Revolution Analytics provides Revolution R, which adds functionality to the open source R programming language for statistical analysis and predictive modeling. Revolution R improves R's productivity, performance, and ability to handle large datasets. It features an interactive development environment, multi-threaded math for faster computation, and tools to perform distributed, parallel analytics on big data in Hadoop and databases.
In-Database Analytics Deep Dive with Teradata and RevolutionRevolution Analytics
Teradata and Revolution Analytics worked together to develop in-database analytical capabilities for Teradata Database. Teradata v14.10 provides a foundation for in-database analytics in Teradata. Revolution Analytics has ported its Revolution R Enterprise (RRE) Version 7.1 to use the in-database capabilities of version 14.10. With RRE inside Teradata, users can run fully parallelized algorithms in each node of the Teradata appliance to achieve performance and data scale heretofore unavailable. We'll get past the market-ecture quickly and dive into a “how it really works” presentation, review implications for system configuration and administration, and then take questions from Teradata users who will be charged with deploying and administering Teradata systems as platforms for big data analytics inside the database engine.
Revolution R Enterprise - 100% R and More Webinar PresentationRevolution Analytics
R users already know why the R language is the lingua franca of statisticians today: because it's the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this presentation, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
In-Database Analytics Deep Dive with Teradata and RevolutionRevolution Analytics
Teradata and Revolution Analytics worked together to develop in-database analytical capabilities for Teradata Database. Teradata v14.10 provides a foundation for in-database analytics in Teradata. Revolution Analytics has ported its Revolution R Enterprise (RRE) Version 7.1 to use the in-database capabilities of version 14.10. With RRE inside Teradata, users can run fully parallelized algorithms in each node of the Teradata appliance to achieve performance and data scale heretofore unavailable. We'll get past the market-ecture quickly and dive into a “how it really works” presentation, review implications for system configuration and administration, and then take questions from Teradata users who will be charged with deploying and administering Teradata systems as platforms for big data analytics inside the database engine.
Revolution R Enterprise - 100% R and More Webinar PresentationRevolution Analytics
R users already know why the R language is the lingua franca of statisticians today: because it's the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this presentation, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
In this presentation from Revolution Analytics, Bill Jacobs presents: Are You Ready for Big Data Analytics?
"Revolution Analytics delivers advanced analytics software at half the cost of existing solutions. By building on open source R—the world's most powerful statistics software—with innovations in big data analysis, integration and user experience, Revolution Analytics meets the demands and requirements of modern data-driven businesses."
Learn more: http://www.revolutionanalytics.com
Watch the presentation video: http://wp.me/p3RLEV-12S
100% R and More: Plus What's New in Revolution R Enterprise 6.0Revolution Analytics
R users already know why the R language is the lingua franca of statisticians today: because it's the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this webinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
VP of Product Development, Dr. Sue Ranney will also provide an overview of the features introduced in Revolution R Enterprise 6.0 including:
1. Big Data Generalized Linear Model, the new RevoScaleR function that provides a fast, scalable, distributable implementation of generalized linear models, offering impressive speed-ups relative to glm on in-memory data frames
2. Platform LSF Cluster Support, which allows you to create a distributed compute context for the Platform LSF workload manager
3. Azure Burst support added to RxHpcServer
4. Updated R engine (R 2.14.2)
5. Ability to use RevoScaleR analysis functions with non-xdf data sources such as SAS, SPSS or text
6. New methods for RxXdfData data sources including head, tail, names, dim, colnames, length, str, and formula
7. New function rxRoc for generating ROC curves
Big data analytics on teradata with revolution r enterprise bill jacobsBill Jacobs
Revolution Analytics brings big data analytics to Teradata database. Presentation from Teradata Partners, October 2013 overviewing Revolution R Enterprise for Teradata by Bill Jacobs, Director, Product Marketing, Revolution Analytics.
Applications in R - Success and Lessons Learned from the MarketplaceRevolution Analytics
Adoption of the R language has grown rapidly in the last few years, and is ranked as the number-one data science language in several surveys. This accelerating R adoption curve has been driven by the Big Data revolution, and the fact that so many data scientists — having learned R at university — are actively unlocking the secrets hidden in these new, vast data troves.
