The document discusses a call for abstracts for the Cloud Futures 2010 conference at Microsoft Research. The call invites submissions that illustrate how cloud computing can advance research across various fields like computer science, earth sciences, healthcare, and social sciences. It highlights how cloud computing provides vast computing resources and the potential to create new services and methods for research.
Presentation by Prof. Werner Paulus, Université de Rennes. Presentation was held at the EMAP training seminar in Larnaca, for future Erasmus Mundus Joint Masters consortia (18. – 21. February 2010).
Larnaca, 18. – 21. February 2010
Open Linked Governmental Data for Citizen Engagement – A workshop about the benefits and restrictions of open linked governmental data and the role of metadata in citizen engagement (Anneke Zuiderwijk, Marijn Janssen, Keith Jeffery, Yannis Charalabidis) #cedem12
Project Briefing: Spring 2008 Task Force Meeting: Moving to MobileMichelle Jacobs
Cell phones and other mobile devices are ubiquitous and offer increasingly robust operating systems, user interfaces, and hardware sophistication. The potential of these devices for accessing the richness of library and information content, services, and applications provided is largely unrealized. This is particularly unfortunate in considering library outreach to undergraduate students. This session will review projects in development in the Undergraduate Library at the University of Illinois at Urbana-Champaign and the College Library at the University of California at Los Angeles to explore possibilities for communication, content delivery, and instruction through mobile devices and the principles guiding these projects. Significant time in the session will be devoted to discussion of the opportunities, possibilities, challenges, and issues as libraries work to address this important issue - the move to mobile.
Presentation by Prof. Werner Paulus, Université de Rennes. Presentation was held at the EMAP training seminar in Larnaca, for future Erasmus Mundus Joint Masters consortia (18. – 21. February 2010).
Larnaca, 18. – 21. February 2010
Open Linked Governmental Data for Citizen Engagement – A workshop about the benefits and restrictions of open linked governmental data and the role of metadata in citizen engagement (Anneke Zuiderwijk, Marijn Janssen, Keith Jeffery, Yannis Charalabidis) #cedem12
Project Briefing: Spring 2008 Task Force Meeting: Moving to MobileMichelle Jacobs
Cell phones and other mobile devices are ubiquitous and offer increasingly robust operating systems, user interfaces, and hardware sophistication. The potential of these devices for accessing the richness of library and information content, services, and applications provided is largely unrealized. This is particularly unfortunate in considering library outreach to undergraduate students. This session will review projects in development in the Undergraduate Library at the University of Illinois at Urbana-Champaign and the College Library at the University of California at Los Angeles to explore possibilities for communication, content delivery, and instruction through mobile devices and the principles guiding these projects. Significant time in the session will be devoted to discussion of the opportunities, possibilities, challenges, and issues as libraries work to address this important issue - the move to mobile.
CARL Annual Pre-Conference Engage Session: The Reference Toolkit - RevisitedMichelle Jacobs
This is the Keynote presentation from the CARL 2010 CARL Annual Pre-Conference Engage Session: The Reference Toolkit - Revisited. Handouts included; a Reference Tools kit for brainstorming, a "LifeCycle" document to move an Emerging Technology forward.
“Reference Desk Toolkit”
A Moderated Discussion Hosted by CARLDIG-South and the Mt. San Antonio
College Library
Friday, April 17, 2009
9:00-9:30: Registration and continental breakfast
9:30-12:30: Presentations and moderated discussion
Founders Hall, Mt. San Antonio College
Hunches, Instincts, and Trusting Your Gut (Warm Gun 2014)Leah Buley
The challenge for all designers is how to critique the work, your own or someone else’s. Can you translate your gut feelings into a clear, credible point of view and communicate it to others?
Practice building for the ux team of one uxlxLeah Buley
These slides were presented in a workshop at UX London 2011. The workshop covered the methods, soft skills, and strategies to help UX teams of one build their own careers and do their best work in a resource constrained environments. Includes an overview of different "types" of teams of one, challenges, and some useful methods.
In this presentation, I share my own experiences learning about generative design and how to do it. This presentation includes lots of practical tips and tricks.
The Right Research Method For Any Problem (And Budget)Leah Buley
The mighty user research toolkit is packed with techniques. It can do everything from blue sky innovation research, to need-finding and requirements gathering, to product validation and testing. But many teams don't exploit the full toolkit, sticking instead to one side or the other of the quant versus qual divide, or returning again and again to that tired old workhorse—usability testing. This presentation is a primer on the range of research methods available, and a guide for determining which is the best technique for what you’re trying to learn now (and for your budget).
How to Be a UX Team of One was presented by Leah Buley at the 2008 IA Summit in Miami, Florida.
It shows techniques for generative design that can be used by solo user experience practitioners.
Agile methods for better and faster UX solutions. This 3-hour workshop was presented at Agile 2008 in Toronto. Co-developed with Dan Harrelson (http://www.slideshare.net/dharrels).
Policies aimed at bringing universities closer together have always been (and still are) sensitive political issues.
Ascertaining the position and weight of UTC in a COMUE* alongside two major French Universities (Paris 4
(Sorbonne) and University of Paris 6 (Pierre & Marie Curie, or UPMC) has been no simple matter. Among the issues
is the place for technology in a world of traditional ‘pure’ science. Another is the pedagogical contribution of the
arts and humanities that have been an integral factor for UTC, in both teaching and research since the beginning.
CARL Annual Pre-Conference Engage Session: The Reference Toolkit - RevisitedMichelle Jacobs
This is the Keynote presentation from the CARL 2010 CARL Annual Pre-Conference Engage Session: The Reference Toolkit - Revisited. Handouts included; a Reference Tools kit for brainstorming, a "LifeCycle" document to move an Emerging Technology forward.
“Reference Desk Toolkit”
A Moderated Discussion Hosted by CARLDIG-South and the Mt. San Antonio
College Library
Friday, April 17, 2009
9:00-9:30: Registration and continental breakfast
9:30-12:30: Presentations and moderated discussion
Founders Hall, Mt. San Antonio College
Hunches, Instincts, and Trusting Your Gut (Warm Gun 2014)Leah Buley
The challenge for all designers is how to critique the work, your own or someone else’s. Can you translate your gut feelings into a clear, credible point of view and communicate it to others?
Practice building for the ux team of one uxlxLeah Buley
These slides were presented in a workshop at UX London 2011. The workshop covered the methods, soft skills, and strategies to help UX teams of one build their own careers and do their best work in a resource constrained environments. Includes an overview of different "types" of teams of one, challenges, and some useful methods.
In this presentation, I share my own experiences learning about generative design and how to do it. This presentation includes lots of practical tips and tricks.
The Right Research Method For Any Problem (And Budget)Leah Buley
The mighty user research toolkit is packed with techniques. It can do everything from blue sky innovation research, to need-finding and requirements gathering, to product validation and testing. But many teams don't exploit the full toolkit, sticking instead to one side or the other of the quant versus qual divide, or returning again and again to that tired old workhorse—usability testing. This presentation is a primer on the range of research methods available, and a guide for determining which is the best technique for what you’re trying to learn now (and for your budget).
How to Be a UX Team of One was presented by Leah Buley at the 2008 IA Summit in Miami, Florida.
It shows techniques for generative design that can be used by solo user experience practitioners.
Agile methods for better and faster UX solutions. This 3-hour workshop was presented at Agile 2008 in Toronto. Co-developed with Dan Harrelson (http://www.slideshare.net/dharrels).
Policies aimed at bringing universities closer together have always been (and still are) sensitive political issues.
Ascertaining the position and weight of UTC in a COMUE* alongside two major French Universities (Paris 4
(Sorbonne) and University of Paris 6 (Pierre & Marie Curie, or UPMC) has been no simple matter. Among the issues
is the place for technology in a world of traditional ‘pure’ science. Another is the pedagogical contribution of the
arts and humanities that have been an integral factor for UTC, in both teaching and research since the beginning.
