This presentation gives the details about the sources for big data, the value of big data, what to do with big data, the platforms, the infrastructures and the architectures for big data analytics
SMi Group is bringing to London this December, a new masterclass training course entitled Big Data for Utilities - combining and creating value from transactional, geospatial and real-time domain information. Don't miss this must attend course in association with Alliander and SAP UK & Ireland
The current challenges and opportunities of big data and analytics in emergen...IBM Analytics
Big data and analytics present many possibilities for emergency management specialists and first responders. Some of these benefits include pinpointing vulnerabilities, bringing in the right resources and maximizing existing resources to pave the way to adoption. However, these opportunities are not without challenges. Emergency management experts Adam Crowe, Director, Emergency Preparedness at Virginia Commonwealth University; William Moorhead, President of All Clear Emergency Management Group; and Gary Nestler, Associate Partner and Global Leader, Emergency Management solutions at IBM discuss these challenges and opportunities in this slideshare—which is intended to help disaster management stakeholders achieve the most accurate situational awareness using analytics.
Discover analytics solutions for emergency management http://ibm.co/emergencymgmt
SMi Group is bringing to London this December, a new masterclass training course entitled Big Data for Utilities - combining and creating value from transactional, geospatial and real-time domain information. Don't miss this must attend course in association with Alliander and SAP UK & Ireland
The current challenges and opportunities of big data and analytics in emergen...IBM Analytics
Big data and analytics present many possibilities for emergency management specialists and first responders. Some of these benefits include pinpointing vulnerabilities, bringing in the right resources and maximizing existing resources to pave the way to adoption. However, these opportunities are not without challenges. Emergency management experts Adam Crowe, Director, Emergency Preparedness at Virginia Commonwealth University; William Moorhead, President of All Clear Emergency Management Group; and Gary Nestler, Associate Partner and Global Leader, Emergency Management solutions at IBM discuss these challenges and opportunities in this slideshare—which is intended to help disaster management stakeholders achieve the most accurate situational awareness using analytics.
Discover analytics solutions for emergency management http://ibm.co/emergencymgmt
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
Introduction to Modern Data Virtualization 2021 (APAC)Denodo
Watch full webinar here: https://bit.ly/2XXyc3R
“Through 2022, 60% of all organisations will implement data virtualization as one key delivery style in their data integration architecture," according to Gartner. What is data virtualization and why is its adoption growing so quickly? Modern data virtualization accelerates that time to insights and data services without copying or moving data.
Watch on-demand this webinar to learn:
- Why organizations across the world are adopting data virtualization
- What is modern data virtualization
- How data virtualization works and how it compares to alternative approaches to data integration and management
- How modern data virtualization can significantly increase agility while reducing costs
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
This report helps the user to understand trends in big data, cloud and medical devices, the key players in the ecosystem , the top users of this technology
The Role of Community-Driven Data Curation for EnterprisesEdward Curry
With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of data and its fitness for use. Traditional approaches to curation are struggling with increased data volumes, and near real-time demands for curated data. In response, curation teams have turned to community crowd-sourcing and semi-automatedmetadata tools for assistance. This chapter provides an overview of data curation, discusses the business motivations for curating data and investigates the role of community-based data curation, focusing on internal communities and pre-competitive data collaborations. The chapter is supported by case studies from Wikipedia, The New York Times, Thomson Reuters, Protein Data Bank and ChemSpider upon which best practices for both social and technical aspects of community-driven data curation are described.
E. Curry, A. Freitas, and S. O’Riáin, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25-47.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
What are big data in the contacts of energy & utilities, and how/where can the utilities find value in the data. In this C-level presentation we discussed the three prime areas: grid operations, smart metering and asset & workforce management. A section on cognitive computing for utilities have been omitted from the presentation due to confidentiality - but I tell you - it is mind-blowing perspectives on how IBM Watson will help utilities plan and optimize their operations in the near future!
