An information system is designed to capture, store, process, and provide access to information to support organizational processes and decision making. The document discusses the design of a resource registry information system to support a hybrid cloud-based infrastructure. The resource registry collects and manages metadata about software systems, resources, and their status to enable service discovery, monitoring, and elastic resource allocation. It implements an open model to flexibly support evolving resource types and management needs over the long lifespan of the infrastructure.
Building a Tiered Digital Storage Environment on User-Defined Metadata to Ena...inside-BigData.com
In this deck, David Fellinger from the iRODS Consortium writes that, as eResearch has evolved to accommodate sensor and
other types of big data, iRODS can enable complete workflow control, data lifecycle management, and present discoverable data sets with assured traceability and reproducibility.
"The iRODS software is very flexible with respect to changes based on new circumstances or new sensors. Conditions may
dictate various priorities. For example, drought conditions may change the priority of sensor information on a Smart Farm. Weather conditions may change the priority and analysis requirements of various atmospheric sensors in a climate research facility. In all cases, iRODS can be re-configured easily to accommodate changing requirements.
Data ingestion is far more complex than a simple file create operation. It is, in fact, the first operation in a process workflow that must be metadata driven to enable efficiency of analysis. The data must be properly cataloged, and apportioned to diminish research cycle time. HSM systems are simply designed to build archives. The use of iRODS to manage tiered storage elements enables both the process and the subsequent distribution operations allowing researchers in many areas to gather, process, and discover data of interest."
Learn more: https://wp.me/p3RLHQ-lfx
and
https://irods.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software.
EOSC-hub brings together multiple service providers to create the Hub: a single contact point for European researchers and innovators to discover, access, use and reuse a broad spectrum of resources for advanced data-driven research.
This presentation introduces the services on offer to scientists of all disciplines
Birgit Plietzsch “RDM within research computing support” SALCTG June 2013SALCTG
An overview of Research Data Management: the research process from developing ideas to preservation of data; funder perspectives, the impact on the wider service, Data Asset Frameworks, preservation and access, and cost implications.
Slide deck from presentation on Oct 8, 2015 at Johns Hopkins University. Topic is Digital Curation in Art Museums: Technology, People, Process. #jhudigcur
Building a Tiered Digital Storage Environment on User-Defined Metadata to Ena...inside-BigData.com
In this deck, David Fellinger from the iRODS Consortium writes that, as eResearch has evolved to accommodate sensor and
other types of big data, iRODS can enable complete workflow control, data lifecycle management, and present discoverable data sets with assured traceability and reproducibility.
"The iRODS software is very flexible with respect to changes based on new circumstances or new sensors. Conditions may
dictate various priorities. For example, drought conditions may change the priority of sensor information on a Smart Farm. Weather conditions may change the priority and analysis requirements of various atmospheric sensors in a climate research facility. In all cases, iRODS can be re-configured easily to accommodate changing requirements.
Data ingestion is far more complex than a simple file create operation. It is, in fact, the first operation in a process workflow that must be metadata driven to enable efficiency of analysis. The data must be properly cataloged, and apportioned to diminish research cycle time. HSM systems are simply designed to build archives. The use of iRODS to manage tiered storage elements enables both the process and the subsequent distribution operations allowing researchers in many areas to gather, process, and discover data of interest."
Learn more: https://wp.me/p3RLHQ-lfx
and
https://irods.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software.
EOSC-hub brings together multiple service providers to create the Hub: a single contact point for European researchers and innovators to discover, access, use and reuse a broad spectrum of resources for advanced data-driven research.
This presentation introduces the services on offer to scientists of all disciplines
Birgit Plietzsch “RDM within research computing support” SALCTG June 2013SALCTG
An overview of Research Data Management: the research process from developing ideas to preservation of data; funder perspectives, the impact on the wider service, Data Asset Frameworks, preservation and access, and cost implications.
Slide deck from presentation on Oct 8, 2015 at Johns Hopkins University. Topic is Digital Curation in Art Museums: Technology, People, Process. #jhudigcur
This presentation contains a broad introduction to big data and its technologies.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.
Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.
EUDAT Research Data Management | www.eudat.eu | EUDAT
| www.eudat.eu | The presentation gives an introduction to Research Data Management, explaining why it is important to manage and share data.
November 2016
Persistent Identifiers in EUDAT services| www.eudat.eu | EUDAT
| www.eudat.eu | The EUDAT data domain handles registered data. Each digital object should have a persistent identifier. This persistent identifier is used for: Replica identification; Identification of the repository of record (in the case of replication); Querying of additional information; Checksum (time stamped)...
Research Data Services: The EUDAT B2SERVICE SUITE | www.eudat.eu | EUDAT
| www.eudat.eu | EUDAT offers common data services, supporting multiple research communities as well as individuals, through a geographically distributed, resilient network connecting general purpose data centres and community-specific data repositories.
B2STAGE- how to shift large amounts of data| www.eudat.eu | EUDAT
| www.eudat.eu | B2STAGE is a reliable, efficient, light-weight and easy-to-use service to transfer research data sets between EUDAT storage resources and high-performance computing (HPC) workspaces.
2010 EGITF Amsterdam - Gap between GRID and HumanitiesDirk Roorda
How useful/relevant is GRID and High Performance Computing in its current form for the Humanities, especially within the European Infrastructure projects CLARIN, DARIAH and CESSDA? We need virtual use cases!
Big data analytics and machine intelligence v5.0Amr Kamel Deklel
Why big data
What is big data
When big data is big data
Big data information system layers
Hadoop echo system
What is machine learning
Why machine learning with big data
Introduction to Persistent Identifiers| www.eudat.eu | EUDAT
| www.eudat.eu | What are persistent identifiers? Why use persistent identifiers? Different persistent identifier systems; The HANDLE system; EPIC PID system; Policies; Use cases
Ver 2 July 2017
DDI Data Description Statistics Protection Software is a tool which enables easy anonymization processes of DDI standard univariate statistics. It was developed primarily to address the issue of statistical disclosure control for single variable aggregated data, publically distributed to particularly provide information to scientific researchers to promote detailed official statistics microdata use for scientific purposes. It enables quick and easy automatic data protection by performing data protection methods such as bracketing, top- and bottom-coding, variable (information) removal, numeric descriptive statistics protection and low frequency protection following the minimum frequency rule. In contrast to existing microdata and aggregated data anonymization tools, the developed software protects aggregated data directly in the XML code.
The presentation gives an overview of what metadata is and why it is important. It also addresses the benefits that metadata can bring and offers advice and tips on how to produce good quality metadata and, to close, how EUDAT uses metadata in the B2FIND service.
November 2016
FAIR Data in Trustworthy Data Repositories Webinar - 12-13 December 2016| www...EUDAT
| www.eudat.eu | This webinar was co-organised by DANS, EUDAT and OpenAIRE and was held on 12th and 13th December 2016.
Everybody wants to play FAIR, but how do we put the principles into practice?
There is a growing demand for quality criteria for research datasets. In this webinar we will argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgements about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository.
In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders.
The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets.
In this webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.
It describe cloud infrastructure required for big data. It discusses the object storage and virtualization required for big data. Ceph is discussed as example.
Delivering Faster Insights with a Logical Data FabricDenodo
Watch full webinar here: https://bit.ly/38B5yOW
We will learn from our speakers today how a logical data fabric helps organisations realise faster insights. They will touch on the recent Forrester total economic impact report, as well as discuss real life customer use cases where a demonstrably faster time to insights helped achieve better decision making, supporting improved business goals.
This presentation contains a broad introduction to big data and its technologies.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.
Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.
EUDAT Research Data Management | www.eudat.eu | EUDAT
| www.eudat.eu | The presentation gives an introduction to Research Data Management, explaining why it is important to manage and share data.
November 2016
Persistent Identifiers in EUDAT services| www.eudat.eu | EUDAT
| www.eudat.eu | The EUDAT data domain handles registered data. Each digital object should have a persistent identifier. This persistent identifier is used for: Replica identification; Identification of the repository of record (in the case of replication); Querying of additional information; Checksum (time stamped)...