In this webinar David Smith, Chief Community Officer, will take a look at the growth of R and the innovative uses of R in business, government and non-profit sectors. Then Neera Talbert, Vice President, Professional Services will take you into the trenches of recent customer deployments and share best practices and pitfalls to avoid in deploying or expanding your own R applications.
The use of R statistical package in controlled infrastructure. The case of Cl...Adrian Olszewski
Facts and myths on the use of the R statistical package in controlled, validated environments by the example of Clinical Research in the pharmaceutical industry. This is the first part constituting the introduction. Technical details will be presented in the part II.
This document was presented at a conference organized by Polish National Group of the International Society for Clinical Biostatistics.
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
GNU R in Clinical Research and Evidence-Based MedicineAdrian Olszewski
Is GNU R (an environment for statistical computing) suitable enough for Biostatisticians involved in Clinical Research? Can it replace or support SAS in this area? Well, I think this presentation may help to remove any doubts. If you are a Biostatistician (and probably a SAS user), you may find it useful.
The presentation is under constant improvement.
You can find it also on CRAN (contributed documentation) and at http://www.r-clinical-research.com
This session will demonstrate how the all-star line-up featuring R and Storm enables real-time processing on massive data sets; a real home run! The presenters will use actual baseball data and a real-world use case to compose an implementation of the use case as Storm components (spouts, bolts, etc.) and highlight how R can be an effective tool in prototyping a solution. Attendees will leave the session with information that could easily be applied for other use cases such as video game analytics, fraud detection, intrusion detection, and consumer propensity to buy calculations.
The business need for real-time analytics at large scale has focused attention on the use of Apache Storm, but an approach that is sometimes overlooked is the use of Storm and R together. This novel combination of real-time processing with Storm and the practical but powerful statistical analysis offered by R substantially extends the usefulness of Storm as a solution to a variety of business critical problems. By architecting R into the Storm application development process, Storm developers can be much more effective. The aim of this design is not necessarily to deploy faster code but rather to deploy code faster. Just a few lines of R code can be used in place of lengthy Storm code for the purpose of early exploration – you can easily evaluate alternative approaches and quickly make a working prototype.
Microsoft and Revolution Analytics -- what's the add-value? 20150629Mark Tabladillo
Microsoft has been a leader in the enterprise analytics space for years. In 2014, Microsoft had already created R language functionality within Azure Machine Learning. On April 6, 2015, Microsoft and closed on a deal to acquire Revolution Analytics, a company focusing on scalable processing solutions initiated by the well-known R language. Many data science projects and initial demos do not need high-volume solutions: however, having a high-volume answer for the R language allows for planning or working toward the largest data science solutions.
This presentation describes the add-value for the Revolution Analytics acquisition. The talk covers 1) an overview of current data science technologies from Microsoft; 2) a description of the R language; 3) a brief review of the add-value for R with Azure Machine Learning, and 4) a description of the performance architecture and demo of the language constructs developed by Revolution Analytics. Most of the presentation will be focused on sections two and four. It is anticipated that these technologies will be partially if not fully integrated into SQL Server 2016.
Turbo-Charge Your Analytics with IBM Netezza and Revolution R Enterprise: A S...Revolution Analytics
Everyone involved in high-stakes analytics wants power, speed and flexibility regardless of the size of the data set and complexity of the analysis. Trailblazing organizations that have deployed IBM Netezza Analytics with their IBM Netezza data warehouse appliances (TwinFin) with Revolution R Enterprise are getting all three.
Revolution Analytics was the first company dedicated to the R Project. This presentation from useR! 2014 covers the history of Revolution Analytics since its founding in 2007 and its contributions to the R project and community.
Presented to eRum (Budapest), May 2018
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe the doAzureParallel package, a backend to the "foreach" package that automates the process of spawning a cluster of virtual machines in the Azure cloud to process iterations in parallel. This will include an example of optimizing hyperparameters for a predictive model using the "caret" package.