Educational Innovation & Technology at MIT at Moodle Share FairBrandon Muramatsu
Description of some of the projects that innovative educational projects at MIT with a focus on K-12 outreach. Projects presented include: OpenCourseWare (OCW Finder and OER Recommender), Highlights for High School, Visualizing Cultures, and Software Tools for Academics & Researchers. Presented by Brandon Muramatsu and Jeff Merriman at the Moodle Share Fair in Millis, MA, May 28, 2009.
Bridging Sensor Data Streams and Human KnowledgeMattia Zeni
Generating useful and meaningful knowledge out of personal big data is a difficult task that presents multiple challenges due to the intrinsic characteristics of these type of data, namely their volume, velocity, variety and noisiness. This work proposes an interdisciplinary approach for solving this problem that is based on the idea that the user and the world surrounding him can be modeled, defining most of the elements of her context as entities (locations, people, objects) in addition with their attributes and the relations among them. This allows to create a structure out of the unstructured, noisy and highly variable sensor data that can then be used by the machine to provide personalized, context-aware services to the final user with the final goal of improving her quality of life.
Automating Big Data Management, DISIT LabPaolo Nesi
DISIT lab (http://www.disit.org ) is an expert on data management with a long track record in the field producing, new knowledge, inovation and tools presently used in several context and infrastructure as: Florence Smart City, ECLAP.eu, Apretoscana. etc.
Main skill of disit lab is on data management lifecycle, models and tool: Automating: aggregation, integration, reconciliation, licensing, improvement, enrichment, etc.
Efficient data Ingestion: from non structured data to RDF models
Exploiting: Open and Private data, licensing support, towards noSQL Graph Databases stores.
Quality and process metrics, benchmarks
Knowledge engineering and graph databases:
Ontology modeling on domain analysis
Some DISIT ontologies: smart city, smart cloud, railways nets, cultural heritage, etc.
Semantic reasoners: models and algorithms
Benchmarking
Data Mining, Data Analytics via AI and statistics
Predictive models, critical events detection
NLP algorithms and tools, NLP hadoop
Semantic computing, Link Discovering on LD, LOD in the world.
DISIT Managed Infrastructures:
Smart City Florence, Italy: http://www.disit.org/km4city
Twitter Vigilance: http://www.disit.org/tv
Two social networks: http://www.ECLAP.eu, http://www.apretoscana.org
ICT 14, 15, 17, 18: DISIT
Has huge experience and consolidated research line in:
automating data integration and aggregation processes: smart city, cloud, cultural heritage, etc.
Data mining, NLP, predictive models
Graph Data base modeling and benchmarking
Is managing growing big data in multiple domains for
Smart City + Social Media: mobility, e-health, urban services, user behavior, environment, weather, culture,..
Has strong connections with SME in Italy as APRETOSCANA agency
DISIT Log
Dr. Frederic Andres (NII, Japan) „Collective Intelligence-based Social Projec...Saulius Maskeliunas
(Presentation at the VU IMI and LIKS AI section seminar, 8th November 2013)
Abstract:
Social Project Management is a novel enhancement approach to project management based on social network. Social Project Management is defined as the effort of designing and executing research project, problem-solving tasks collaboratively by levering social networking. It aims at:
– Exploiting weak ties between researchers and implicit research know-how to improve activity execution and improving of knowledge sharing and collective intelligence.
– Increasing transparency and participation to the decision procedures, so as to raise awareness of the research processes and acceptance of the outcomes.
– Involving (informal) communities in research execution, thus assigning the execution to a broader set of performers or to find most appropriate contributor within a group.
We will present how social project management combines social networking, collective intelligence, and problem solving to increase the effectiveness of best practices. Current work on Collective Intelligence will be presented for the applicability of universal knowledge sharing inside social project management.
Normalmente parliamo e presentiamo Azure IoT (Central) con un taglio un po' da "maker". In questa sessione, invece, vediamo di parlare allo SCADA engineer. Come si configura Azure IoT Central per il mondo industriale? Dov'è OPC/UA? Cosa c'entra IoT Plug & Play in tutto questo? E Azure IoT Central...quali vantaggi ci da? Cerchiamo di rispondere a queste e ad altre domande in questa sessione...
Allo sviluppatore Azure piacciono i servizi PaaS perchè sono "pronti all'uso". Ma quando proponiamo le nostre soluzioni alle aziende, ci scontriamo con l'IT che apprezza gli elementi infrastrutturali, IaaS. Perchè non (ri)scoprirli aggiungendo anche un pizzico di Hybrid che con il recente Azure Kubernetes Services Edge Essentials si può anche usare in un hardware che si può tenere anche in casa? Quindi scopriremo in questa sessione, tra gli altri, le VNET, le VPN S2S, Azure Arc, i Private Endpoints, e AKS EE.
Static abstract members nelle interfacce di C# 11 e dintorni di .NET 7.pptxMarco Parenzan
Did interfaces in C# need evolution? Maybe yes. Are they violating some fundamental principles? We see. Are we asking for some hoops? Let's see all this by telling a story (of code, of course)
Azure Synapse Analytics for your IoT SolutionsMarco Parenzan
Let's find out in this session how Azure Synapse Analytics, with its SQL Serverless Pool, ADX, Data Factory, Notebooks, Spark can be useful for managing data analysis in an IoT solution.
Power BI Streaming Data Flow e Azure IoT Central Marco Parenzan
Dal 2015 gli utilizzatori di Power BI hanno potuto analizzare dati in real-time grazie all'integrazione con altri prodotti e servizi Microsoft. Con streaming dataflow, si porterà l'analisi in tempo reale completamente all'interno di Power BI, rimuovendo la maggior parte delle restrizioni che avevamo, integrando al contempo funzionalità di analisi chiave come la preparazione dei dati in streaming e nessuna creazione di codice. Per vederlo in funzione, studieremo un caso specifico di streaming come l'IoT con Azure IoT Central.
Power BI Streaming Data Flow e Azure IoT CentralMarco Parenzan
Dal 2015 gli utilizzatori di Power BI hanno potuto analizzare dati in real-time grazie all'integrazione con altri prodotti e servizi Microsoft. Con streaming dataflow, si porterà l'analisi in tempo reale completamente all'interno di Power BI, rimuovendo la maggior parte delle restrizioni che avevamo, integrando al contempo funzionalità di analisi chiave come la preparazione dei dati in streaming e nessuna creazione di codice. Per vederlo in funzione, studieremo un caso specifico di streaming come l'IoT con Azure IoT Central.
Power BI Streaming Data Flow e Azure IoT CentralMarco Parenzan
Since 2015, Power BI users have been able to analyze data in real-time thanks to the integration with other Microsoft products and services. With streaming dataflow, you'll bring real-time analytics completely within Power BI, removing most of the restrictions we had, while integrating key analytics features like streaming data preparation and no coding. To see it in action, we will study a specific case of streaming such as IoT with Azure IoT Central.
What are the actors? What are they used for? And how can we develop them? And how are they published and used on Azure? Let's see how it's done in this session
Generic Math, funzionalità ora schedulata per .NET 7, e Azure IoT PnP mi hanno risvegliato un argomento che nel mio passato mi hanno portato a fare due/tre viaggi, grazie all'Università di Trieste, a Cambridge (2006/2007 circa) e a Seattle (2010, quando ho parlato pubblicamente per la prima volta di Azure :) e che mi ha fatto conoscere il mito Don Box!), a parlare di codice in .NET che aveva a che fare con la matematica e con la fisica: le unità di misura e le matrici. L'avvento dei Notebook nel mondo .NET e un vecchio sogno legato alla libreria ANTLR (e tutti i miei esercizi di Code Generation) mi portano a mettere in ordine 'sto minestrone di idee...o almeno ci provo (non so se sta tutto in piedi).
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.NET is better every year for a developer who still dreams of developing a video game. Without pretensions and without talking about Unity or any other framework, just "barebones" .NET code, we will try to write a game (or parts of it) in the 80's style (because I was a kid in those years). In Christmas style.
Building IoT infrastructure on edge with .net, Raspberry PI and ESP32 to conn...Marco Parenzan
IoT scenarios necessarily pass through the Edge component and the Raspberry PI is a great way to explore this world. If we need to receive IoT events from sensors, how do I implement an MQTT endpoint? Kafka is a clever way to do this. And how do I process the data? Kafka? Spark? Rabbit ?. How do we write custom code for these environments? .NET, now in version 6 is another clever way to do it! And maybe, we can also communicate with Azure. We'll see in this session if we can make it all work!
