These are the slides from a plenary panel that I participated in at IEEE Cloud 2011 on July 5, 2011 in Washington, D.C. I discussed the Open Science Data Cloud and concluded the talk by three research questions
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
This is a talk titled "Cloud-Based Services For Large Scale Analysis of Sequence & Expression Data: Lessons from Cistrack" that I gave at CAMDA 2009 on October 6, 2009.
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).
The PRP is a partnership of more than 50 institutions, led by researchers at UC San Diego and UC Berkeley and includes the National Science Foundation, Department of Energy, and multiple research universities in the US and around the world. The PRP builds on the optical backbone of Pacific Wave, a joint project of CENIC and the Pacific Northwest GigaPOP (PNWGP) to create a seamless research platform that encourages collaboration on a broad range of data-intensive fields and projects.
The Pacific Research Platform (PRP) aims to achieve transparent and rapid data access among collaborating scientists at multiple institutions through an integrated implementation of data-focused networking that extends the university campus Science DMZ model to a regional, national, and, eventually, a global scale.
PRP researchers are routinely achieving high-performance end-to-end networking from their labs to their collaborators’ labs and data centers, traversing multiple, heterogeneous Science DMZs and wide-area networks connecting multiple campus gateways, enabling researchers across the partnership to transfer data over dedicated optical lightpaths at speeds from 10Gb/s to 100Gb/s.
This is a talk I gave at a Northwestern University - Complete Genomics Workshop on April 21, 2011 about using clouds to support research in genomics and related areas.
New learning technologies seem likely to transform much of science, as they are already doing for many areas of industry and society. We can expect these technologies to be used, for example, to obtain new insights from massive scientific data and to automate research processes. However, success in such endeavors will require new learning systems: scientific computing platforms, methods, and software that enable the large-scale application of learning technologies. These systems will need to enable learning from extremely large quantities of data; the management of large and complex data, models, and workflows; and the delivery of learning capabilities to many thousands of scientists. In this talk, I review these challenges and opportunities and describe systems that my colleagues and I are developing to enable the application of learning throughout the research process, from data acquisition to analysis.
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.
The Matsu Project - Open Source Software for Processing Satellite Imagery DataRobert Grossman
The Matsu Project is an Open Cloud Consortium project that is developing open source software for processing satellite imagery data using Hadoop, OpenStack and R.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
Big Data, Big Computing, AI, and Environmental ScienceIan Foster
I presented to the Environmental Data Science group at UChicago, with the goal of getting them excited about the opportunities inherent in big data, big computing, and AI--and to think about how to collaborate with Argonne in those areas. We had a great and long conversation about Takuya Kurihana's work on unsupervised learning for cloud classification. I also mentioned our work making NASA and CMIP data accessible on AI supercomputers.
Cloud Testbeds for Standards Development and InnovationAlan Sill
Invited talk given at the 2014 Chip-to-Cloud Security Forum "Advances in Securing Embedded, Mobile and Cloud Services and Ecosystems" in the seminar session on "Procurement, SLAs, and Standardisation on a Global Scale." In this talk, Dr. Sill reviews the history of cloud and grid computing, the formation and charter description for Phases I and II of the US National Institute of Standards and Technology (NIST) "SAJACC" working group, and brings the discussion up to date with an overview of current "DevOps"-oriented cloud standards and software interoperability hands-on testing efforts worldwide.
This is a talk titled "Cloud-Based Services For Large Scale Analysis of Sequence & Expression Data: Lessons from Cistrack" that I gave at CAMDA 2009 on October 6, 2009.
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).
The PRP is a partnership of more than 50 institutions, led by researchers at UC San Diego and UC Berkeley and includes the National Science Foundation, Department of Energy, and multiple research universities in the US and around the world. The PRP builds on the optical backbone of Pacific Wave, a joint project of CENIC and the Pacific Northwest GigaPOP (PNWGP) to create a seamless research platform that encourages collaboration on a broad range of data-intensive fields and projects.
The Pacific Research Platform (PRP) aims to achieve transparent and rapid data access among collaborating scientists at multiple institutions through an integrated implementation of data-focused networking that extends the university campus Science DMZ model to a regional, national, and, eventually, a global scale.
PRP researchers are routinely achieving high-performance end-to-end networking from their labs to their collaborators’ labs and data centers, traversing multiple, heterogeneous Science DMZs and wide-area networks connecting multiple campus gateways, enabling researchers across the partnership to transfer data over dedicated optical lightpaths at speeds from 10Gb/s to 100Gb/s.
This is a talk I gave at a Northwestern University - Complete Genomics Workshop on April 21, 2011 about using clouds to support research in genomics and related areas.