By David Smith. Presented at Microsoft Build (Seattle), May 7 2018.
Your data scientists have created predictive models using open-source tools, proprietary software, or some combination of both, and now you are interested in lifting and shifting those models to the cloud. In this talk, I'll describe how data scientists can transition their existing workflows — while using mostly the same tools and processes — to train and deploy machine learning models based on open source frameworks to Azure. I'll provide guidance on keeping connections to data sources up-to-date, evaluating and monitoring models, and deploying applications that make use of those models.
More Related Content
Similar to Revolution R Enterprise - 100% R and More
In this presentation from Revolution Analytics, Bill Jacobs presents: Are You Ready for Big Data Analytics?
"Revolution Analytics delivers advanced analytics software at half the cost of existing solutions. By building on open source R—the world's most powerful statistics software—with innovations in big data analysis, integration and user experience, Revolution Analytics meets the demands and requirements of modern data-driven businesses."
Learn more: http://www.revolutionanalytics.com
Watch the presentation video: http://wp.me/p3RLEV-12S
100% R and More: Plus What's New in Revolution R Enterprise 6.0Revolution Analytics
R users already know why the R language is the lingua franca of statisticians today: because it's the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this webinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
VP of Product Development, Dr. Sue Ranney will also provide an overview of the features introduced in Revolution R Enterprise 6.0 including:
1. Big Data Generalized Linear Model, the new RevoScaleR function that provides a fast, scalable, distributable implementation of generalized linear models, offering impressive speed-ups relative to glm on in-memory data frames
2. Platform LSF Cluster Support, which allows you to create a distributed compute context for the Platform LSF workload manager
3. Azure Burst support added to RxHpcServer
4. Updated R engine (R 2.14.2)
5. Ability to use RevoScaleR analysis functions with non-xdf data sources such as SAS, SPSS or text
6. New methods for RxXdfData data sources including head, tail, names, dim, colnames, length, str, and formula
7. New function rxRoc for generating ROC curves
Big data analytics on teradata with revolution r enterprise bill jacobsBill Jacobs
Revolution Analytics brings big data analytics to Teradata database. Presentation from Teradata Partners, October 2013 overviewing Revolution R Enterprise for Teradata by Bill Jacobs, Director, Product Marketing, Revolution Analytics.
Applications in R - Success and Lessons Learned from the MarketplaceRevolution Analytics
Adoption of the R language has grown rapidly in the last few years, and is ranked as the number-one data science language in several surveys. This accelerating R adoption curve has been driven by the Big Data revolution, and the fact that so many data scientists — having learned R at university — are actively unlocking the secrets hidden in these new, vast data troves.
In this webinar David Smith, Chief Community Officer, will take a look at the growth of R and the innovative uses of R in business, government and non-profit sectors. Then Neera Talbert, Vice President, Professional Services will take you into the trenches of recent customer deployments and share best practices and pitfalls to avoid in deploying or expanding your own R applications.
The use of R statistical package in controlled infrastructure. The case of Cl...Adrian Olszewski
Facts and myths on the use of the R statistical package in controlled, validated environments by the example of Clinical Research in the pharmaceutical industry. This is the first part constituting the introduction. Technical details will be presented in the part II.
This document was presented at a conference organized by Polish National Group of the International Society for Clinical Biostatistics.
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
GNU R in Clinical Research and Evidence-Based MedicineAdrian Olszewski
Is GNU R (an environment for statistical computing) suitable enough for Biostatisticians involved in Clinical Research? Can it replace or support SAS in this area? Well, I think this presentation may help to remove any doubts. If you are a Biostatistician (and probably a SAS user), you may find it useful.
The presentation is under constant improvement.
You can find it also on CRAN (contributed documentation) and at http://www.r-clinical-research.com
This session will demonstrate how the all-star line-up featuring R and Storm enables real-time processing on massive data sets; a real home run! The presenters will use actual baseball data and a real-world use case to compose an implementation of the use case as Storm components (spouts, bolts, etc.) and highlight how R can be an effective tool in prototyping a solution. Attendees will leave the session with information that could easily be applied for other use cases such as video game analytics, fraud detection, intrusion detection, and consumer propensity to buy calculations.