How can you handle defects? If you are in a factory, production can produce objects with defects. Or values from sensors can tell you over time that some values are not "normal". What can you do as a developer (not a Data Scientist) with .NET o Azure to detect these anomalies? Let's see how in this session.
Quali vantaggi ci da Azure? Dal punto di vista dello sviluppo software, uno di questi è certamente la varietà dei servizi di gestione dei dati. Questo ci permette di cominciare a non essere SQL centrici ma utilizzare il servizio giusto per il problema giusto fino ad applicare una strategia di Polyglot Persistence (e vedremo cosa significa) nel rispetto di una corretta gestione delle risorse IT e delle pratiche di DevOps.
C'è ancora diffidenza nei confronti dell'Internet of Things e il costo delle soluzioni custom non aiuta. Azure IoT Central è un servizio SaaS personalizzabile che rende accessibile a costi sostenibili. Vediamo quali sonole peculiarità di questo servizio.
Come puoi gestire i difetti? Se sei in una fabbrica, la produzione può produrre oggetti con difetti. Oppure i valori dei sensori possono dirti nel tempo che alcuni valori non sono "normali". Cosa puoi fare come sviluppatore (non come Data Scientist) con .NET o Azure per rilevare queste anomalie? Vediamo come in questa sessione.
It happens that we have to develop several services and deploy them in Azure. They are small, repetitive but different, often not very different. Why not use code generation techniques to simplify the development and implementation of these services? Let's see with .NET comes to meet us and helps us to deploy in Azure.
Running Kafka and Spark on Raspberry PI with Azure and some .net magicMarco Parenzan
IoT scenarios necessarily pass through the Edge component and the Raspberry PI is a great way to explore this world. If we need to receive IoT events from sensors, how do I implement an MQTT endpoint? Kafka is a clever way to do this. And how do I process the data in Kafka? Spark is another clever way of doing this. How do we write custom code for these environments? .NET, now in version 6 is another clever way to do it! And maybe, we also communicate with Azure. We'll see in this session if we can make it all work!
Time Series Anomaly Detection with Azure and .NETTMarco Parenzan
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
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.
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
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.
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
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.
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
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
2010.04.30 summary of cloud futures 2010 marco parenzan pov
1. Cloud Futures 2010
http://research.microsoft.com/en-
us/events/cloudfutures2010/
Marco Parenzan
MOSE
MOSE – University of Trieste 30 April, 2010 - slide 1
2. Marco Parenzan
36Più di 30 anni; sposato e due figli; abito a Fiume Veneto
(PN)
Un ―prodotto‖ di questa Università
Passato di sviluppo software in aziende...
...di software (visione esterna)...
...di produzione (visione interna)....
―Insourcing― (il contrario dell‘Outsourcing)
Sono un libero professionista
Sviluppo applicazioni su commessa
Consulenza
Collaboro con il Laboratorio MOSE dell‘Università degli Studi
di Trieste
Mi occupo di metodologie e strumenti di sviluppo
Mi occupo di progetti software 2
MOSE – University of Trieste 30 April, 2010 - slide 2
3. Attività di formazione
9 anni come Docente a Contratto in questa Università
4 alla Triennale di Ing.Inf (Programmazione dei Calcolatori – ex. Prof.
Cesari)
5 alla Magistrale di Ing.Inf (Programmazione dei Web Services)
Formazione in azienda
Formazione in enti regionali
IAL/Centro Formazione Pordenone (Villaggio del Fanciullo)
2 IFTS (Istruzione e formazione tecnica superiore, 1200 ore – Tecnico
Software): intero curriculum Microsoft
Speaker per user groups
xe.net (http://www.xe.net/)
UGI.ALT.net (http://www.ugialt.net/)
1nn0va (http://www.1nn0va.net/)
Eventi presso il Consorzio Universitario di Pordenone
3
MOSE – University of Trieste 30 April, 2010 - slide 3
4. 1nn0va
L'associazione non ha scopo di lucro, è apartitica, apolitica e
ha finalità esclusivamente scientifica.
Si prefigge la diffusione delle tecnologie emergenti e attuali
attraverso l'organizzazione di conferenze, la redazione a la
diffusione di pubblicazioni, l'applicazione pratica in progetti
non a fini di lucro di innovative tecniche e metodologie di
sviluppo informatico, il coordinamento con altre Associazioni,
gruppi o Enti.
Divulgazione sul territorio (pordenonese)
MOSE – University of Trieste 30 April, 2010 - slide 4
5. Primo 1nn0valab: 28 maggio 2010
ECCEZIONALE EVENTO WPF DI 1NN0VA: NASCE 1nn0vaLAB.
Niente slide o lunghi monologhi, potrete ''toccare con mano'', digitare
direttamente il codice mostrato da Marco su un PC messo
eccezzionalmente a vostra completa disposizione per questo evento.
Dalla versione 3.0 del framework .NET, rilasciata nel novembre del 2006, abbiamo a disposizione una nuova
libreria per sviluppare applicazioni desktop: Windows Presentation Foundation. A quattro anni dal rilascio, e in
concomitanza del rilascio del framework .NET 4.0, è ora di fare il salto. Capiremo:
· quali sono i nuovi presupposti e quindi le differenze dal vecchio modello GDI delle Windows Forms
· il nuovo sistema di layout
· la nuova relazione tra designer e developer con l‘approccio dichiarativo e il linguaggio XAML
· l‘uso di pattern architetturali nello sviluppo di applicazioni desktop con il Model-View-View Model (M-V-
VM)
· perchè è ora di passare a WPF, visto che abbiamo anche Silverlight per lo sviluppo di Rich Internet
Applications (ora alla versione 4) e (supernovità) lo sviluppo di applicazioni per il prossimo Windows Phone 7
L'evento si svolgerà al Consorzio Universitario di Pordenone sito in via
Prasecco 3a, sala L2 (piano interrato), edificio B
MOSE – University of Trieste 30 April, 2010 - slide 5
7. Call for Abstracts
“Advancing Research with Cloud Computing”
Cloud computing is fast becoming the most important platform for
research. Researchers today need vast computing resources to collect, share,
manipulate, and explore massive data sets as well as to build and deploy new
services for research. Cloud computing has the potential to advance research
discoveries by making data and computing resources readily available
at unprecedented economy of scale and nearly infinite scalability. To realize the full
promise of cloud computing for research, however, one must think about the cloud
as a holistic platform for creating new services, new experiences, and new methods
to pursue research, teaching and scholarly communication. This goal presents a
broad range of interesting questions.
We invited extended abstracts that illustrate the role of cloud computing across a
variety of research and curriculum development areas—including computer science,
earth sciences, healthcare, humanities, life sciences, and social sciences—that
highlight how new techniques and methods of research in the cloud may solve
distinct challenges arising in those diverse areas.
Source: http://research.microsoft.com/en-us/events/cloudfutures2010/
MOSE – University of Trieste 30 April, 2010 - slide 7
9. The University of Washington
eScience Institute
Ed Lazowska
Bill & Melinda Gates Chair in
Computer Science & Engineering
University of Washington
Director
University of Washington
eScience Institute
http://lazowska.cs.washington.edu/cloud2010.pdf
MOSE – University of Trieste 30 April, 2010 - slide 9
10. Dan Reed
MOSE – University of Trieste 30 April, 2010 - slide 10
11. Massive volumes of data from
sensors and networks of sensors
MOSE – University of Trieste 30 April, 2010 - slide 11
12. SDSS
Apache Point telescope,
80TB of raw image data
(80,000,000,000,000 bytes)
over a 7 year period
MOSE – University of Trieste 30 April, 2010 - slide 12
13. Large Hadron Collider
700MB of data
per second,
60TB/day, 20PB/year
MOSE – University of Trieste 30 April, 2010 - slide 13
14. Illumina Major labs
HiSeq 2000 have 25-100
Sequencer of these
machines
~1TB/day
MOSE – University of Trieste 30 April, 2010 - slide 14
15. Regional Scale
Nodes of the NSF
Ocean Observatories
Initiative
1000 km of fiber
optic cable on the
seafloor,thousands of
connecting
chemical, physical,
and biological
sensors
MOSE – University of Trieste 30 April, 2010 - slide 15
16. The Web
20+ billion web pages
x 20KB = 400+TB
One computer can
read 30-35 MB/sec
just to read the web
from disk => 4 months
MOSE – University of Trieste 30 April, 2010 - slide 16
17. eScience: Sensor-driven (data-driven)
science and engineering
Jim Gray
Transforming science (again!)
