The Next-Generation sequencing data-deluge requires storage and compute services to be provisioned at an ever-increasing rate. Can Cloud (and last decade's buzzword, Grid), help us?
Talk given at the NHGRI Cloud computing workshop, 2010.
The computational requirements of next generation sequencing is placing a huge demand on IT organisations .
Building compute clusters is now a well understood and relatively straightforward problem. However, NGS sequencing applications require large amounts of storage, and high IO rates.
This talk details our approach for providing storage for next-gen sequencing applications.
Talk given at BIO-IT World, Europe, 2009.
Next-generation sequencing: Data mangementGuy Coates
Next-generation sequencing is producing vast amounts of data. Providing storage and compute is only half the battle. Researchers and IT staff need to be able to "manage" data, in order to stay productive.
Talk given at BIO-IT World, Europe 2010.
Next generation genomics: Petascale data in the life sciencesGuy Coates
Keynote presentation at OGF 28.
The year 2000 saw the release of "The" human genome, the product of a the combined sequencing effort of the whole planet. In 2010, single institutions are sequencing thousands of genomes a year, producing petabytes of data. Furthermore, many of the large scale sequencing projects are based around international collaboration and consortia. The talk will explore how Grid and Cloud technologies are being used to share genomics data around the planet, revolutionizing life science research.
The computational requirements of next generation sequencing is placing a huge demand on IT organisations .
Building compute clusters is now a well understood and relatively straightforward problem. However, NGS sequencing applications require large amounts of storage, and high IO rates.
This talk details our approach for providing storage for next-gen sequencing applications.
Talk given at BIO-IT World, Europe, 2009.
Next-generation sequencing: Data mangementGuy Coates
Next-generation sequencing is producing vast amounts of data. Providing storage and compute is only half the battle. Researchers and IT staff need to be able to "manage" data, in order to stay productive.
Talk given at BIO-IT World, Europe 2010.
Next generation genomics: Petascale data in the life sciencesGuy Coates
Keynote presentation at OGF 28.
The year 2000 saw the release of "The" human genome, the product of a the combined sequencing effort of the whole planet. In 2010, single institutions are sequencing thousands of genomes a year, producing petabytes of data. Furthermore, many of the large scale sequencing projects are based around international collaboration and consortia. The talk will explore how Grid and Cloud technologies are being used to share genomics data around the planet, revolutionizing life science research.
Architectures for Data Commons (XLDB 15 Lightning Talk)Robert Grossman
These are the slides from a 5 minute Lightning Talk that I gave at XLDB 2015 on May 19, 2015 at Stanford. It is based in part on our experiences developing the NCI Genomic Data Commons (GDC).
10 Popular Hadoop Technical Interview QuestionsZaranTech LLC
Big Data has been attested as one of the fastest growing technologies of this decade and thus potent enough to produce a large number of jobs. While enterprises across industrial stretch have started building teams, Hadoop technical interview questions could vary from simple definitions to critical case studies. Let’s take quick glimpse at the most obvious ones.
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
The title of this talk is a crass attempt to be catchy and topical, by referring to the recent victory of Watson in Jeopardy.
My point (perhaps confusingly) is not that new computer capabilities are a bad thing. On the contrary, these capabilities represent a tremendous opportunity for science. The challenge that I speak to is how we leverage these capabilities without computers and computation overwhelming the research community in terms of both human and financial resources. The solution, I suggest, is to get computation out of the lab—to outsource it to third party providers.
Abstract follows:
We have made much progress over the past decade toward effective distributed cyberinfrastructure. In big-science fields such as high energy physics, astronomy, and climate, thousands benefit daily from tools that enable the distributed management and analysis of vast quantities of data. But we now face a far greater challenge. Exploding data volumes and new research methodologies mean that many more--ultimately most?--researchers will soon require similar capabilities. How can we possible supply information technology (IT) at this scale, given constrained budgets? Must every lab become filled with computers, and every researcher an IT specialist?
I propose that the answer is to take a leaf from industry, which is slashing both the costs and complexity of consumer and business IT by moving it out of homes and offices to so-called cloud providers. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity, empowering investigators with new capabilities and freeing them to focus on their research.
I describe work we are doing to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date, and suggest a path towards large-scale delivery of these capabilities. I also suggest that these developments are part of a larger "revolution in scientific affairs," as profound in its implications as the much-discussed "revolution in military affairs" resulting from more capable, low-cost IT. I conclude with some thoughts on how researchers, educators, and institutions may want to prepare for this revolution.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
Detailed presentation on big data hadoop +Hadoop Project Near Duplicate Detec...Ashok Royal
Bigdata Hadoop, Its components and a Hadoop project is described in Details.