See more on http://www.ibmbigdatahub.com/industry/energy-utilities
Data Democratization for Faster Decision-making and Business Agility (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3ogsO7F
Presented at 3rd Chief Digital Officer Asia Summit
The idea behind Data democratization is to enable every type of user in a company to have access to data and to ensure that there is no dependency on any single party that might create a bottleneck to data access. But this is easier said than done especially given the complex data management landscape that most organizations have today. Data virtualization is a modern data integration technique that not only delivers data in real time without replication but also simplifies data discovery, data exploration and navigating between related data sets.
In this on-demand session, you will understand how data virtualization enables enterprises to:
- Reduce up to 80% the time required to deliver data to the business adapted to the needs of each user
- Apply consistent security and governance policies across the self-service data delivery process
- Seamlessly implement the concept of 'Data Marketplace'
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
Introduction to Modern Data Virtualization 2021 (APAC)Denodo
Watch full webinar here: https://bit.ly/2XXyc3R
“Through 2022, 60% of all organisations will implement data virtualization as one key delivery style in their data integration architecture," according to Gartner. What is data virtualization and why is its adoption growing so quickly? Modern data virtualization accelerates that time to insights and data services without copying or moving data.
Watch on-demand this webinar to learn:
- Why organizations across the world are adopting data virtualization
- What is modern data virtualization
- How data virtualization works and how it compares to alternative approaches to data integration and management
- How modern data virtualization can significantly increase agility while reducing costs
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
This report helps the user to understand trends in big data, cloud and medical devices, the key players in the ecosystem , the top users of this technology
The Role of Community-Driven Data Curation for EnterprisesEdward Curry
With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of data and its fitness for use. Traditional approaches to curation are struggling with increased data volumes, and near real-time demands for curated data. In response, curation teams have turned to community crowd-sourcing and semi-automatedmetadata tools for assistance. This chapter provides an overview of data curation, discusses the business motivations for curating data and investigates the role of community-based data curation, focusing on internal communities and pre-competitive data collaborations. The chapter is supported by case studies from Wikipedia, The New York Times, Thomson Reuters, Protein Data Bank and ChemSpider upon which best practices for both social and technical aspects of community-driven data curation are described.
E. Curry, A. Freitas, and S. O’Riáin, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25-47.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
What are big data in the contacts of energy & utilities, and how/where can the utilities find value in the data. In this C-level presentation we discussed the three prime areas: grid operations, smart metering and asset & workforce management. A section on cognitive computing for utilities have been omitted from the presentation due to confidentiality - but I tell you - it is mind-blowing perspectives on how IBM Watson will help utilities plan and optimize their operations in the near future!
See more on http://www.ibmbigdatahub.com/industry/energy-utilities
Data Democratization for Faster Decision-making and Business Agility (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3ogsO7F
Presented at 3rd Chief Digital Officer Asia Summit
The idea behind Data democratization is to enable every type of user in a company to have access to data and to ensure that there is no dependency on any single party that might create a bottleneck to data access. But this is easier said than done especially given the complex data management landscape that most organizations have today. Data virtualization is a modern data integration technique that not only delivers data in real time without replication but also simplifies data discovery, data exploration and navigating between related data sets.
In this on-demand session, you will understand how data virtualization enables enterprises to:
- Reduce up to 80% the time required to deliver data to the business adapted to the needs of each user
- Apply consistent security and governance policies across the self-service data delivery process
- Seamlessly implement the concept of 'Data Marketplace'
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan
In the age of Big Data and large volume analytics there is a lot to cover and a lot to learn. While at Microsoft developing Windows HDInsight and now developing a one of kind Big Data product at my own company Big Data Perspective, San Francisco I have lived last several years covering Big Data at various level. This talk is customized for database and business intelligence (BI) professionals, programmers, Hadoop administrators, researchers, technical architects, operations engineers, data analysts, and data scientists understand the core concepts of Big Data Analytics on Hadoop. This webinar will be useful for those, who wants to know what is Hadoop, and how they can take advantage just by spending few dollars to run the cluster. The webinar is great for those who are looking to deploy their first data cluster and run MapReduce jobs to discover insights.
Memo for the Danish Emergency Management Agency by student Anna Boye Koldaas, Master of Science (MSc)-student in Security Risk Management at Copenhagen University.