MOSE – University of Trieste 30 April, 2010 - slide 17
19. eScience is about the analysis of data
The automated or semi-automated extraction of
knowledge from massive volumes of data
It’s not just a matter of volume
MOSE – University of Trieste 30 April, 2010 - slide 19
20. Large Synoptic Survey
Telescope (LSST)
40TB/day
(an SDSS every two days),
100+PB in its 10-year
lifetime
400mbps sustained data
rate between
Chile and NCSA
MOSE – University of Trieste 30 April, 2010 - slide 20
21. LSST Data Management System is widely distributed
Headquarters Site Archive Center
Systems Operations Archive Site
Co-located
Center (SOC) Data Access Center (DAC)
Education and Public • Site
Outreach Center (EPOC) • A physical
location/space
that hosts DM
centers
Connected via
•
Base Site
dedicated,
Base Center • Center
protected fiber
• optic circuits
A DM functional
Co-located capability hosted
Data Access Center (DAC) at a Site
NSF Review
December 15-17, 2009 NSF Review
Tucson, AZ
[Andy Connolly, University
December 15-17, 2009 of Washington, and LSST]
Tucson, AZ
MOSE – University of Trieste 30 April, 2010 - slide 21
22. But astronomy is substantially ahead of
most other fields
Data management in computational astrophysics
fopen()
fread()
fwrite()
fclose()
scp – Jeff Gardner, UW eScience Institute
Each simulation generates a sequence of snapshots;
each snapshot is a single flat file; analysis is via C or
Fortran programs
MOSE – University of Trieste 30 April, 2010 - slide 22
23. Data management in biology
90% of all business data is maintained in spreadsheets
– Enrique Godreau, Voyager Capital
MOSE – University of Trieste 30 April, 2010 - slide 23
24. Top faculty across all disciplines understand
and fear the coming data tsunami
Survey of 125 top
investigators
“Data, data, data”
Flat files and Excel are
the most common data
management tools …
Great for Microsoft
lousy for science!
Typical science workflow:
2 years ago: 1/2 day/week
Now: 1 FTE
In 2 years: 10 FTE
Need tools, tools, tools!
MOSE – University of Trieste 30 April, 2010 - slide 24
25. eScience is married to the Cloud: Scalable
computing and storage for everyone
MOSE – University of Trieste 30 April, 2010 - slide 25
26. Economics of Cloud Users
• Pay by use instead of provisioning for peak
Capacity
Resources
Resources
Demand Capacity
Demand
Time Time
Static data center Data center in the cloud
Unused resources
26
MOSE – University of Trieste 30 April, 2010 - slide 26
27. Cloud Computing: Confusion
The interesting thing about cloud computing is that we’ve redefined
Cloud Computing to include everything that we already do… I don’t understand
what we would do differently in the light of Cloud Computing than change
some of the words in our ads.
Larry Ellison (Oracle CEO) , quoted in the Wall Street Journal, Sept 26, 2008
MOSE – University of Trieste 30 April, 2010 - slide 27
28. Cloud Computing: Confusion
A lot of people are jumping on the [cloud] bandwagon, but I have not heard
two people say the same thing about it. There are multiple definitions
out there of “the cloud”
Andy Isherwood (HP VP of European Software Sales), in ZDNews, Dec 11, 2008
MOSE – University of Trieste 30 April, 2010 - slide 28
29. Cloud Computing: Confusion
It’s stupidity. It’s worse than stupidity: it’s a marketing hype campaign.
Somebody is saying this is inevitable – and whenever you hear somebody saying
that, it’s very likely to be a set of businesses campaigning to make it true.
Richard Stallman (“free software” advocate), in The Guardian, Sept 29, 2008
MOSE – University of Trieste 30 April, 2010 - slide 29
30. Dilbert on Cloud Computing
Novembre 18th, 2009
http://www.dilbert.com/strips/comic/2009-11-18/
Novembre 19th, 2009
http://www.dilbert.com/strips/comic/2009-11-19/
MOSE – University of Trieste 30 April, 2010 - slide 30
39. Cloud Computing
A large-scale distributed computing paradigm that is driven
by economies of scale, in which a pool of abstracted,
virtualized, dynamically-scalable, managed computing power,
storage, platforms, and services are delivered on demand to
external customers over the Internet.
[I. Foster, Y. Zhao, I. Raicu, S. Lu,‖Cloud Computing and Grid
Computing 360-Degree Compared‖, in Proc. IEEE Grid
Computing Environments Workshop, Austin (Tx), Nov. 2008,
pp. 1-10.]
MOSE – University of Trieste 30 April, 2010 - slide 39
40. What Is Cloud Computing?
Three New Aspects to Cloud Computing
The Illusion of Infinite Computing Resources Available on Demand
The Elimination of an Upfront Commitment by Cloud Users
The Ability to Pay for Use of Computing Resources
on a Short-Term Basis as Needed
Above the Clouds: a Berkeley View of Cloud Computing
http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdfCloud Computing
Book Report” on the UC Berkeley Paper “Above the Clouds: a Berkeley View
of Cloud Computing”
http://blogs.msdn.com/pathelland/archive/2009/04/10/book-report-on-the-uc-berkeley-paper-
above-the-clouds-a-berkeley-view-of-cloud-computing.aspx
http://cid-84f3c5ef51d06e8b.skydrive.live.com/self.aspx/.Public/2009/Above-the-Clouds-
090401k.pptx
MOSE – University of Trieste 30 April, 2010 - slide 40
41. Evolution to Cloud Computing
(from another presentation)
Application runs Application runs at a Application runs using
on-premises hoster cloud platform
• Bring my own • Rent machines,
connectivity, • Shared,
machines, Pay someone for a pool of
connectivity, software
Pay someone to host my
multi-tenant
computing resources that can
software, etc. • application using hardware
Less control, but environment set of
be applied to a
that I specify applications
Buy my own hardware, and
•manage my owncontrol
Complete data center fewer • Offers pool of
and responsibility responsibilities computing
• Lower capital costs, resources,
• Upfront capital abstracted from
costs for the but pay for fixed
capacity, even if idle infrastructure
infrastructure
• Pay as you go
MOSE – University of Trieste 30 April, 2010 - slide 41
42. New Application Opportunities
Some Interesting New Types of Applications Enable By the Cloud:
Mobile Interactive Apps: Applications that respond in real time but work with lots of data. Cloud computing offers highly-available
large datasets.
Parallel Batch Processing: “Cost Associativity” – Many systems for a short time. Washington Post used 200EC2 instances to process
17,481 pages of Hillary Clinton’s travel documents within 9 hours of their release.
Rise of Analytics: Again, “Cost Associativity” – Many systems for a short time.
Compute intensive data analysis which may be parallelized.
Compute Intensive Desktop Apps: For example, symbolic mathematics requires lots of computing per unit of data. Cost efficient to
push the data to the cloud for computation
MOSE – University of Trieste 30 April, 2010 - slide 42
43. Conclusions and Questions
about the Cloud of Tomorrow
Utility Computing: It‘s Happening!
Grow and Shrink on Demand
Pay-As-You-Go
Cloud Provider‘s View
Huge Datacenters Opened Economies and Possibilities
Cloud User‘s View
Startups Don‘t Need Datacenters
Established Organizations Leverage Elasticity
UC Berkeley Has Extensively Leveraged Elasticity to Meet Deadlines
Cloud Computing: High-Margin or Low-Margin Business?