Visit http://hadoop-beginners.blogspot.com to see Hadoop Tutorials.
Thanks for the visit. :)
A talk at the RPI-NSF Workshop on Multiscale Modeling of Complex Data, September 12, 2011, Troy NY, USA.
We have made much progress over the past decade toward effectively
harnessing the collective power of IT resources distributed across the
globe. In fields such as high-energy physics, astronomy, and climate,
thousands benefit daily from tools that manage and analyze large
quantities of data produced and consumed by large collaborative teams.
But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that far more--ultimately
most?--researchers will soon require capabilities not so different from those used by these big-science teams. How is the general population of researchers and institutions to meet these needs? Must every lab be filled
with computers loaded with sophisticated software, and every researcher become an information technology (IT) specialist? Can we possibly afford to equip our labs in this way, and where would we find the experts to operate them?
Consumers and businesses face similar challenges, and industry has
responded by moving IT out of homes and offices to so-called cloud providers (e.g., GMail, Google Docs, Salesforce), slashing costs and complexity. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity. More importantly, we can free researchers from the burden of managing IT, giving them back their time to focus on research and empowering them to go beyond the scope of what was previously possible.
I describe work we are doing at the Computation Institute to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date and suggest a path towards
large-scale delivery of these capabilities.
Introductory Big Data presentation given during one of our Sizing Servers Lab user group meetings. The presentation is targeted towards an audience of about 20 SME employees. It also contains a short description of the work packages for our BIg Data project proposal that was submitted in March.
Roots tech 2013 Big Data at Ancestry (3-22-2013) - no animationsWilliam Yetman
This was one of my first presentations on Big Data at Ancestry.com. The audience was split between Family Historians interested in the Technology and Developers interested in our Big Data Story. So the presentation is a mix. I think there is plenty for a someone with an interest in technology and enough meat for a "technologist".
Keep this in mind as you look at this presentation.
Thanks,
-Bill-
Keynote given at BOSC, 2010.
Does the hype surrounding cloud match the reality?
Can we use them to solve the problems in provisioning IT services to support next-generation sequencing?
Architectures for Data Commons (XLDB 15 Lightning Talk)Robert Grossman
These are the slides from a 5 minute Lightning Talk that I gave at XLDB 2015 on May 19, 2015 at Stanford. It is based in part on our experiences developing the NCI Genomic Data Commons (GDC).
10 Popular Hadoop Technical Interview QuestionsZaranTech LLC
Big Data has been attested as one of the fastest growing technologies of this decade and thus potent enough to produce a large number of jobs. While enterprises across industrial stretch have started building teams, Hadoop technical interview questions could vary from simple definitions to critical case studies. Let’s take quick glimpse at the most obvious ones.
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
The title of this talk is a crass attempt to be catchy and topical, by referring to the recent victory of Watson in Jeopardy.
My point (perhaps confusingly) is not that new computer capabilities are a bad thing. On the contrary, these capabilities represent a tremendous opportunity for science. The challenge that I speak to is how we leverage these capabilities without computers and computation overwhelming the research community in terms of both human and financial resources. The solution, I suggest, is to get computation out of the lab—to outsource it to third party providers.
Abstract follows:
We have made much progress over the past decade toward effective distributed cyberinfrastructure. In big-science fields such as high energy physics, astronomy, and climate, thousands benefit daily from tools that enable the distributed management and analysis of vast quantities of data. But we now face a far greater challenge. Exploding data volumes and new research methodologies mean that many more--ultimately most?--researchers will soon require similar capabilities. How can we possible supply information technology (IT) at this scale, given constrained budgets? Must every lab become filled with computers, and every researcher an IT specialist?
I propose that the answer is to take a leaf from industry, which is slashing both the costs and complexity of consumer and business IT by moving it out of homes and offices to so-called cloud providers. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity, empowering investigators with new capabilities and freeing them to focus on their research.
I describe work we are doing to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date, and suggest a path towards large-scale delivery of these capabilities. I also suggest that these developments are part of a larger "revolution in scientific affairs," as profound in its implications as the much-discussed "revolution in military affairs" resulting from more capable, low-cost IT. I conclude with some thoughts on how researchers, educators, and institutions may want to prepare for this revolution.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
Detailed presentation on big data hadoop +Hadoop Project Near Duplicate Detec...Ashok Royal
Bigdata Hadoop, Its components and a Hadoop project is described in Details.
Visit http://hadoop-beginners.blogspot.com to see Hadoop Tutorials.
Thanks for the visit. :)
A talk at the RPI-NSF Workshop on Multiscale Modeling of Complex Data, September 12, 2011, Troy NY, USA.