The business need for real-time analytics at large scale has focused attention on the use of Apache Storm, but an approach that is sometimes overlooked is the use of Storm and R together. This novel combination of real-time processing with Storm and the practical but powerful statistical analysis offered by R substantially extends the usefulness of Storm as a solution to a variety of business critical problems. By architecting R into the Storm application development process, Storm developers can be much more effective. The aim of this design is not necessarily to deploy faster code but rather to deploy code faster. Just a few lines of R code can be used in place of lengthy Storm code for the purpose of early exploration – you can easily evaluate alternative approaches and quickly make a working prototype.
Microsoft and Revolution Analytics -- what's the add-value? 20150629Mark Tabladillo
Microsoft has been a leader in the enterprise analytics space for years. In 2014, Microsoft had already created R language functionality within Azure Machine Learning. On April 6, 2015, Microsoft and closed on a deal to acquire Revolution Analytics, a company focusing on scalable processing solutions initiated by the well-known R language. Many data science projects and initial demos do not need high-volume solutions: however, having a high-volume answer for the R language allows for planning or working toward the largest data science solutions.
This presentation describes the add-value for the Revolution Analytics acquisition. The talk covers 1) an overview of current data science technologies from Microsoft; 2) a description of the R language; 3) a brief review of the add-value for R with Azure Machine Learning, and 4) a description of the performance architecture and demo of the language constructs developed by Revolution Analytics. Most of the presentation will be focused on sections two and four. It is anticipated that these technologies will be partially if not fully integrated into SQL Server 2016.
Turbo-Charge Your Analytics with IBM Netezza and Revolution R Enterprise: A S...Revolution Analytics
Everyone involved in high-stakes analytics wants power, speed and flexibility regardless of the size of the data set and complexity of the analysis. Trailblazing organizations that have deployed IBM Netezza Analytics with their IBM Netezza data warehouse appliances (TwinFin) with Revolution R Enterprise are getting all three.
Revolution Analytics was the first company dedicated to the R Project. This presentation from useR! 2014 covers the history of Revolution Analytics since its founding in 2007 and its contributions to the R project and community.
Similar to Revolution R Enterprise - 100% R and More (20)
Presented to eRum (Budapest), May 2018
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe the doAzureParallel package, a backend to the "foreach" package that automates the process of spawning a cluster of virtual machines in the Azure cloud to process iterations in parallel. This will include an example of optimizing hyperparameters for a predictive model using the "caret" package.
By David Smith. Presented at Microsoft Build (Seattle), May 7 2018.
Your data scientists have created predictive models using open-source tools, proprietary software, or some combination of both, and now you are interested in lifting and shifting those models to the cloud. In this talk, I'll describe how data scientists can transition their existing workflows — while using mostly the same tools and processes — to train and deploy machine learning models based on open source frameworks to Azure. I'll provide guidance on keeping connections to data sources up-to-date, evaluating and monitoring models, and deploying applications that make use of those models.
Presentation delivered by David Smith to NY R Conference https://www.rstats.nyc/, April 2018:
Minecraft is an open-world creativity game, and a hit with kids. To get kids interested in learning to program with R, we created the "miner" package. This package is a collection of simple functions that allow you to connect with a Minecraft instance, manipulate the world within by creating blocks and controlling the player, and to detect events within the world and react accordingly.
The miner package is intended mainly for kids, to inspire them to learn R while playing Minecraft. But the development of the package also provides some useful insights into how to build an R package to interface with a persistent API, and how to instruct others on its use. In this talk I'll describe how to set up your own Minecraft server, and how to use and extend the package. I'll also provide a few examples of the package in action in a live Minecraft session.
While Python is a widely-used tool for AI development, in this talk I'll make the case for considering R as a platform for developing models for intelligent applications. Firstly, R provides a first-class experience working deep learning frameworks with its keras integration. Equally importantly, it provides the most comprehensive suite of statistical data analysis tools, which are extremely useful for many intelligent applications such as transfer learning. I'll give a few high-level examples in this talk, and we'll go into further detail in the accompanying interactive code lab.