Potential Cost Factor of 5-7X
Today‘s Cloud Providers Had Big Datacenter Infrastructure Anyway
Implications of Cloud:
Application Software: Scale-Up and Down Rapidly; Client and Cloud
Infrastructure Software: Runs on VMs; Has Built-in Billing
Hardware Systems: Huge Scale; Container-Based; Energy Proportional
MOSE – University of Trieste 30 April, 2010 - slide 43
44. Top 10 Obstacles and Opportunities
Obstacle Opportunity
1 Availability of Service Use Multiple Cloud Providers;
Use Elasticity to Prevent DDOS
2 Data Lock-In Standardized APIs; Compatible Software to
Enable Surge Computing
3 Data Confidentiality and Auditability Deploy Encryption, VLANs, Firewalls;
Geographical Data Storage
4 Data Transfer Bottlenecks FedExing Disks; Data Backup/Archival;
Higher Bandwidth Switches
5 Performance Unpredictability Improved VM Support; Flash Memory; Gang
Scheduling VMs
6 Scalable Storage Invent Scalable Store
7 Bugs in Large Distributed Systems Invent Debugger that Relies on Dist VMs
8 Scaling quickly Auto-Scaler; Snaphots for Conservation
9 Reputation Fate Sharing Reputation Guarding Services
10 Software Licensing Pay-for-Use Licenses; Bulk Use Sales
MOSE – University of Trieste 30 April, 2010 - slide 44
45. #3 Obstacle: Data Confidentiality and Auditability
“My sensitive corporate data will never be in the cloud!”
Current Clouds Are Essentially Public Auditability Is Required
Networks
Sarbanes-Oxley
They Are Exposed to More Attacks HIPAA
Berkeley Believes There Are No Fundamental Obstacles
to Making Cloud Computing as Secure as Most In-House IT
Encrypted Storage Virtual LANs Network Middleboxes (Firewalls, Packet Filters)
Encrypted Data in the Cloud Is Likely More Secure than Unencrypted Data on Premises
Maybe: Cloud Concerns over
More Focus on
Provided Auditability Virtual National Boundaries USA PATRIOT Act Gives
Capabilities… Some Europeans
Auditing Below VMs Foreign Subpoenas Worries over SaaS in the
USA
Maybe More Tamper Resistant Blind Subpoenas
MOSE – University of Trieste 30 April, 2010 - slide 45
46. #4 Obstacle: Data Transfer Bottlenecks
Problem: At $100 to $150 per Terabyte Transferred,
Data Placement and Movement Is an Issue
Opportunity-1: Sneaker-Net Opportunity-2: Keep Data
Jim Gray Found Cheapest Transfer in Cloud
Was FedEx-ing Disks If the Data Is in the Cloud,
1 Data Failure in 400 Attempts Transfer Doesn‘t Cost
Example: Ship 10TB from Amazon Hosting Large Data
UC Berkeley to Amazon
E.g. US Census
-- WAN: S3 < 20Mbits/sec:
10TB 4Mil Seconds > 45 Days
Free on S3; Free on EC2
$1000 in AMZN Net Fees Entice EC2 Business
-- FedEx: Ten 1TB Disks via Overnight Shipping
< 1 Day to Write 10TB to Disks Locally
Opportunity-3: Cheaper WAN
Cost ≈ $400 High-End Routers Are a Big
Effective BW of 1500Mbits/Sec Part of the Cost of Data
“NetFlix for Cloud Computing” Transfer
Research into Routing using
Cheap Commodity Computers
MOSE – University of Trieste 30 April, 2010 - slide 46
47. To better understand, read the originals...
Above the Clouds: a Berkeley View of Cloud Computing
http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdfCloud Computing
Book Report‖ on the UC Berkeley Paper ―Above the Clouds: a Berkeley
View of Cloud Computing‖
http://blogs.msdn.com/pathelland/archive/2009/04/10/book-report-on-the-uc-berkeley-
paper-above-the-clouds-a-berkeley-view-of-cloud-computing.aspx
http://cid-84f3c5ef51d06e8b.skydrive.live.com/self.aspx/.Public/2009/Above-the-Clouds-
090401k.pptx
Demystifying the Cloud (Simon Guest)
http://simonguest.com/blogs/smguest/archive/2009/05/14/Slides-from-TechEd-2009.aspx
An introduction to Cloud Computing
http://s3.amazonaws.com/ppt-download/ima-cloud-computing-mar2010-v8-100320181538-
phpapp02.pdf?Signature=GhK3ogCr2Z%2FzhWFa%2F%2BJUr1cT1eg%3D&Expires=12699
58049&AWSAccessKeyId=AKIAJLJT267DEGKZDHEQ
…and many others
MOSE – University of Trieste 30 April, 2010 - slide 47
48. A Spectrum of Application
Models
Less Constrained Constraints in the App Model More Constrained
Google App Engine
Amazon AWS Microsoft Azure Traditional Web Apps
.NET CLR/Windows Only Auto Scaling/Provisioning
VMs Look Like Hardware
Choice of Language
No Limit on App Model
Some Auto Failover/ Scale (but Force.Com
User Must Implement Scalability and
needs declarative application
Failover SalesForce Biz Apps
properties)
Auto Scaling/Provisioning
Less Automation Automated Management Services More Automation
MOSE – University of Trieste 30 April, 2010 - slide 48
49. Spectrum of Clouds
Instruction Set VM (Amazon EC2, 3Tera)
Bytecode VM (Microsoft Azure)
Framework VM
Google AppEngine, Force.com
Lower-level, Higher-level,
Less management More management
EC2 Azure AppEngine Force.com
49
MOSE – University of Trieste 30 April, 2010 - slide 49
50. A Spectrum of Application Models
Which Model Will Dominate?? High-Level Languages and
Frameworks Can Be Built on Lower-
Analogy: Programming Languages and Frameworks Level
• Low-Level Languages (C/C++) Allow Fine-Grained Control
• Building a Web App in C++ Is a Lot of Cumbersome Work
• Ruby-on-Rails Hides the Mechanics but Only If You Follow More-Constrained Clouds May Be
Request/Response and Ruby’s Abstractions Built on Less-Constrained Ones
MOSE – University of Trieste 30 April, 2010 - slide 50
51. Deploying A Service Manually
Resource allocation
Machines must be chosen to host roles of the service
Fault domains, update domains, resource utilization, hosting environment, etc.
Procure additional hardware if necessary
IP addresses must be acquired
Provisioning
Machines must be setup
Virtual machines created
Applications configured
DNS setup
Load balancers must be programmed
Upgrades
Locate appropriate machines
Update the software/settings as necessary
Only bring down a subset of the service at a time
Maintaining service health
Software faults must be handled
Hardware failures will occur
Logging infrastructure is provided to diagnose issues
This is ongoing work…you‘re never done
MOSE – University of Trieste 30 April, 2010 - slide 51
52. Windows Azure Service Lifecycle
Goal is to automate life cycle as much as possible
Coding & Provisioning Deployment Maintain goal
Modeling state
•New services •Desired •Mapping and •Monitor
and updates configuration deploying to •React to
actual events
hardware
•Network
configuration
Developer Developer/ Automated Automated
Deployer
MOSE – University of Trieste 30 April, 2010 - slide 52
53. ARCHITECTURE OF A CLOUD
ENVIRONMENT
users
INTERNET
Application
Application
Application Data Application
s
developers
COMPUTE STORAGE
Platform
FABRIC
Infrastructure
MOSE – University of Trieste 30 April, 2010 - slide 53
54. Domain Specific Cloud
Components for General
Availability in the Research
Marco Parenzan
•Tenure - Web Service Programming
Computer Engineering – University of Trieste
•Researcher
Methodologies and Tools
MOSE Laboratory – University of Trieste
Maurizio Fermeglia
•Full Professor Chemical Engineering
MOSE Laboratory – University of Trieste
MOSE – University of Trieste 30 April, 2010 - slide 54
55. MOSE: Molecular Simulation Engineering
Vision
Multi – Scale Molecular Modeling will revolution the
world of research and industrial production in the next
years by strongly accelerating the development of new
products.