Klarity is a robust and comprehensive dashboard-styled platform providing metrics and analytics extracted from social media "big data" for marketers and entreprises. The proprietary engine developed by Social Media Broadcasts crawls influencer networks such as Facebook, Google+, Instagram, Pinterest, Twitter, Youtube, Sina Weibo and Tencent Weibo, collecting granular data and translating the information into meaningful insights allowing users to monitor social activity, measure performance and gather social intelligence.
Product Placement: The Present & The Futureitandlaw
It has been over three years since the first piece of legislation permitting product placement in the UK was introduced. Restrictions on product placement in on-demand programme services, were relaxed in December 2009. After much debate, the door for product placement in television programmes was finally opened in April 2010. However, since then there has not been a rush of product placement deals. The media industry predicts the year 2013 is when product placement would become an established means of advertising.
#PolíticosViolentos, un análisis de la agresión en el discurso de Cristina Ki...Santiago Castelo
Presentación utilizada en el Coloquio "Être leader en Amérique(s) et en Europe", organizado por la Asociación Analyse des Discours de l'Amerique Latine (ADAL). París, 21 de noviembre de 2014.
Rethink Your 2021 Data Management Strategy with Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2O2r3NP
In the last several decades, BI has evolved from large, monolithic implementations controlled by IT to orchestrated sets of smaller, more agile capabilities that include visual-based data discovery and governance. These new capabilities provide more democratic analytics accessibility that is increasingly being controlled by business users. However, given the rapid advancements in emerging technologies such as cloud and big data systems and the fast changing business requirements, creating a future-proof data management strategy is an incredibly complex task.
Catch this on demand session to understand:
- BI program modernization challenges
- What is data virtualization and why is its adoption growing so quickly?
- How data virtualization works and how it compares to alternative approaches to data integration
- How modern data virtualization can significantly increase agility while reducing costs
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
This content was presented during the Smart Data Summit Dubai 2015 in the UAE on May 25, 2015, by Jesus Barrasa, Senior Solutions Architect at Denodo Technologies.
In the era of Big Data, IoT, Cloud and Social Media, Information Architects are forced to rethink how to tackle data management and integration in the enterprise. Traditional approaches based on data replication and rigid information models lack the flexibility to deal with this new hybrid reality. New data sources and an increasing variety of consuming applications, like mobile apps and SaaS, add more complexity to the problem of delivering the right data, in the right format, and at the right time to the business. Data Virtualization emerges in this new scenario as the key enabler of agile, maintainable and future-proof data architectures.
Is Your Organization Ready to Embrace a Digital Twin?Cognizant
Before industrial organizations invest in technologies for creating data-driven product design strategies, they need to reassess their operational maturity and technology readiness to compete in a world where the virtual and physical seamlessly fuse as digital twins.
Modernize your Infrastructure and Mobilize Your DataPrecisely
Modernizing your infrastructure can get complicated really fast. The keys to success involve breaking down data silos and moving data to the cloud in real time. But building data pipelines to mobilize your data in the cloud can be time consuming. You need solutions that decrease bandwidth, ensure data consistency, and enable data migration and replication in real-time; solutions that help you build data pipelines in hours, not days.
Watch this on-demand webinar to learn about the trends and pitfalls related to modernizing your infrastructure to cloud, how the pace of on-prem data growth demands accelerating data streaming to analytics platforms, and why mobilizing your data for the cloud improves business outcomes.
Nurturing Digital Twins: How to Build Virtual Instances of Physical Assets to...Cognizant
To embark on the digital twin jounrey, assess your readiness, define and communicate a vision, set common data management rules and build in flexibility for intelligence.
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Data and Application Modernization in the Age of the Cloudredmondpulver
Data modernization is key to unlocking the full potential of your IT investments, both on premises and in the cloud. Enterprises and organizations of all sizes rely on their data to power advanced analytics, machine learning, and artificial intelligence.
Yet the path to modernizing legacy data systems for the cloud is full of pitfalls that cost time, money, and resources. These issues include high hardware and staffing costs, difficulty moving data and analytical processes to cloud environments, and inadequate support for real-time use cases. These issues delay delivery timelines and increase costs, impacting the return on investment for new, cutting-edge applications.