Research Data Services: The EUDAT B2SERVICE SUITE | www.eudat.eu | EUDAT
| www.eudat.eu | EUDAT offers common data services, supporting multiple research communities as well as individuals, through a geographically distributed, resilient network connecting general purpose data centres and community-specific data repositories.
B2STAGE- how to shift large amounts of data| www.eudat.eu | EUDAT
| www.eudat.eu | B2STAGE is a reliable, efficient, light-weight and easy-to-use service to transfer research data sets between EUDAT storage resources and high-performance computing (HPC) workspaces.
2010 EGITF Amsterdam - Gap between GRID and HumanitiesDirk Roorda
How useful/relevant is GRID and High Performance Computing in its current form for the Humanities, especially within the European Infrastructure projects CLARIN, DARIAH and CESSDA? We need virtual use cases!
Big data analytics and machine intelligence v5.0Amr Kamel Deklel
Why big data
What is big data
When big data is big data
Big data information system layers
Hadoop echo system
What is machine learning
Why machine learning with big data
Introduction to Persistent Identifiers| www.eudat.eu | EUDAT
| www.eudat.eu | What are persistent identifiers? Why use persistent identifiers? Different persistent identifier systems; The HANDLE system; EPIC PID system; Policies; Use cases
Ver 2 July 2017
DDI Data Description Statistics Protection Software is a tool which enables easy anonymization processes of DDI standard univariate statistics. It was developed primarily to address the issue of statistical disclosure control for single variable aggregated data, publically distributed to particularly provide information to scientific researchers to promote detailed official statistics microdata use for scientific purposes. It enables quick and easy automatic data protection by performing data protection methods such as bracketing, top- and bottom-coding, variable (information) removal, numeric descriptive statistics protection and low frequency protection following the minimum frequency rule. In contrast to existing microdata and aggregated data anonymization tools, the developed software protects aggregated data directly in the XML code.
The presentation gives an overview of what metadata is and why it is important. It also addresses the benefits that metadata can bring and offers advice and tips on how to produce good quality metadata and, to close, how EUDAT uses metadata in the B2FIND service.
November 2016
FAIR Data in Trustworthy Data Repositories Webinar - 12-13 December 2016| www...EUDAT
| www.eudat.eu | This webinar was co-organised by DANS, EUDAT and OpenAIRE and was held on 12th and 13th December 2016.
Everybody wants to play FAIR, but how do we put the principles into practice?
There is a growing demand for quality criteria for research datasets. In this webinar we will argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgements about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository.
In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders.
The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets.
In this webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.
It describe cloud infrastructure required for big data. It discusses the object storage and virtualization required for big data. Ceph is discussed as example.
Delivering Faster Insights with a Logical Data FabricDenodo
Watch full webinar here: https://bit.ly/38B5yOW
We will learn from our speakers today how a logical data fabric helps organisations realise faster insights. They will touch on the recent Forrester total economic impact report, as well as discuss real life customer use cases where a demonstrably faster time to insights helped achieve better decision making, supporting improved business goals.
Cloud computing: Legal and ethical issues in library and information servicese-Marefa
Provides an overview of what is cloud computing and its role in library networking and automation. It presents the legal and ethical issues facing library and information specialists when using cloud computing including confidentiality, privacy and licensing.
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)Denodo
Watch full webinar here: https://bit.ly/3dudL6u
It's not if you move to the cloud, but when. Most organisations are well underway with migrating applications and data to the cloud. In fact, most organisations - whether they realise it or not - have a multi-cloud strategy. Single, hybrid, or multi-cloud…the potential benefits are huge - flexibility, agility, cost savings, scaling on-demand, etc. However, the challenges can be just as large and daunting. A poorly managed migration to the cloud can leave users frustrated at their inability to get to the data that they need and IT scrambling to cobble together a solution.
In this session, we will look at the challenges facing data management teams as they migrate to cloud and multi-cloud architectures. We will show how the Denodo Platform can:
- Reduce the risk and minimise the disruption of migrating to the cloud.