Mission
Material Sciences: thermo physical properties for materials, polymer
technology and nanoscience/nanotechnology
Life Sciences: drug-receptor interactions, drug-design, QSAR, drug-
S02 S08
delivery…
Process simulation: process synthesis, design, modeling for chemical,
S01 COL1
MAKUPB
COL2
S05 H3
T1
H1 P1
S13Z MAKUPA
S12
F1 Q1
S07
S10
M1
S06
H2
biochemical, energy production
H4
S14 S13
MOSE – University of Trieste 30 April, 2010 - slide 55
56. Multiscale Molecular Modeling
Characteristic Time
years Engineering
Engineering
design
design
hours
Simulazione
Process
minutes Simulation
di processo
FEM
seconds
Mesoscale
modeling FEM
(segments)
microseconds
Meccanica
Molecular
nanoseconds Mechanics
molecolare
(atoms)
(atomi)
picoseconds Quantum
Meccanica
Mechanics
Quantistica
femtoseconds (electrons)
(elettroni)
1Å 1nm 1μm 1mm 1m
Characteristic Length
MOSE – University of Trieste 30 April, 2010 - slide 56
57. Message Passing Multiscale Molecular Modeling
Engineering
design
Process
Simulation
FEM
Mesoscale
modeling
(segments)
Molecular
Mechanics
(atoms)
Quantum
Meccanica
Mechanics
Quantistica
(electrons)
(elettroni)
MOSE – University of Trieste 30 April, 2010 - slide 57
58. Cloud-based Message Passing for
Multiscale Molecular Modeling
Engineering
Engineering
design
Quantum design
Mechanics Process
(electrons) Simulation
FEM
Simulazione
di processo
FEM
Mesoscale Meccanica
modeling molecolare
(segments) (atomi)
MOSE – University of Trieste 30 April, 2010 - slide 58
59. Abstract
This paper deals with availability of cloud computing to computational research labs. We will focus to the
concept of availability. This concept may have two different interpretations, namely:
―Available‖ as an accessible resource, always, from everywhere
―Available‖ as the ability to consume a service (as a client or as the publisher)
AvailableAccessible
This paper will focus on the second interpretation: a cloud service is ―available‖ if it is easy for anyone in the
academic community (and not) to consume the cloud. Indeed, cloud allows sharing ―knowledge‖ in form of
components or data to be ―executed‖ in the cloud. The challenge here is to make possible for
researchers, not necessarily expert in programming and computer science, to make
available her/his knowledge in form of components and data tables.
The solution we propose is based on Domain Specific Languages (DSL), by which a researcher will express the
components in her/his specific language, that will be user-friendly since it is directly related to the particular
research field. In this framework, cloud components will be expressed in terms of a generic mathematical model
rather than a software component. This vision is quite common in computing thanks to the availability of many
tools that simplify the development of DSL such as dynamic languages like IronRuby or revolutionary data
access with SQL Server Modeling.
The objective of this work is to present a model of a general ―Domain Specific Cloud Component‖ (DSCC) that
can be expressed, published and consumed by the research community using tools that allow an easy and direct
implementation for the mathematical algorithms developed by the scientists. The general concept will be applied
to specific examples by developing frameworks customized to share a specific ―DSCC‖.
Examples will be taken in the area of multiscale molecular modeling for the design of nanostructured polymer
systems (nanotechnology) and the estimation of the environmental impact of a production process
(sustainability).
MOSE – University of Trieste 30 April, 2010 - slide 59
60. MOSE in the cloud…
…no. Why?
Because it heavily depends on software (molecular simulation, process
simulation) that are not on the Cloud
Can MOSE access ―alone‖ the Cloud? No, at the moment
The actors:
Chemists, Chemical Engineers, Materials Engineers, Biologists, Medical
Doctors …
Just ―Computer Science‖ classes in the first two years of Engineering
Curriculum (some C/C++, no VB(A) or .NET, some Matlab)
But they need programs to solve their problems…
…and sometimes they try to write them!
MOSE – University of Trieste 30 April, 2010 - slide 60
61. Objectives of this Research
Move MOSE to the Cloud!
Cannot wait software companies
Computer engineers can ―simplify‖ write these codes
But she needs speaking with (non-computer) engineers about the
details (Analysis, Specifications, ―DOMAIN‖)
Why don‘t we enable (non-computer) scientists writing their
own code?
Simplifying (programming) tools to consume the Cloud
Allow DOMAIN Engineers participating actively building the
platform
MOSE – University of Trieste 30 April, 2010 - slide 61
62. Simplification development path
We are ‗still @ C++‘ (some apps need C++ plug in/custom code)
We already stepped into CLR world
Example our development in CAPE-OPEN (http://co-lan.org)
The next step are
Dynamic Languages such as Python or Ruby
DSLs world for data (custom data texts)
Again another example in CAPE-OPEN
Native VM Dynamic DSL
Internal
C/C++ C#/Java Python/Ruby
External
MOSE – University of Trieste 30 April, 2010 - slide 62
63. Traditional App Architecture: n-Tier apps
Presentation
Infrastructure
Business Logic (BLL)
Data Access (DAL)
MOSE – University of Trieste 30 April, 2010 - slide 63
64. Onion Architecture
Domain Driver Design User Interface
Application Services
Domain Services Database
Domain
M
Model Services
G
File
system
etc
MOSE – University of Trieste 30 April, 2010 - slide 64
71. VISUAL UNIT
PROGRAMMING WITH
CAPEOPENSTUDIO.NET
Marco Parenzan, Maurizio Fermeglia
MOSE Lab – University of Trieste
MOSE – University of Trieste 30 April, 2010 - slide 71
74. Language: our tool
Il linguaggio di programmazione è il nostro strumento (―la
nostra cassetta degli attrezzi‖)
Ma la metafora (meccanica) si ferma qui
Una chiave inglese è immutabile
I nostri linguaggi no
Non siamo più nel XX secolo in cui VB6, C, C++, Pascal, Perl, Java,
Javascript erano a lungo stabili…
Stiamo vivendo un momento particolarmente fertile…
MOSE – University of Trieste 30 April, 2010 - slide 75
75. A Language Renaissance
Diversi paradigmi di programmazione
Imperativo
Object Oriented
Funzionale
Dichiarativo
Visual Studio 2010 esce con un nuovo linguaggio ufficiale
(F#)
Tipizzazione
Statica (compile type)
Dinamica (runtime)
MOSE – University of Trieste 30 April, 2010 - slide 76
76. Why should we care then?
More languages, more
options
DLR gives apps instant C# 4.0
scripting abilities Dynamic
C# has moved in that C# 3.0
Programming
direction too! Language Integrated
Query
LINQ C# 2.0
Lambda expressions Generics
Parallel extensions (C# C# 1.0
4.0) Managed Code
‗dynamic‘ (C# 4.0) and
‗var‘ keywords
MOSE – University of Trieste 30 April, 2010 - slide 77
77. Using internal DSL
(aka Fluent Interface)
MOSE – University of Trieste 30 April, 2010 - slide 78
82. Good
Unlimited expressiveness
You choose execution
environment
Bad
Requires “more” work
No IDE support
MOSE – University of Trieste 30 April, 2010 - slide 83
83. Don Box
MOSE – University of Trieste 30 April, 2010 - slide 84
84. Don Box Career
DevelopMentor years (‗90ties)
Worldwide COM expert (Essential COM)
Millenium Work (‗90ties thru 21° century)
SOAP Specifications
XML musings (Essential XML)
XML Schema
XML Infoset
.NET Expert (Essential .NET)
TechEd 2001, Barcelona
Musings having a bath in a tube on stage
Microsoft years (since 2002)
Indigo Architect (Windows Communication Foundation – 2° generation
Web Services - .NET 3.0)
Oslo (now SQL Server Modeling)
MOSE – University of Trieste 30 April, 2010 - slide 85
85. Il linguaggio “M”
DSL Point.m DSLX DomainX.m DSLY DomainY.m
Domain Model Domain Model Domain Model
GPSLanguage.mg DomainX.mg DomainY.mg
Domain Grammar Domain Grammar Domain Grammar
"M"
Domain-specific data models type Point {
X : Integer where X < 100;
Y : Integer?;
MSchema }
Domain-specific grammars language GPSLanguage {
syntax Main = h:Integer ("," v:Integer)?