Watch this webinar in which James Kobielus, TDWI senior research director for data management, explores how enterprises are modernizing their mainframe data and application infrastructures in the cloud to sustain innovation and drive efficiencies. Kobielus will engage John de Saint Phalle, senior product manager at Precisely, in a discussion that addresses the following key questions:
When should enterprises consider migrating and replicating all their data assets to modern public clouds vs. retaining some on-premises in hybrid deployments?How should enterprises modernize their legacy data and application infrastructures to unlock innovation and value in the age of cloud computing?What are the key investments that enterprises should make to modernize their data pipelines to deliver better AI/ML applications in the cloud?What is the optimal data engineering workflow for building, testing, and operationalizing high-quality modern AI/ML applications in the cloud?What value does real-time replication play in migrating data and applications to modern cloud data architectures?What challenges do enterprises face in ensuring and maintaining the integrity, fitness, and quality of the data that they migrate to modern clouds?What tools and methodologies should enterprise application developers use to refactor and transform legacy data applications that have migrated to modern clouds
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Real life use cases from across Europe (Walid Aoudi - Cognizant)
This presentation will present some Cognizant Big Data clients return on experiences on continental Europe and UK. The main focus will be centered on use cases through the presentation of the business drivers behind these projects. Key highlights around the big data architecture and approach solutions will be presented. Finally, the business outcomes in terms of ROI provided by the solutions implementations will be discussed.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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!
Knowledge engineering: from people to machines and back
A technical Introduction to Big Data Analytics
1. A Technical Introduction to Big Data Analytics
Pethuru Raj PhD
Infrastructure Architect
IBM Global Cloud Center of Excellence (CoE)
IBM India, Bangalore
E-mail: peterindia@gmail.com
4. The Classification of the IT Trends
• The Technology Space - There is a cornucopia of technologies (Computing, Connectivity,
Miniaturization, Middleware, Sensing, Actuation, Perception, Analyses, Knowledge Engineering, etc.)
• The Process Space – With new kinds of services, applications, data, infrastructures, and devices
joining into the mainstream IT, fresh process consolidation, orchestration, governance and
management mechanisms are emerging. That is, process excellence is the ultimate aim
• Infrastructure Space – Infrastructure consolidation, convergence, centralization, federation,
automation and sharing methods clearly indicate the infrastructure trends in the computing and
communication disciplines. Physical infrastructures turn to be virtual infrastructures. Two major
infrastructural types are
• System Infrastructure (Compute, Storage, & Network)
• Application Infrastructure – Integration Backbones, Platforms (Design, Development, Deployment,
Delivery, Management, etc.), Messaging Middleware, Databases (SQL and NoSQL), etc.
• Architecture Space – Service oriented architecture (SOA), event-driven architecture (EDA), model-
driven architecture (MDA), resource oriented architecture (ROA) and so on are the leading
architectural patterns
• The Device Space is fast evolving (Slim & Sleek, handy & trendy, mobile, wearable, implantable,
portable, etc.). Everyday machines are tied up with one another as well as to the remote Web / Cloud
• Data Space – Data are being produced in an automated and massive manner
5. The TectonicTrendsTowards the Ensuing Knowledge Era
1. Data is being positioned as the strategic asset for any organization
2. Analytics has been an important ingredient for worldwide business enterprises
to
Strategize and Plan Ahead
Take Informed Decisions
Proceed with Confidence and Clarity (Insights-driven Enterprises)
With the arrival of newer technologies, the capabilities and competencies of
Analytics have been consistently on the climb.
In sync up with big data, platforms and infrastructures, big insights will become the
norm for worldwide organizations
6. For any Strategic and SustainableTransformation
Leverage Data Assets Insightfully
Optimize InfrastructureTechnologically
Innovate Processes Consistently
Assimilate Architectures Appropriately
ChooseTechnologies Carefully
Ensure Accessibility, Simplicity & Consumability Cognitively
11. The Deeper and Broader Integration pours out Big Data
• Device to Device (D2D) Integration
• Device to Enterprise (D2E) Integration - In order to have remote and real-time
monitoring, management, repair, and maintenance, and for enabling decision-
support and expert systems, ground-level heterogeneous devices have to be
synchronized with control-level enterprise packages such as ERP, SCM, CRM,
KM etc.