New learning technologies seem likely to transform much of science, as they are already doing for many areas of industry and society. We can expect these technologies to be used, for example, to obtain new insights from massive scientific data and to automate research processes. However, success in such endeavors will require new learning systems: scientific computing platforms, methods, and software that enable the large-scale application of learning technologies. These systems will need to enable learning from extremely large quantities of data; the management of large and complex data, models, and workflows; and the delivery of learning capabilities to many thousands of scientists. In this talk, I review these challenges and opportunities and describe systems that my colleagues and I are developing to enable the application of learning throughout the research process, from data acquisition to analysis.
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.
The Matsu Project - Open Source Software for Processing Satellite Imagery DataRobert Grossman
The Matsu Project is an Open Cloud Consortium project that is developing open source software for processing satellite imagery data using Hadoop, OpenStack and R.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
Big Data, Big Computing, AI, and Environmental ScienceIan Foster
I presented to the Environmental Data Science group at UChicago, with the goal of getting them excited about the opportunities inherent in big data, big computing, and AI--and to think about how to collaborate with Argonne in those areas. We had a great and long conversation about Takuya Kurihana's work on unsupervised learning for cloud classification. I also mentioned our work making NASA and CMIP data accessible on AI supercomputers.
Cloud Testbeds for Standards Development and InnovationAlan Sill
Invited talk given at the 2014 Chip-to-Cloud Security Forum "Advances in Securing Embedded, Mobile and Cloud Services and Ecosystems" in the seminar session on "Procurement, SLAs, and Standardisation on a Global Scale." In this talk, Dr. Sill reviews the history of cloud and grid computing, the formation and charter description for Phases I and II of the US National Institute of Standards and Technology (NIST) "SAJACC" working group, and brings the discussion up to date with an overview of current "DevOps"-oriented cloud standards and software interoperability hands-on testing efforts worldwide.
Understanding the Big Picture of e-ScienceAndrew Sallans
A. Sallans. "Understanding the Big Picture of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011
Metadata and Semantics Research Conference, Manchester, UK 2015
Research Objects: why, what and how,
In practice the exchange, reuse and reproduction of scientific experiments is hard, dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: codes fork, data is updated, algorithms are revised, workflows break, service updates are released. Neither should they be viewed just as second-class artifacts tethered to publications, but the focus of research outcomes in their own right: articles clustered around datasets, methods with citation profiles. Many funders and publishers have come to acknowledge this, moving to data sharing policies and provisioning e-infrastructure platforms. Many researchers recognise the importance of working with Research Objects. The term has become widespread. However. What is a Research Object? How do you mint one, exchange one, build a platform to support one, curate one? How do we introduce them in a lightweight way that platform developers can migrate to? What is the practical impact of a Research Object Commons on training, stewardship, scholarship, sharing? How do we address the scholarly and technological debt of making and maintaining Research Objects? Are there any examples
I’ll present our practical experiences of the why, what and how of Research Objects.
Science Services and Science Platforms: Using the Cloud to Accelerate and Dem...Ian Foster
Ever more data- and compute-intensive science makes computing increasingly important for research. But for advanced computing infrastructure to benefit more than the scientific 1%, we need new delivery methods that slash access costs, new sustainability models beyond direct research funding, and new platform capabilities to accelerate the development of new, interoperable tools and services.
The Globus team has been working towards these goals since 2010. We have developed software-as-a-service methods that move complex and time-consuming research IT tasks out of the lab and into the cloud, thus greatly reducing the expertise and resources required to use them. We have demonstrated a subscription-based funding model that engages research institutions in supporting service operations. And we are now also showing how the platform services that underpin Globus applications can accelerate the development and use of an integrated ecosystem of advanced science applications, such as NCAR’s Research Data Archive and OSG Connect, thus enabling access to powerful data and compute resources by many more people than is possible today.
In this talk, I introduce Globus services and the underlying Globus platform. I present representative applications and discuss opportunities that this platform presents for both small science and large facilities.
Cloud Dataverse: A Data repository platform for an OpenStack CloudMerce Crosas
In the last 10 years, the Dataverse project has been a leader in open-source repository software for sharing and archiving research data. Dataverse has an active, growing community of developers and users, with 22 installations of the software around the world. The Harvard Dataverse repository alone hosts 70,000 datasets, 330,000 data files, with contributions from more than 500 institutions.
Cloud Dataverse combines Dataverse and OpenStack by storing datasets in OpenStack’s Swift Object storage and replicating datasets from Dataverse repositories world-wide to the cloud(s) -- offering enormous value to both the Dataverse and OpenStack communities. It provides Dataverse users the ability to host larger datasets and efficiently compute on data from around the world using OpenStack’s compute services. It provides OpenStack users with a repository system that is much richer than Amazon’s Public Datasets service.
