom context from Open
and semantic tagging and drugs keywords Data initiatives
Trends of medical Trends of medical
Trends of socio-
keywords used in keywords searched in
economical factors
forums Google
Determination
Health claims by Correlation
coeff. (R²) sorted
act typology Search Machine matrix
7
8. Understand the semantic field of Healthcare
Message Word stemming, tagging Timelines of
tokenization and common word healthcare
by date filtering with NTLK key words
How to tag Healthcare
words?
1-Build a first list of
keywords
Healthcare
semantic
2-Enrich the list
with highly field
searched keywords keywords
database
3-Learn
automatically from
Wikipedia Medical
Categories
8
9. How to find correlations between time series?
Compare the evolution of the variable and the claims over the time
Find non linear regression and learn a polymorphic predictive function
f(x) from the dataset with Support Vector Regression (SVR)
y Problem to solve
f(x) + ε 1 𝑇
min 𝑤 . 𝑤
f(x) w 2
f(x) - ε
𝑦 𝑖 - (𝑤 𝑇 ·ϕ(x) + b) ≤ ε
(𝑤 𝑇 ·ϕ(x) + b) - 𝑦 𝑖 ≤ ε
Resolution
x • Stochastic gradient descendent
• Test the response through the coef.
of determination R²
Open source ML library helps!
9
10. Data Processing Profiles
The current volume of external data grabbed is large but not so huge (~10 Gb)
Data aggregation
Eg. Select … Group By Date
Data volume
Correlation search ~5Gb . 123 = 8,64 Tb
Eg. SVR computing
Data volume
We need Parallel Computing to divide
RAM requirement and time processing !
10
12. IT drivers
Requirements IT drivers
Aggregate data
from Mb to Gb file Data
while sequential IO Elasticity
aggregation
reading
SVR, NLP Large Tasks
execution time is CPU Elasticity
~100ms by task execution
Process many Tb Large RAM
in memory data RAM Elasticity
execution
Commodity HW
Increase the ROI of Low CAPEX
the research OSS SW
project while
decreasing the
TCO
Low OPEX Cost Elasticity
12
13. Available solutions
RAM Elasticity
OSS Software
CPU Elasticity
Cost Elasticity
IO Elasticity
Commodity
Hardware
RDBMS
In Memory analytics
HPC
Hadoop
With With With
repartitioning repartitioning repartitioning
AWS Elastic MapReduce
Through Task Through Task
13
15. Hadoop components
Custom App Dataming tools BI tools
Java, C#, PHP, … R, SAS Tableau, Pentaho, …
Hue Pig Streaming Hive
Hadoop GUI Flow processing MR scripting SQL-like querying
Oozie MapReduce Zookeeper
MR workflow Parallel processing framework Coordination service
Mahout Sqoop
Machine Learning
RDBMS integration
Hama
Bulk synchronous Flume
processing Data stream integration
Solr HBase
Full text search NoSQL on HDFS
HDFS
Distributed file storage
Grid of commodity hardware – storage and processing
15
16. General architecture of the platform
DataViz Application
• Store detailed
results for
• Store raw drill down
data AWS S3 Redis
• Store results
files
Core Task Master
Instance 1 Instance 1 Instance
Core Task
Instance 2 Instance 2
Task • For SVR and
2 x m2.4xlarge
Instances 3 NLP
processing,
&4 only
4 x m2.4xlarge
16
17. Data aggregation with Pig Job flow
Num_of_messages_by_date.pig
records = LOAD ‘/input/forums/messages.txt’
AS (str_date:chararray, message:chararray,
url:chararray);
date_grouped = GROUP records BY str_date
results = FOREACH date_grouped GENERATE
group, COUNT(records);
DUMP results;
17
18. Hadoop streaming
Hadoop streaming runs map/reduce jobs with any
executables or scripts through standard input and
standard output
It looks like that (on a cluster) :
cat input.txt | map.py | sort | reduce.py
Why Hadoop streaming?
Intensive use of NLTK for Natural Language Processing
Intensive use of NumPy and Sklearn for Machine Learning
18
19. Stemmed word distribution with Hadoop streaming, mapper.py
Stem_distribution_by_date/mapper.py
import sys
import nltk
from nltk.tokenize import regexp_tokenize
from nltk.stem.snowball import FrenchStemmer
# input comes from STDIN (standard input)
for line in sys.stdin:
line = line.strip()
str_date, message, url = line.split(";")
stemmer = FrenchStemmer("french")
tokens = regexp_tokenize(message, pattern='w+')
for token in tokens:
word = stemmer.stem(token)
if len(word) >= 3:
print '%s;%s' % (word, str_date)
19
20. Stemmed word distribution with Hadoop streaming, reducer.py
Stem_distribution_by_date/reducer.py
import sys
import json
from itertools import groupby
from operator import itemgetter
from nltk.probability import FreqDist
def read(f):
for line in f:
line = line.strip()
yield line.split(';')
data = read(sys.stdin)
for current_stem, group in groupby(data, itemgetter(0)):
values = [item[1] for item in group]
freq_dist = FreqDist()
print "%s;%s" % (current_stem, json.dumps(freq_dist))
20
22. Conclusions
The correlation search identifies currently 462 variables correlated with a R² >= 80%
and a lag >= 1 month
Amazon Elastic MapReduce provides the elasticity required by the morphology of
the jobs and the cost elasticity
Monthly cost with zero activity : < 5 €
Monthly cost with intensive activity : < 1 000 €
The equivalent cost of the platform would be around 50 000 €
The S3 transfer overhead is not a problem due the volume of stored data
While Correlation search processing, only 80% max of the virtual CPU are
used due to job scheduling with a parallelism factor of 36 instead of 48
regarding SMP
22
23. Future works
Data mining
Increase the number of data sources
Testing the robustness of the predictive model over the time
Reducing the over fitting of the correlation
Enhance the correlation search for word while testing combinations
IT
Switch only the correlation search to a map reduce engine for SMP
architecture and cluster of cores, inspired by the Stanford Phoenix and the
Nokia Disco engine
Industrialize the data mining components as a platform for generalization to
IARD insurance, banking, e-commerce, telecoms and retails
23
24. OCTO in a nutshell
Big data Analytics Offer
Business case and benchmark studies
Business Proof of Concept
Data feeds : Web Trends
Big Data and Analytics architecture design
Big data project delivery
Training, seminar : Big Data, Hadoop
IT Consulting firm OCTO offices
Established in 1998
175 employees
19,5 million turnover worldwide (2011)
Verticals-based organization
Banking – Financial Services
Insurance
Media – Internet – Leisure
Industry – Distribution
Telecom – Services
24