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe several techniques available in R to speed up workloads like these, by running multiple iterations simultaneously, in parallel.
Many of these techniques require the use of a cluster of machines running R, and I'll provide examples of using cloud-based services to provision clusters for parallel computations. In particular, I will describe how you can use the SparklyR package to distribute data manipulations using the dplyr syntax, on a cluster of servers provisioned in the Azure cloud.
Presented by David Smith at Data Day Texas in Austin, January 27 2018.
A look at the changing perceptions of R, from the early days of the R project to today. Microsoft sponsor talk, presented by David Smith to the useR!2017 conference in Brussels, July 5 2017.
Predicting Loan Delinquency at One Million Transactions per SecondRevolution Analytics
Real-time applications of predictive models must be able to generate predictions at the rate that transactions are generated. Previously, such applications of models trained using R needed to be converted to other languages like C++ or Java to achieve the required throughput. In this talk, I’ll describe how to use the in-database R processing capabilities of Microsoft R Server to detect fraud in a SQL Server database of loan records at a rate exceeding one million transactions per second. I will also show the process of training the underlying gradient-boosted tree model on a large training set using the out-of-memory algorithms of Microsoft R.
Presented by David Smith at The Data Science Summit, Chicago, April 20 2017.
The ability to independently reproduce results is a critical issue within the scientific community today, and is equally important for collaboration and compliance in business. In this talk, I'll introduce several features available in R that help you make reproducibility a standard part of your data science workflow. The talk will include tips on working with data and files, combining code and output, and managing R's changing package ecosystem.
Presented by David Smith, R Community Lead (Microsoft), at Monktoberfest October 2016.
The value of open source isn’t just in the software itself. The communities that form around open source software provide just as much value and sometimes even more: in ongoing development, in documentation, in support, in marketing, and as a supply of ready-trained employees. Companies who build on open source tend to focus on the software, but neglect communities at their peril.
In this talk, I share some of my experiences in building community for an open-source software company, Revolution Analytics, and perspectives since the acquisition by Microsoft in 2015.
R is more than just a language. Many of the reasons why R has become such a popular tool for data science come from the ecosystem surrounding the R project. R users benefit from the many resources and packages created by the community, while commercial companies (including Microsoft) provide tools to extend and support R, and services to help people use R.
In this talk, I will give an overview of the R Ecosystem and describe how it has been a critical component of R’s success, and include several examples of Microsoft’s contributions to the ecosystem.
(Presented to EARL London, September 2016)
(Presented by David Smith at useR!2016, June 2016. Recording: https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/R-at-Microsoft )
Since the acquisition of Revolution Analytics in April 2015, Microsoft has embarked upon a project to build R technology into many Microsoft products, so that developers and data scientists can use the R language and R packages to analyze data in their data centers and in cloud environments.
In this talk I will give an overview (and a demo or two) of how R has been integrated into various Microsoft products. Microsoft data scientists are also big users of R, and I'll describe a couple of examples of R being used to analyze operational data at Microsoft. I'll also share some of my experiences in working with open source projects at Microsoft, and my thoughts on how Microsoft works with open source communities including the R Project.
Hadoop is famously scalable. Cloud Computing is famously scalable. R – the thriving and extensible open source Data Science software – not so much. But what if we seamlessly combined Hadoop, Cloud Computing, and R to create a scalable Data Science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based Web Service. Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms at scale.
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.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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:
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
3. Dec ember 14, 2011: Welc ome! Revolution Confidential
Thanks for coming.