• Device to Cloud (D2C) Integration - As most of the enterprise systems are
moving to clouds, device to cloud (D2C) connectivity is gaining importance.
• Cloud to Cloud (C2C) Integration – Disparate, distributed and decentralised
clouds are getting connected to provide better prospects
15. The Unequivocal Result : the Data-drivenWorld
BusinessTransactions, Interactions, Operations, and Analytical data
System Infrastructure Log files
Social & People data
Customer, Product, Sales and other business data
Machine and Sensor Data
Scientific Experimentation & Observation Data (Genetics, Particle
Physics, Climate modeling, Drug Discovery, etc.,)
16. Why Big Data is Strategically Significant for Businesses?
17. Big Data brings in
Enhanced Business Value through better performance and productivity
Bigger and Bigger Insights through a host of newer Analytics and Use Cases
22. Big Data Big Insights
Aggregate all kinds of distributed, different and decentralized data
Analyze the formatted and formalized data
Articulate the extracted actionable intelligence
Act based on the insights delivered and raise the bar for futuristic analytics
(Real-time, predictive, prescriptive and personal analytics)
Accentuate business performance and productivity
24. The Drivers for Big Data Analysis
1. There is an Exponential Growth in Data Generation due to
◦ The continued increase in diverse and distributed data sources
2. The Maturity,Stability and Convergence ofTechnologies - DataVirtualization, Management,
Storage,Transmission,Analysis andVisualizationTechniques,Tips, andTools
3. The Massive Adoption and Adaption of Cloud Infrastructures (Compute, Storage and Network)
4. The Realization of more comprehensive, accurate, and speedier Knowledge Discovery and
Dissemination Platforms and Processes
5. Enhanced BusinessValue
6. NewerTypes of Analytics
◦ Domain-specific Analytics (Customer Sentiment, Social, Security, Retail, Fraud Detection
Analysis, etc.) and
◦ Generic Analytics(Predictive, Prescriptive, High-Performance, Real-time, Smarter
Analytics, etc.)
33. Machine Data Analytics - Use Cases
Here are a few ROI examples from a 1% improvement in productivity across different industries:
Commercial aviation industry — a 1% improvement in fuel savings would yield a savings of $30
billion over 15 years.
Utilities — In global gas-fired power plant fleet a 1% improvement could yield a $66 billion savings
in fuel consumption.
Global health care industry — A 1% efficiency gain from reduction of process inefficiencies
globally could yield more than $63 billion in health care savings.
Railway Networks — Freight moved across the world rail networks, if improved by 1% could yield
another gain of $27 billion in fuel savings.
Upstream Oil and Gas Exploration – a 1% improvement in capital utilization upstream oil and
gas exploration and development could total $90 billion in avoided or deferred capital expenditures.
The convergence of intelligent devices, intelligent networks and intelligent decisioning (Insight vs. Hindsight
analytics) is definitely paving the foundation for the next growth spurt or productivity gains.
44. Big Data Analytics:The Platforms
Analytical, Distributed, Scalable and Parallel Databases
Data warehouses, Data Marts, etc.
In-Memory Systems (SAP HANA, etc.)
In-Database Systems (SAS, etc.)
Distributed File Systems (HDFS)
Hadoop Implementations (Cloudera, Map R, HortonWorks,Apache
Hadoop, DataStax, etc.)
NoSQL & Hybrid Databases
45. Parallel DBMS
Standard relational tables and SQL
◦ Indexing, compression,caching, I/O sharing
◦ Tables partitioned over nodes
◦ Transparent to the user
Meet performance
◦ Needed highly skilled DBA
Flexible query interfaces
◦ UDFs varies accros implementations
Fault tolerance
◦ Not score so well
Assumption: failures are rare
Assumption: dozens of nodes in clusters
45
46. MapReduce Programming Model & Hadoop Platforms
MapReduce is a programming model which specifies:
◦ A map function that processes a key/value pair to generate a set of intermediate key/value pairs,
◦ A reduce function that merges all intermediate values associated with the same intermediate key.