We have made much progress over the past decade toward effectively
harnessing the collective power of IT resources distributed across the
globe. In fields such as high-energy physics, astronomy, and climate,
thousands benefit daily from tools that manage and analyze large
quantities of data produced and consumed by large collaborative teams.
But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that far more--ultimately
most?--researchers will soon require capabilities not so different from those used by these big-science teams. How is the general population of researchers and institutions to meet these needs? Must every lab be filled
with computers loaded with sophisticated software, and every researcher become an information technology (IT) specialist? Can we possibly afford to equip our labs in this way, and where would we find the experts to operate them?
Consumers and businesses face similar challenges, and industry has
responded by moving IT out of homes and offices to so-called cloud providers (e.g., GMail, Google Docs, Salesforce), slashing costs and complexity. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity. More importantly, we can free researchers from the burden of managing IT, giving them back their time to focus on research and empowering them to go beyond the scope of what was previously possible.
I describe work we are doing at the Computation Institute to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date and suggest a path towards
large-scale delivery of these capabilities.
Introductory Big Data presentation given during one of our Sizing Servers Lab user group meetings. The presentation is targeted towards an audience of about 20 SME employees. It also contains a short description of the work packages for our BIg Data project proposal that was submitted in March.
Roots tech 2013 Big Data at Ancestry (3-22-2013) - no animationsWilliam Yetman
This was one of my first presentations on Big Data at Ancestry.com. The audience was split between Family Historians interested in the Technology and Developers interested in our Big Data Story. So the presentation is a mix. I think there is plenty for a someone with an interest in technology and enough meat for a "technologist".
Keep this in mind as you look at this presentation.
Thanks,
-Bill-
Keynote given at BOSC, 2010.
Does the hype surrounding cloud match the reality?
Can we use them to solve the problems in provisioning IT services to support next-generation sequencing?
This is the course that was presented by James Liddle and Adam Vile for Waters in September 2008.
The book of this course can be found at: http://www.lulu.com/content/4334860
Healthcare systems around the world are looking to Precision Medicine -- care decisions tailored for the individual patient -- as a means to drive better care outcomes at lower cost. Today, the most promising technology that has made this possible in certain diseases like cancer is sequencing a patient's genome. For infectious diseases, sequencing has revolutionized our understanding of outbreaks and how they spread. Genome sequencing has progressed significantly in the past decade to improve throughput and lower costs by 100X or more. It is a data and compute intensive endeavor, which most biomedical research and care delivery networks are not equipped to handle. This session features Dr. Swaine Chen from the Genome Institute of Singapore, and the Broad Institute Cromwell team, discussing the problem of dealing with the scale of genomic data, and how they solved these to deliver results.
The need to process huge data is increasing day by day. Processing huge data involves compute, network and storage. In terms of Big Data, What it takes to innovate and what is innovation at the end? This talk provide high level details on the need of big data and capabilities of Mapr converged data platform.
Speaker: Vijaya Saradhi Uppaluri, Technical Director at MapR Technologies
Research and technology explosion in scale-out storageJeff Spencer
A view of the directions storage is taking in science & technology from Ryan Sayre, technical strategist in the office of the CTO for EMC Isilon, using examples from recent work in life science genomics and other industries taking advantage of the combination of extreme computing (HPC) and big data. As presented at the Bull sponsored Science & Innovation 2013 conference Westminster.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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.
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/
7. Past Collaborations Data Sequencing Centre + DCC Sequencing centre Sequencing centre Sequencing centre Sequencing centre
8. Future Collaborations Collaborations are short term: 18 months-3 years. Sequencing Centre 3 Sequencing Centre 1 Sequencing Centre 2A Sequencing Centre 2B Federated access
9. Genomics Data Unstructured data (flat files) Data size per Genome Structured data (databases) DAS, bioMART etc ? Intensities / raw data (2TB) Alignments (200 GB) Sequence + quality data (500 GB) Variation data (1GB) Individual features (3MB)
16. iRODS ICAT Catalogue database Rule Engine Implements policies Irods Server Data on disk User interface WebDAV, icommands,fuse Irods Server Data in database Irods Server Data in S3
17.
18.
19. Allows user at institute A to seamlessly access data at institute B in a controlled manner.
47. Put VMs on compute that is “attached” to the data. Data CPU CPU CPU CPU Data CPU CPU CPU CPU VM
48. Proto-Example: Ssaha trace search Hash Table (320 GB) trace Database ~30TB 1. hash database CPU CPU CPU CPU hash hash hash hash 2 .Distribute hash across machines query 3. Run query in parallel
56. Compute architecture VS CPU CPU CPU Fat Network Posix Global filesystem CPU CPU CPU CPU thin network Local storage Local storage Local storage Local storage Batch schedular hadoop/S3 Data-store Data-store