=> Point { X { h }, Y { v }};
}
MGrammar
Abstract data model Point { X { 100 }, Y { 200 } }
MGraph
86
MOSE – University of Trieste 30 April, 2010 - slide 86
86. Da M al Repository
Da “Oslo” a “SQL Server Modeling”
ModelA.m M.exe MX.exe
Domain Model Domain Model
ModelB.m Compiler Loader
ModelC.m ModelABC.mx
SQL
M M
Framework Framework Server
MOSE – University of Trieste 30 April, 2010 - slide 87
87. IntelliPad
Chiamato inizialmente EMACS.NET
Editor testuale, non ha funzioni visuali
Buffer interni interagiscono con runtime
Parsing in tempo reale
Generazione risultati in finestre side-by-side
MOSE – University of Trieste 30 April, 2010 - slide 88
88. MGrammar in Intellipad
Input Grammar Output
Text MGraph
Transfor
m
Errors
MOSE – University of Trieste 30 April, 2010 - slide 89
90. ANTLR Overview
ANother Tool for Language Recognition
written by Terence Parr in Java
Easier to use than most/all similar tools
graphical grammar editor and debugger I’m a
Supported byBovet using Swing
written by Jean
ANTLRWorks professor at the
University of San
Used to implement Francisco.
domain-specific languages (DSLs)
“real” programming languages Ter
http://www.antlr.org I worked with
Ter as a masters
download ANTLR and ANTLRWorks here student there.
both are free and open source
docs, articles, wiki, mailing list, examples
Jean
ANTLR 3
3
MOSE – University of Trieste 30 April, 2010 - slide 91
91. ANTLR Overview ...
Uses EBNF grammars
can directly express optional and repeated elements BNF grammars require more
Extended Backus-Naur Form verbose syntax to express these.
supports subrules (parenthesized groups of elements)
Supports many target languages
for generated code
Java, Ruby, Python, Objective-C, C, C++ and C#
Provides infinite lookahead
most parser generators don’t
used to choose between rule alternatives
Plug-ins available for
4 ANTLR 3
IDEA and Eclipse
MOSE – University of Trieste 30 April, 2010 - slide 92
92. ANTLR Overview ...
Supports LL(*)
LL(k) parsers are top-down parsers that
construct a Leftmost derivation of the input
parse from Left to right Wikipedia has
look ahead k tokens
good descriptions
of LL and LR.
LR(k) parsers are bottom-up parsers that
parse from Left to right
construct a Rightmost derivation of the input
look ahead k tokens
LL parsers can’t handle left-recursive rules
most people find LL grammars easier to understand than LR
Supports predicates
aid in resolving ambiguities (non-syntactic rules)
5 ANTLR 3
MOSE – University of Trieste 30 April, 2010 - slide 93
93. ANTLRWorks
A graphical grammar editor and debugger
Features
highlights grammar syntax errors
checks for grammar errors beyond the syntax variety
such as conflicting rule alternatives
displays a syntax diagram for the selected rule
debugger can step through creation of parse trees and ASTs
38 ANTLR 3
MOSE – University of Trieste 30 April, 2010 - slide 94
94. ANTLRWorks ...
parser rule
syntax diagram
39 ANTLR 3
lexer rule
MOSE – University of Trieste syntax diagram 30 April, 2010 - slide 95
95. ANTLRWorks ...
grammar check
result
40 ANTLR 3
MOSE – University of Trieste 30 April, 2010 - slide 96
96. ActiProSoftware
Free add-ons are
included that
integrate domain-
specific language
(DSL) parsers created
using Microsoft Oslo's
MGrammar and
ANTLR with
SyntaxEditor.
MOSE – University of Trieste 30 April, 2010 - slide 97
97. Le peculiarità di un Dynamic Language
Semplice e stringato
È una scelta tipica di chi ha sviluppato questi linguaggi
Sintassi leggera
Molte funzioni lasciate in API in linguaggi statici sono implementate nel
linguaggio
Interpretato
È la diretta conseguenza della semplicità, evitando la ―complicazione‖
di un processo di compilazione
Implicitamente tipizzato
Il tipo è associato ai valori, non alle variabili
Non permettono di verificare e notificare gli errori di tipo se non
quando vanno in esecuzione
98
MOSE – University of Trieste 30 April, 2010 - slide 98
98. Cos’è Python?
Un linguaggio di programmazione general purpose
Sviluppato da Guido van Rossum nel 1991
Un linguaggio dinamico usato spesso come linguaggio di
scripting
Supporta diversi paradigmi di programmazione:
Object Oriented
Imperative
Functional
È stato creato con questi obiettivi in mente:
Leggibilità del codice
Sintassi minimalista
Un esteso set di librerie
Duck Typing
99
MOSE – University of Trieste 30 April, 2010 - slide 99
99. Cos’è IronPython?
È una implementazione del linguaggio Python su piattaforma .NET
IronPython è scritto interamente in C#
Creato da Jim Hugunin
sviluppatore anche di Jython (Python su JVM)
Voleva scrivere un paper dal titolo ―Why .NET is a Terrible Platform for
Dynamic Languages‖
―Itwas a little less than a year ago that I first started
investigating the Common Language Runtime (CLR). My
plan was to do a little work and then write a short pithy
article called, "Why .NET is a terrible platform for dynamic
languages‖" http://www.ironpython.com/old.html
Nel settembre del 2004 iniziava a lavorare in Microsoft
―My plans changed when I found the CLR to be an
excellent target for the highly dynamic Python language.
Since then I've spent much of my spare time working on
the development of IronPython‖ http://www.ironpython.com/old.html
http://www.python.org/community/pycon/dc2004/papers/9/
http://conferences.oreillynet.com/presentations/os2004/hugunin_jim_up.ppt
100
MOSE – University of Trieste 30 April, 2010 - slide 100
100. Armando Fox (U.C. Berkley)
Ha parlato di uso di
Python in ambienti
Cloud
Parla di PLL
(Production Level
Languages)...
Vs. BLL
MOSE – University of Trieste 30 April, 2010 - slide 101
101. Dynamic Languages on .NET
IronPython IronRuby C# VB.NET Others…
Dynamic Language Runtime
Expression Trees Dynamic Dispatch Call Site Caching
Object JavaScript Python Ruby COM
binder binder binder binder binder
MOSE – University of Trieste 30 April, 2010 - slide 102
102. Storage in Windows Azure
GOAL:
SCALABLE, DURABLE
STORAGE Tables: simply Queues: serially
structured data,
Blobs: large, accessed messages
accessed using
unstructured or requests, allowing
Windows Azure storage is an data (audio,
ADO.NET Data web-roles and worker-
application managed by the Services
video, etc) roles to interact
Fabric Controller
Windows Azure applications can use
native storage or SQL Azure
Application state is kept in storage
services, so worker roles can
replicate as needed
MOSE – University of Trieste 30 April, 2010 - slide 103
103. Simplification steps
1. Write apps running on cloud
1. Windows Azure
2. (ASP.NET MVC2) Web Role for the front-end
3. Worker Role for background processing
4. Table, Blob and Queue for ―unstructured‖, but easy, storage
2. Use Dynamic Languages to do the processing
1. Simplified deployment
2. Simplified ―code‖ model
3. Simplified type management (dynamic typing, no variable declaration)
4. Now fully integrated in .NET with DLR and IronPython and IronRuby
3. Input and Output as structured text
1. ―M‖ (in ―Oslo‖, now SQL Server Modeling) gives us a generic schema
language (more general that XSD) and more ―readable‖ than xml
2. This gives structure and metadata to the Azure Storage data (as
requested by Ed Lazowska in his yesterday wonderful keynote)
MOSE – University of Trieste 30 April, 2010 - slide 104
104. Domain Specific Cloud
Components for General
Availability in the Research
Demo
MOSE – University of Trieste 30 April, 2010 - slide 105
105. The matrix was too simple?
This is a two-dimensional matrix of three dimensional vectors
Size of cube is: 100 nano meters
MOSE – University of Trieste 30 April, 2010 - slide 106
106. The results
1. Write apps running on cloud
1. Windows Azure
2. (ASP.NET MVC2) Web Role for the front-end
3. Web Role for background processing
2. Use Dynamic Languages to do the processing
1. Simplified deployment
2. Simplified ―code‖ model
3. Simplified type management (dynamic typing, no
variable declaration)
4. Now fully integrated in .NET with DLR and
IronPython and IronRuby
3. Input and Output as structured text
1. Oslo (now SQL Server Modeling) gives us a generic
schema language (more general that Xsd) and
more ―readable‖ than xml
2. Structured text as data sources
MOSE – University of Trieste 30 April, 2010 - slide 107
107. Conclusions
Why MOSE needs the cloud?