Hadoop comprises large-scale, distributed, elastic, and fault-tolerant data processing and storage
modules
◦ Is a MapReduce implementation for processing large data sets over 1000s of nodes.
◦ Maps and Reduces run independently of each other over blocks of data distributed across a
cluster
46
51. Why Hadoop?
Better application development productivity through a more flexible data model;
Greater ability to scale dynamically to support more users and data;
Improved performance to satisfy expectations of users wanting highly responsive
applications and to allow more complex processing of data.
Scalability to large data volumes:
◦ Scan 100 TB on 1 node @ 50 MB/sec = 23 days
◦ Scan on 1000-node cluster = 33 minutes
Divide-And-Conquer (i.e., data partitioning)
Cost-efficiency
◦ Commodity nodes (cheap, but unreliable)
◦ Commodity network
◦ Automatic fault-tolerance (fewer administrators)
◦ Easy to use (fewer programmers)
Satisfies fault tolerance
Works on heterogeneous environment
52. NoSQL Databases
NoSQL encompasses a wide variety of different database technologies and were developed in response
to a rise in the volume of data stored about users, objects and products, the frequency in which this data
is accessed, and performance and processing needs.
Document databases pair each key with a complex data structure known as a document.Documents
can contain many different key-value pairs, or key-array pairs, or even nested documents.
Graph stores are used to store information about networks, such as social connections.Graph stores
include Neo4J and HyperGraphDB.
Key-value stores are the simplest NoSQL databases. Every single item in the database is stored as an
attribute name (or "key"), together with its value. Examples of key-value stores are Riak andVoldemort.
Some key-value stores, such as Redis, allow each value to have a type, such as "integer", which adds
functionality.
Wide-column stores such as Cassandra and HBase are optimized for queries over large datasets, and
store columns of data together, instead of rows.
Cassandra (Facebook) (CQL is the query language)
BigTable (Google)
Dynomo (Amazon)
RIAK (SoftLayer) (Apache Lucene)
MongoDB
CouchDB (UNQL is the query language0
53. RelationalVs. NoSQL Databases
SQL Databases NoSQL Databases
The relational model takes data and separates it into
many interrelated tables. Tables reference each other
through foreign keys
The relational model minimizes the amount of storage
space required, because each piece of data is only
stored in one place. However, space efficiency comes at
expense of increased complexity when looking up data.
The desired information needs to be collected from
many tables (often hundreds in today’s enterprise
applications) and combined before it can be provided to
the application. When writing data, the write needs to be
coordinated and performed on many tables.
Developers generally use object-oriented programming
languages to build applications. It’s usually most efficient
to work with data that’s in the form of an object with a
complex structure consisting of nested data, lists, arrays,
etc. The relational data model provides a very limited
data structure that doesn’t map well to the object model.
Instead data must be stored and retrieved from tens or
even hundreds of interrelated tables. Object-relational
frameworks provide some relief but the fundamental
impedance mismatch still exists between the way an
application would like to see its data and the way it’s
actually stored in a relational database
NoSQL databases have a very different model. For
example, a document-oriented NoSQL database takes
the data you want to store and aggregates it into
documents using the JSON format. Each JSON document
can be thought of as an object to be used by your
application. A JSON document might, for example, take
all the data stored in a row that spans 20 tables of a
relational database and aggregate it into a single
document/object.
Aggregating this information may lead to duplication of
information, but since storage is no longer cost
prohibitive, the resulting data model flexibility, ease of
efficiently distributing the resulting documents and read
and write performance improvements make it an easy
trade-off for web-based applications.
Document databases, on the other hand, can store an
entire object in a single JSON document and support
complex data structures. This makes it easier to
conceptualize data as well as write, debug, and evolve
applications, often with fewer lines of code
54. RelationalVs. NoSQL Databases
SQL Databases NoSQL Databases
Relational technology requires strict definition of a
schema prior to storing any data into a database.