- Make it easier and quicker for users to find the data that they need - wherever it is located.
- Provide a uniform security layer that spans hybrid and multi-cloud environments.
A Logical Architecture is Always a Flexible Architecture (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3joZa0a
The current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL, and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML, and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time-consuming. A logical data warehouse is a modern data architecture that allows organizations to leverage all of their data irrespective of where the data is stored, what format it is stored in, and what technologies or protocols are used to store and access the data.
Watch this session to understand:
- What is a logical data warehouse and how to architect one
- The benefits of logical data warehouse – speed with agility
- Customer use case depicting logical architecture implementation
MasterClass Series: Unlocking Data Sharing Velocity with Data VirtualizationDenodo
Watch full webinar here: https://buff.ly/49FKgdM
Join us for an exciting webinar that delves into the world of data sharing and its pivotal role in accelerating data-driven decisions. In an era where every second counts, we’ll showcase how data virtualization acts as the indispensable bridge between disparate data sources and swift consumption.
During this webinar, you’ll see:
- The Great Data Race: Imagine a scenario where time is of the essence, and two data experts compete head-to-head to connect with as many data sources as possible. Witness the electrifying race as they navigate through a multitude of data repositories, showcasing their prowess in sourcing valuable information.
- Data Fusion in Real-Time: Once the data sources are harnessed, our experts will demonstrate how to seamlessly blend these disparate datasets into a coherent and insightful data product. Witness firsthand the lightning-fast transformation of raw data into a valuable asset that can drive informed decisions.
- Unleashing Data Across Ecosystems: In today's interconnected world, the real power of data lies in its versatility. Our experts will illustrate how a well-structured data product can be quickly integrated into numerous consuming applications. Discover the sheer speed at which data can be disseminated across your organization's ecosystem.
- Data Virtualization: The Essential Enabler We will emphasize the crucial role of data virtualization in making this entire process possible. Data virtualization acts as the linchpin, seamlessly connecting various data sources, transforming them into a cohesive unit, and facilitating rapid distribution to consuming applications. Learn how it empowers organizations to harness data at the speed of thought.
Don’t miss this unique webinar as we break down the barriers to data sharing and empower you to unlock the true potential of your data. Register now and embark on a journey towards data-driven excellence.
The Industrial Internet is an emerging communication infrastructure that connects people, data, and machines to enable access and control of mechanical devices in unprecedented ways. It connects machines embedded with sensors and sophisticated software to other machines (and end users) to extract data, make sense of it, and find meaning where it did not exist before. Machines--from jet engines to gas turbines to medical scanners--connected via the Industrial Internet have the analytical intelligence to self-diagnose and self-correct, so they can deliver the right information to the right people at the right time (and in real-time).
Despite the promise of the Industrial Internet, however, supporting the end-to-end quality-of-service (QoS) requirements is hard. This talk will discuss a number of technical issues emerging in this context, including:
Precise auto-scaling of resources with a system-wide focus.
Flexible optimization algorithms to balance real-time constraints with cost and other goals.
Improved fault-tolerance fail-over to support real-time requirements.
Data provisioning and load balancing algorithms that rely on physical properties of computations.