Slides and replay available (soon) at:
http://bit.ly/rOSvwK
David Smith
VP Marketing, Revolution Analytics
Editor, Revolutions blog
http://blog.revolutionanalytics.com
Twitter: @revodavid
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4. In today’s webc as t: Revolution Confidential
About Revolution Analytics and R
What Revolution R adds to R
Resources for getting more from R
Q&A
Introducing Revolution R 4
5. What is R ? Download the White PaperConfidential
R is Hot
Revolution
bit.ly/r-is-hot
Data analysis software
A programming language
Development platform designed by and for statisticians
An environment
Huge library of algorithms for data access, data
manipulation, analysis and graphics
An open-source software project
Free, open, and active
A community
Thousands of contributors, 2 million users
Resources and help in every domain
5
6. R is exploding in popularity and
func tionality Revolution Confidential
Scholarly Activity
Google Scholar hits (’05-’09 CAGR)
R 46% “I’ve been astonished by the rate at which
R has been adopted. Four years ago,
SAS -11%
everyone in my economics department [at
SPSS -27%
the University of Chicago] was using
Stata; now, as far as I can tell, R is the
S-Plus 0% standard tool, and students learn it first.”
Stata 10%
Deputy Editor for New Products at Forbes
Package Growth
Number of R packages listed on CRAN
“A key benefit of R is that it provides near-
instant availability of new and
experimental methods created by its user
base — without waiting for the
development/release cycle of commercial
software. SAS recognizes the value of R
to our customer base…”
Product Marketing Manager SAS Institute, Inc.
2002 2004 2006 2008 2010
Source: http://r4stats.com/popularity 6
7. “ R is the mos t powerful & flexible s tatis tic al
Revolution Confidential
programming language in the world” 1
Capabilities
Sophisticated
statistical analyses
Predictive analytics
Data visualization
Applications
Real-time trading MSFT [2009-
Last 29.29
Finance 30
Risk assessment 25
Forecasting 20
Bio-technology 15
Drug development
Social networks
.. and more
1. Norman Nie, multiple interviews 7
8. From: The R Ecosystem
R Us er C ommunity bit.ly/R-ecosystem
8
10. R evolution R E nterpris e is Revolution Confidential
10
11. R P roduc tivity E nvironment (Windows )
Revolution Confidential
Script with type
ahead and code Solutions window
snippets for organizing
code and data
Sophisticated
debugging with
breakpoints , variable Objects
values etc. loaded in the
R
Environment
Packages Object
installed and details
loaded
http://www.revolutionanalytics.com/demos/revolution-productivity-environment/demo.htm
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12. Interac tive Debugging Revolution Confidential
One-click to set a breakpoint in an R script
Step in/out/over, inspect variables
Eliminate the edit -> browser -> repair cycle
12
13. P erformanc e: Multi-threaded Math Revolution Confidential
Open Revolution R
Source R Enterprise
Computation (4-core laptop) Open Source R Revolution R Speedup
Linear Algebra1
Matrix Multiply 327 sec 13.4 sec 23x
Cholesky Factorization 31.3 sec 1.8 sec 17x
Linear Discriminant Analysis 216 sec 74.6 sec 2x
General R Benchmarks2
R Benchmarks (Matrix Functions) 22 sec 3.5 sec 5x
R Benchmarks (Program Control) 5.6 sec 5.4 sec Not appreciable
1. http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php
2. http://r.research.att.com/benchmarks/
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14. T hree P aradigms for B ig Data Revolution Confidential
Standard R engine is constrained by
capacity and performance
Revolution R Enterprise offers three
methods for big data with R:
Off-line: high-performance file-based analytics
Off-line, parallel & distributed analytics
On-line, in-database analytics
Hadoop
Netezza
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15. R evolution R E nterpris e with R evoS c aleR
B ig Data S tatis tic s in R Revolution Confidential
www.revolutionanalytics.com/bigdata
Every US airline
departure and arrival,
1987-2008
File: AirlineData87to08.xdf
Rows: 123.5 million
Variables: 29
Size on disk: 13.2Gb
arrDelayLm2 <- rxLinMod(ArrDelay ~ DayOfWeek:F(CRSDepTime),cube=TRUE)
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16. E xample: Old Wives C ens us A nalys is Revolution Confidential
http://info.revolutionanalytics.com/Cen
susOldWivesWhitePaper.html
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17. R evoS c aleR – Dis tributed C omputing Revolution Confidential