Leveraging Open Source Technologies to Enable Scientific Archiving and Discovery; Steve Hughes, NASA; Data Publication Repositories
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
Positioning University of California Information Technology for the Future: S...Larry Smarr
05.02.15
Invited Talk
The Vice Chancellor of Research and Chief Information Officer Summit
“Information Technology Enabling Research at the University of California”
Title: Positioning University of California Information Technology for the Future: State, National, and International IT Infrastructure Trends and Directions
Oakland, CA
Research results in peer-reviewed publications are reproducible, right? If only it was so clear cut. With high profile paper retractions and pushes for better data sharing by funders, publishers and the community, the spotlight is now focussing on the whole way research is conducted around the world.
This talk from the Software Sustainability Institute's Collaborations Workshop 2014 describes how cloud computing, with Microsoft Azure, is helping researchers realize the goals of scientific reproducibility.
Find out more at www.azure4research.com
Accelerating data-intensive science by outsourcing the mundaneIan Foster
Talk at eResearch New Zealand Conference, June 2011 (given remotely from Italy, unfortunately!)
Abstract: Whitehead observed that "civilization advances by extending the number of important operations which we can perform without thinking of them." I propose that cloud computing can allow us to accelerate dramatically the pace of discovery by removing a range of mundane but timeconsuming research data management tasks from our consciousness. I describe the Globus Online system that we are developing to explore these possibilities, and propose milestones for evaluating progress towards smarter science.
Cloud Standards in the Real World: Cloud Standards Testing for DevelopersAlan Sill
Learn about standards studied in the US National Science Foundation Cloud and Autonomic Computing Industry/University Cooperative Research Center Cloud Standards Testing Lab and how you can get involved to extend the successes from these results in your own cloud software settings. Presented at the O'Reilly OSCON 2014 Open Cloud Day.
Video available at https://www.youtube.com/watch?v=eD2h0SqC7tY
Some Frameworks for Improving Analytic Operations at Your CompanyRobert Grossman
I review three frameworks for analytic operations that are designed to improve the value obtained when deploying analytic models into products, services and internal operations.
This a talk that I gave at BioIT World West on March 12, 2019. The talk was called: A Gen3 Perspective of Disparate Data:From Pipelines in Data Commons to AI in Data Ecosystems.
Crossing the Analytics Chasm and Getting the Models You Developed DeployedRobert Grossman
There are two cultures in data science and analytics - those that develop analytic models and those that deploy analytic models into operational systems. In this talk, we review the life cycle of analytic models and provide an overview of some of the approaches that have been developed for managing analytic models and workflows and for deploying them, including using analytic engines and analytic containers . We give a quick overview of languages for analytic models (PMML) and analytic workflows (PFA). We also describe the emerging discipline of AnalyticOps that has borrowed some of the techniques of DevOps.
This is an overview of the Data Biosphere Project, its goals, its architecture, and the three core projects that form its foundation. We also discuss data commons.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
This is a talk that I gave at the Molecular Medicine Tri Conference on data commons and data sharing to accelerate research discoveries and improve patient outcomes. It also covers how your organization can build a data commons using the Open Commons Consortium's Data Commons Framework and the University of Chicago's Gen3 data commons platform.
Practical Methods for Identifying Anomalies That Matter in Large DatasetsRobert Grossman
Robert L. Grossman, Practical Methods for Identifying Anomalies That Matter in Large Datasets, O’Reilly, Strata + Hadoop World, San Jose, California, February 20, 2015.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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/
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
Open Science Data Cloud (IEEE Cloud 2011)
1. OCC Open Science Data Cloud(www.opensciencedatacloud.org) Robert Grossman University of ChicagoOpen Cloud Consortium Open Data Group July 5, 2011 1
2. I’ll describe a new project (the Open Science Data Cloud) and three research questions generated by the project.
3. Open Science Data Cloud The OCC is a not-for-profit supporting the scientific community by operating cloud infrastructure.
4. The OSDC is a hosted distributed facility managed by the OCC that: Manages & archives medium and large size datasets. Provides computational resources to analyze them. Provides networking to share the datasets with your colleagues and with the public.
16. Variety of analysis Scientist with laptop Wide Open Science Data Cloud Med High energy physics, astronomy Low Data Size Medium to Large Small Very Large Dedicated infrastructure No infrastructure General infrastructure
21. Project Matsu 2: An Elastic Cloud For Earth Science Data 10 matsu.opencloudconsortium.org
22. Research Questions Develop technology to encapsulate a scientist’s data and analysis tools and to export, save and move these between clouds. Develop protocols, utilities, and applications so that new racks and containers can be added to data clouds with minimal human involvement. Develop technology to support the long term, low cost preservation of data in clouds.