It will also explore how the OMG Data Distribution Service (DDS) provides key building blocks needed to create a dependable and elastic software infrastructure for the Industrial Internet.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Globus Compute wth IRI Workflows - GlobusWorld 2024
Information Systems
1. Information Systems
Introduction to concepts, requirements, approaches, and best-practices
for designing Information systems in hybrid data infrastructure
Pasquale Pagano
2. PasqualePagano
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2
• Education
• Master Degree in Computer Science
• Ph.D in Information Engineering on Distributed Systems
• Organization
• CNR – ISTI, InfraScience Group
• Experience
• D4Science Hybrid Data Infrastructure, Technical Director
• gCube Open-Source Framework, Technical Director
• BlueBRIDGE EU Project, Technical Director
• SoBigData EU Project, Infrastructure Manager
• Parthenos EU Project, Infrastructure Operation Manager
• Bio and contact
• it.linkedin.com/in/pasqualepagano/
• pasquale.pagano@isti.cnr.it
3. Outline
Information System
• What it is and how to define it
Context
• Hybrid cloud-based infrastructure
Resource Registry
• Hybrid cloud-based infrastructure information system
Conclusions
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4. Information Systems
An information system (IS) is
• any organized system for the collection, organization, storage and
communication of information
• an integrated set of components for collecting, storing, and
processing data and for providing information, knowledge, and
digital products [Encyclopaedia Britannica]
Information consists of data that is
1. accurate and timely,
2. specific and organized for a purpose,
3. presented within a context that gives it meaning and relevance,
4. can increase understanding and decrease uncertainty
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4Introduction
5. Information Systems
An information system (IS) is
• a combination of hardware, software, infrastructure and trained
personnel organized to facilitate planning, control, coordination, and
decision making in an organization [businessdictionary]
Trained personnel consists of human resources and :
1. procedures for using, operating, and maintaining the information
system
2. set of basic principles and associated guidelines, a.k.a policies,
formulated and enforced to direct and limit actions in pursuit of long-
term goals
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5Introduction
6. Information Systems
An information system (IS) is
• a software system to capture, transmit, store, retrieve, and
manipulate data produced by software systems to provide access to
information, thereby supporting people, organizations, or other
software systems [MIT Press]
Software systems become producer and consumer of the
Information System making it at the core of their business activities
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6Introduction
7. Information Systems Definition
A software system
• to capture, transmit, store, retrieve, and manipulate data
produced by software systems
• to provide access to information, organized for a purpose
and within a contextual domain
• used, accessed, and maintained according to well-known procedures
operated under the limit of the (evolving) organization policies
• to support people within an organization and other
software systems
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7Introduction
9. e-Infrastructures
e-Infrastructures enable researchers in different locations across
the world to collaborate in the context of their home institutions or
in national or multinational scientific initiatives.
They can work together by having shared access to unique or
distributed scientific facilities (including data, instruments,
computing and communications)
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9Context
11. e-Infrastructures
Data e-Infrastructure: an e-Infrastructure promoting data
sharing and consumption. Addresses the needs of the
research activity performed by a certain community.
InformationSystems12/12/16
11Context
12. e-Infrastructures
Computational e-Infrastructure: an e-Infrastructures
offering computational resources distributed in a network
environment. Uses Cloud computing to execute calculations
with a large number of connected computers. Offers
collaboration facilities for scientists to share experimental
results
InformationSystems12/12/16
12Context
13. Requirements for e-Infrastructures
• Support collaborative research and experimentation
• Implement Reproducibility-Repeatability-Reusability
• Allow sharing of data, methods, workflows, and findings
• Grant open access to produced scientific knowledge and data
• Tackle simplified access to existing computing and storage resources
• Ensure low operational and maintenance costs
• Manage heterogeneous data and service access policies
13
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Context
14. Virtual Research Environment
12/12/16InformationSystems
14
An operational environment
• Where set of resources (data,
services, computational, and
storage resources)
• are assigned to group of users
via interfaces
• for a limited timeframe
L. Candela, D. Castelli, P. Pagano (2013) Virtual Research Environments: An Overview and a Research Agenda. Data Science Journal, Vol. 12
Created on demand
Regulated by tailored policies
No cost for the resource
providers
Open to host and operate
custom software
Context
15. D4Science
European e-Infrastructure
D4Science is both a Data and a Computational e-
Infrastructure that federates other e-Infrastructures across
administration domains - Hybrid Data Infrastructure
Moreover, it
• Implements the notion of e-
Infrastructure/platform/software as-a-Service
• it offers on demand access to data management services and
computational facilities;
• is policies-driven through the true implementation of Virtual
Research Environments
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15Context
16. Infrastructure as a Service
Infrastructure as a service (IaaS) is a standardized, highly
automated offering, where compute resources, complemented by
storage and networking capabilities are owned and hosted by a
service provider and offered to customers on-demand.