Compute • Portions of the data source are
Data Node made available to each compute
Partition (RevoScaleR) node
• RevoScaleR on the master node
Compute assigns a task to each compute
Data Node node
Partition (RevoScaleR)
Master • Each compute node independently
Node processes its data, and returns its
Compute (RevoScaleR) intermediate results back to the
Data Node master node
Partition (RevoScaleR)
• master node aggregates all of the
intermediate results from each
Compute compute node and produces the
Data Node final result
Partition (RevoScaleR)
*Available now for Microsoft HPC Server
Video demo: http://bit.ly/ugQ9KR
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18. R evolution A nalytic s with Netezza A pplianc e
Revolution Confidential
More info: http://bit.ly/R-Netezza
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19. R evoC onnec tR for Hadoop Revolution Confidential
Write Map-Reduce analytics using
HBASE only R code with these R
packages:
HDFS
rhdfs - R and HDFS
R
Thrift rhbase - R and HBASE
Map or
Reduce
rmr - R and MapReduce
Task rhbase
rhdfs
Node
Revolution R More information at:
Job Client bit.ly/r-hadoop
Tracker rmr
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20. E nterpris e R eadines s :
R evolution R E nterpris e S erver Revolution Confidential
Multi-User Support
Production Applications
Integrate R analytics into Web based applications
Data Analysis and Visualization
Reporting
Dashboards
Interactive applications
Revolution R Enterprise Server with RevoDeployR
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21. Deployment with R evolution R E nterpris e Revolution Confidential
End User Desktop Business
Interactive Web
Applications Intelligence
Applications
(e.g. Excel) (e.g. Jaspersoft)
Application
Client libraries (JavaScript, Java, .NET)
Developer
HTTP/HTTPS – JSON/XML
R RevoDeployR Web Services
Programmer
Session Data/Script
Authentication Administration
Management Management
R
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22. C oming s oon: R evolution R G UI Revolution Confidential
Accessible
Powerful
Extensible
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23. T he A dvanc ed A nalytic s S tac k Revolution Confidential
Deployment / Consumption
Advanced Analytics
ETL
Data / Infrastructure
“Open Analytics Stack” White Paper: bit.ly/lC43Kw
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24. Revolution Confidential
On-Call Technical Support
Consulting
Migration | Analytics | Applications | Validation
Training
R | Revolution R | Statistical Topics
Systems Integration
BI | ERP | Databases | Cloud
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26. Why R ? Revolution Confidential
Every data analysis technique at your fingertips
Create beautiful and unique data visualizations
Get better results faster
Draw on the talents of data scientists worldwide
R is hot, and growing fast
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27. R evolution R E nterpris e Revolution Confidential
Production-Grade Statistical Analysis for the Workplace
High-performance R for multiprocessor systems
Modern Integrated Development Environment
Statistical Analysis of Terabyte-Class Data Sets
In-database R analytics with Hadoop and Netezza
Deploy R Applications via Web Services
Telephone and email technical support
Training and consulting services
100% compatible with R packages
Easy-to-Use GUI1
1 Coming Soon 27
28. F urther R eading Revolution Confidential
http://bit.ly/revo-r-pdf http://bit.ly/r-is-hot
28
29. R evolution R E nterpris e: F ree to A c ademia Revolution Confidential
Personal use
Research
Teaching
Package development
Free Academic Download
www.revolutionanalytics.com/downloads/free-academic.php
Discounted Technical Support Subscriptions Available
29
30. T hank You! Revolution Confidential
Download slides, replay (from Oct 20)
http://bit.ly/rOSvwK
Learn more about Revolution R
revolutionanalytics.com/products
Contact Revolution Analytics
http://bit.ly/hey-revo
Dec 20: Big Data Analysis Starts with R
A 30-minute executive webinar to find out how companies of all types and sizes can
integrate “R” into their “big data” analytics infrastructure strategy.
www.revolutionanalytics.com/news-events/free-webinars
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32. Revolution Confidential
The leading commercial provider of software and support for the
popular open source R statistics language.
www.revolutionanalytics.com
+1 (650) 646 9545
Twitter: @RevolutionR
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