To build a platform to orchestrate the message passing in Multiscale Molecular Modeling
activity
To empower our research team with a flexible scientific platform that drives efficiency,
collaboration and innovation
In the demo we have seen
The ―creation‖ and the execution (invocation) of the single step of the process
The input and the output are the ―messages‖ that walk through the scale
The code:
Definition of a library of a generic cloud component
Usage of Dynamic Languages (IronPython)
A new opportunity in .NET development
More productive (PLLs, as told by Armando Fox yesterday)
More simpler for non programmers
Application of DSLs (Oslo) for the definition of simple input/output messages
More confident with scientific people
More simple than a graphical UI to implement
It gaves metadata/schema to flat files (as requested by Ed Lazowska in his yesterday wonderful
keynote)
What‘s next?
MOSE – University of Trieste 30 April, 2010 - slide 108
108. What is next?
Continue with the project
The definition of a process (an orchestration)
Did you saw the session from Paul Watson yesterday? (―Cloud
Computing from chemical Property Prediction‖)
The users in the process
Collaboration in the process
Again, as Paul said, we agree on a structure like a ―social science
community‖, a Web 2.0 application
Security, Confidentiality
Verticalization on the domain
Remove all the nitty-gritty details that lowers the experience
Define custom component Languages
MOSE – University of Trieste 30 April, 2010 - slide 109
109. Simplification steps
1. Write apps running on cloud
1. Windows Azure
2. (ASP.NET MVC2) Web Role for the front-end
3. Worker Role for background processing
4. Table, Blob and Queue for ―unstructured‖, but easy, storage
2. Use Dynamic Languages to do the processing
1. Simplified deployment
2. Simplified ―code‖ model
3. Simplified type management (dynamic typing, no variable declaration)
4. Now fully integrated in .NET with DLR and IronPython and IronRuby
3. Input and Output as structured text
1. ―M‖ (in ―Oslo‖, now SQL Server Modeling) gives us a generic schema
language (more general that XSD) and more ―readable‖ than xml
2. This gives structure and metadata to the Azure Storage data
4. Write DSL
MOSE – University of Trieste 30 April, 2010 - slide 110
110. Writing a Custom DSL
(Supposed)Needs of the ―non-
cloud component
#naming part (entry point)
Name = "test 0004"
programmer‖ # declarative part
Libraries # sections like cobol
Integrated functionalities input
i(label = "Input Vector")
No ―include‖ data
# static declaration
Data Access as Libraries m(name = "matrix01" # this is the "query"
label = "multiplication matrix")
Connect Command Execute LINQ output
o(label = "Output Vector")
Define Datasource (Metadata), no SQL
schema # coding part
# dynamic like python (and vb)
All-in-one # verbose like visual basic
One Component, one ―file‖ (as much as code "this is the main"
possible)
# alterernative syntax of query from storage
# calculated
Simplifing deployment m = lookup in Matrici
for NomeMatrice
Need of the programmer ### multiline
comment ###
Not so (much) imperative, not so (much)
assign 0 to r
functional, not so (much) object oriented while r is less then m.rows do
assign 0 to c
State is not so bad assign 0nm to a
while c is less then m.cols do
Lambda are cool (no functions, all lambdas) #a = a + m(r,c) * i(c)
increment a by m(r,c) * i(c)
Escape to power (if DSL is ―poor‖) # python has no matrix, but jagged arrays
increment c by 1
Backend of a full language, totally integrated end do
assign a to o
DLR, (Iron)Python, (Iron)Ruby, (Iron)JS increment r by 1
end do
(Javascript) and so on
MOSE – University of Trieste 30 April, 2010 - slide 111
113. Cloud Futures 2010
Le sessioni
MOSE – University of Trieste 30 April, 2010 - slide 114
114. Le sessioni
Esperienze su Private Clouds
Idee su Public/Hybrid Clouds
Esperienze ―sistemistiche‖
meno...esperienze ―programmative‖
MOSE – University of Trieste 30 April, 2010 - slide 115
115. Cloud Computing for Chemical Property
Prediction
Paul Watson, Newcastle University
MOSE – University of Trieste 30 April, 2010 - slide 116
116. Bertrand Meyer
ETH Zurich
MOSE – University of Trieste 30 April, 2010 - slide 117
117. Cloud Futures 2010
Le sessioni degli Italiani
MOSE – University of Trieste 30 April, 2010 - slide 118
118. Danilo Montesi
Danilo Montesi (UniBO) ha
presentato il progetto ―Connected
City Campus‖, per connettere
diverse strutture (dall‘ospedale
all‘università alla biblioteca)
facilitando la comunicazione e i
servizi per i cittadini e sfruttando
dalla rete wireless già esistente.
Come dicevo, molte domande alla
fine sono arrivate sulle leggi
italiane in tema di
privacy/conservazione dei dati.
MOSE – University of Trieste 30 April, 2010 - slide 119
119. Fabio Panzieri
Fabio Panzieri -nella foto
con Judith Bishop, Direttore
Relazioni Esterne di Microsoft
Research- anch‘egli di UniBO,
ha presentato ―QoS-aware
Clouds‖ che propone la
creazione di un middleware
all‘interno della piattaforma di
Cloud per assegnare ―fette di
cloud‖ in modo dinamico e in
funzione del livello di servizio
acquistato dagli utenti.
MOSE – University of Trieste 30 April, 2010 - slide 120
120. Domenico Talia
Domenico Talia dell‘Università
della Calabria, probabilmente fra
noi più coinvolto nell‘argomento
Cloud trattandolo già nella sua
didattica in progetti nazionali ed
europei, ed insegnando proprio
Grid Computing. La sua
presentazione ha ricevuto molte
domande perché ha presentato
un problema reale in gran parte
già risolto tramite soluzioni cloud
open source, ovvero con la
definizione di un framework che
permette agli sviluppatori di
creare processi componendo
servizi disponibili su cloud o
intercloud.
MOSE – University of Trieste 30 April, 2010 - slide 121
121. Antonio Cisternino
La presentazione di Antonio
Cisternino dell‘Università di
Pisa (Informatica) -veterano di
questi eventi e introdotto nel
gruppo di Redmond da oltre
10 anni- ha esposto un
sistema di controllo dinamico
per virtual machine, con client
già disponibili anche per
dispositivi mobili in HTML5, ad
uso di farm di servizi cloud che
debbano garantire disponibilità
e risparmio energetico.
MOSE – University of Trieste 30 April, 2010 - slide 122
122. Marco Parenzan
Marco Parenzan, ricercatore
dell‘Università di Trieste e il
più giovane della nostra
delegazione, ha presentato un
progetto molto interessante
per rendere disponibile l‘uso
del cloud a ricercatori NON
esperti in computer science
(nel caso specifico, a chimici)
attraverso l‘uso di un
linguaggio (DSL) che loro di
esprimere e definire in
maniera semplice le richieste
elaborative e i dati su cui esse
operano.
MOSE – University of Trieste 30 April, 2010 - slide 123
123. Grazie! Q&A
blog: http://blog.codeisvalue.com/
email: marco.parenzan@libero.it
web: http://www.codeisvalue.com/
skype: marco.parenzan
messenger marco.parenzan@live.it
slides http://www.slideshare.com/marco.parenzan
twitter: marco_parenzan
MOSE – University of Trieste 30 April, 2010 - slide 124