• IaaS also hosts users' applications and handles tasks including
system maintenance, backup, and recovery planning.
• Customers are able to self-provision this infrastructure, using a
Web-based graphical user interface that serves as an IT
operations management console for the overall environment.
• API access to the infrastructure may also be offered as an option.
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16Context
17. Cloud Computing
• IaaS is one of three main categories of cloud computing services,
complemented by
• Software as a Service (SaaS)
• software distribution model in which applications are made available
to customers over the Internet.
• removes the need to install and run applications on owned data
center.
• eliminates the expense of hardware acquisition, provisioning and
maintenance, as well as software licensing, installation and support.
• Platform as a Service (PaaS)
• cloud computing model that delivers application development
frameworks to its users as a service.
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17Context
18. Cloud Computing Characteristics
• On-demand
• Provision of computing resources, such as server, service, and
storage, as needed without requiring human interaction
• Broad network access
• Resources are available over a network
• Resource pooling
• Resources pooled to serve multiple users using a multi-tenant model,
with physical and virtual resources dynamically assigned and
reassigned according to consumer demand
• Rapid elasticity
• Resources elastically provisioned and released, automatically, to
horizontally scale rapidly outward and inward as needed
• Measured service
• Resources usage is monitored, controlled, and reported
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18Context
19. D4Science is an hybrid cloud-based infrastructure
technologies integrated to provide
elastic access and usage of data and data-management capabilities
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Humanities and Cultural Heritage
Social Mining
Environmental Studies
Biological and Ecological Studies
Context 19
20. D4Science Service Provision
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20
Empowered Hardware
Package
Repository HW
gHN
Failure Recovery
HW
gHN
HW
gHN
Service provision continuity
HW
gHN
HW
gHN
Balancing utilization with head room
Dynamic Load Balancing
WS
State
WS
State
CPU Usage
30%
CPU Usage
90%
Rapid deployment
Production
HW
gHNPackage
Repository
WS
Dynamic deployment
…
WS
State
WS
State WS
State
WS
State
…
WSWS
Context
21. 12/12/16InformationSystems
D4Science is an hybrid cloud-based infrastructure
• 63 VREs hosted
• +3100 users
• in 44 countries
• from +80 Institutions
• + 430 millions service calls a year
• + 1600 distinct caller hosts
• +25,000 derivative data/month
• +50 data providers
• over a billion quality records
• +20,000 temporal datasets
• +50,000 spatial datasets
• 99.8% service availability
Context 21
22. Hybrid cloud-based infrastructure
challenges
Hundred software systems opportunistically deployed on demand
• The software systems to manage are not known at design time
• The location of any service is known only at runtime
• Any software system has to discover the location of the targeted service
before to use it
• All software systems have to be monitored, controlled, and reported
• Status, load, exploitation usage, and accounting data have to be
constantly updated to enable elasticity and pooling of resources
All these data are managed by the infrastructure Resource Registry
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22Resource Registry
24. Resource Registry
The infrastructure Resource Registry is an Information System designed to
support the operation of an hybrid cloud-based infrastructure
• To capture, transmit, store, retrieve and manipulate data from any
software system enabled on the infrastructure
• Location and properties
• Status, load, exploitation usage, and accounting data
• To provide access to information, organized to enable
• Monitoring, validation, and reporting
• Elasticity and pooling of resources
• To support any software system to
• Discover services and infrastructure
resources
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24Resource Registry
25. Resource Registry
abstract system view
The Resource Registry - core of a SOA within the complexities of an hybrid
cloud-based infrastructure – must enable
• a set of resource management functions
• enabling functions
• publication, discovery
• monitoring, deployment
• contextualization, security, execution
• data management functions
• access, store
• index, search
• transfer, transform
• plus a set of applications
• built against those functions
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25Resource Registry
26. Resource Registry
abstract system view
• Resource types: abstract view over functions
• defined by specifications
• multiple implementations, over time / concurrently
• different implementations, different information
• system cannot globally define them
• implementations produce/consume different facets, independently
• resource semantics dynamic
• no longer predefined in class hierarchies
• implicitly captured by current facets
• changes over time / across “similar” resources
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26Resource Registry
28. Resource Registry
resource model
• defines a framework for collecting facets
• some common properties
• a loose binding to XML/Json
• all resources have:
• A unique identifier
• optional name and description
• one or more policies
• zero or more facets
• uniquely identified
• arbitrary otherwise
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28Resource Registry
32. Resource Model
milestones
• Open-ended model for describing resources
• Open-ended set of manageable resources
• Ability to evolve with the evolving needs of the infrastructure at
no cost for its clients
• by supporting new types of resources at run-time
• by supporting evolution in the way a resource is described
• by supporting the same resource type described by using different
models
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32Resource Registry
34. Conclusions
• Any information system has to be designed for a purpose and
within a contextual domain
• A Resource Registry is an Information System designed to
support the operation of an infrastructure
• Open-ended model since infrastructure resources may not known in
advance
• Open-ended set of manageable resources since an infrastructure
lifetime may span several decades
• Non-functional requirements - e.g. availability, reliability – are key
requirements to consider in the design phase
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35. Further Reading
• Candela, Leonardo, Donatella Castelli, and Pasquale Pagano. "Virtual research environments: an overview and a research agenda." Data
Science Journal 12.0 (2013): 65-91
• Papazoglou, Mike P., and Willem-Jan Van Den Heuvel. "Service oriented architectures: approaches, technologies and research issues."
The VLDB journal 16.3 (2007): 389-415.
• Papazoglou, Mike P. "Service-oriented computing: Concepts, characteristics and directions." Web Information Systems Engineering, 2003.
WISE 2003. Proceedings of the Fourth International Conference on. IEEE, 2003.
• Sivashanmugam, Kaarthik, Kunal Verma, and Amit Sheth. "Discovery of web services in a federated registry environment." Web Services,
2004. Proceedings. IEEE International Conference on. IEEE, 2004
• Khouja, Mehdi, and Carlos Juiz. "Enhanced service discovery via shared context in a distributed architecture." Web Services (ICWS), 2015
IEEE International Conference on. IEEE, 2015.
• Zhu, Fen, Matt W. Mutka, and Lionel M. Ni. "Service discovery in pervasive computing environments." IEEE Pervasive computing 4.4
(2005): 81-90.
• Chakraborty, Dipanjan, et al. "Toward distributed service discovery in pervasive computing environments." IEEE Transactions on Mobile
computing5.2 (2006): 97-112.
• Zhang, Liang-Jie, and Qun Zhou. "CCOA: Cloud computing open architecture." Web Services, 2009. ICWS 2009. IEEE International
Conference on. Ieee, 2009.
• Zhang, Qi, Lu Cheng, and Raouf Boutaba. "Cloud computing: state-of-the-art and research challenges." Journal of internet services and
applications 1.1 (2010): 7-18.
• Wei, Yi, and M. Brian Blake. "Service-oriented computing and cloud computing: challenges and opportunities." IEEE Internet Computing
14.6 (2010): 72.
• Garofalakis, John, et al. "Web service discovery mechanisms: Looking for a needle in a haystack." International Workshop on Web
Engineering. Vol. 38. 2004.
• Sotomayor, Borja, et al. "Virtual infrastructure management in private and hybrid clouds." IEEE Internet computing 13.5 (2009): 14-22.
• Rodero-Merino, Luis, et al. "From infrastructure delivery to service management in clouds." Future Generation Computer Systems 26.8
(2010): 1226-1240.
• Zhang, Xuechai, Jeffrey L. Freschl, and Jennifer M. Schopf. "A performance study of monitoring and information services for distributed
systems." High Performance Distributed Computing, 2003. Proceedings. 12th IEEE International Symposium on. IEEE, 2003.
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36. THANK YOU
Acknowledgement:
Fabio Simeoni, Luca Frosini, Manuele Simi
CNR – ISTI InfraScience
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The content of this presentation is released under the
Creative-Commons CC-BY-SA license