Changing the schema once data is inserted is a big deal.
Want to start capturing new information not previously
considered? Want to make rapid changes to application
behavior requiring changes to data formats and content?
With relational technology, changes like these are
extremely disruptive and frequently avoided
RDBMS supports scale-up implying the fundamentally
centralized, shared-everything architecture of relational
database technology
Enhancement Techniques include
1. Sharding
2. Denormalizing,
3. Distributed caching
NoSQL databases especially document databases are
typically schemaless, allowing you to freely add fields to
JSON documents without having to first define changes.
The format of the data being inserted can be changed at
any time, without application disruption. This allows
application developers to move quickly to incorporate
new data into their applications.
NoSQL use a cluster of standard, physical or virtual
servers to store data and support database operations.
Support the following
Auto-sharding
Data Replication
Distributed query support – “Sharding” a relational
database can reduce, or eliminate in certain cases, the
ability to perform complex data queries. NoSQL database
systems retain their full query expressive power even
when distributed across hundreds of servers.
Integrated caching – Transparently cache data in system
memory. This behavior is transparent to the application
developer and the operations team, compared to
relational technology where a caching tier is usually a
separate infrastructure tier that must be developed to,
deployed on separate servers, and explicitly managed by
the ops team.
58. Big Data Analytics – The Emerging Infrastructures
Analytic, Scalable, Parallel and Distributed Databases & DataWarehouses -
Hardware Appliances (MPP and SMP)
In-Memory Compute Infrastructures (SAP HANA on IBM Power 7)
In-Database Compute Infrastructures (SAS Teradata, etc.)
Expertly Integrated Systems (IBM PureData System for Hadoop,Analytics,
etc.)
Clouds (public, private and hybrid) comprising bare metal servers and
virtual machines (VMs)
59. In-Memory Data Grid (IMDG)
An IMDG is a distributed non-relational data or object store. It can be distributed to
span more than one server.
Reading from memory is more than 3,300 times faster than reading from disk.A
simple calculation would suggest that if it takes an hour to read a set of information
from disk, it would take just over a second to read it from memory
This approach brings data to the cloud, where the application can interact with it,
and the application is completely shielded from the complexity of having to persist
or replicate data back to the on-premise store.
The use of an IMDG also means that while the data is available on the cloud, it is
only available in memory and is never stored on a disk in the cloud.
IMDGs usually support linear scaling to support high loads, data partitioning,
redundancy, and automatic data recovery in case of failures.
65. Why Big Data Analytics in Clouds?
Agility & Affordability - No capital investment of a large size of Infrastructures. Just Use
and Pay
Hadoop Platforms in Clouds - Deploying and using any Hadoop Platforms (generic or
specific, open or commercial-grade, etc.) are fast
NoSQL Databases in Clouds - NoSQL databases are made available in Clouds
WAN OptimizationTechnologies - There areWAN optimization products and
platforms for efficiently transmitting data over the Internet infrastructure
Business Applications in Clouds - With enterprise information systems (EISs), high-
performance computing systems, and the establishment of data storage, social, device and
sensor clouds go up in public clouds, big data analytics at remote, Internet-scale clouds
makes sense.
Cloud Integrators, Brokers & Orchestrators –There are products and platforms for
seamless interoperability among different and distributed systems, services and data
66. Entering into the HybridWorld
1. TheTraditional Analytical Systems (Data Warehouse)Vs.The
Big Data Analytical systems (Hadoop)
2. TheTraditional Databases (RDBMS)Vs.The NoSQL
Databases
3. The Scalable, Distributed, Parallel RDBMSVs.The NoSQL
Databases
72. Big Data Analytics: the Summary
Digitalization, service-enablement, extreme connectivity, distribution,
commoditization, Consumerization, Industrialization, etc. are the
brewing trends towards big data
DataVolume,Variety,Velocity andVariability are on the Rise signalling
a heightened DataValue.This development is due to the diversity
and multiplicity of data sources.
Data Capturing,transmission, Cleansing, Filtering, Formatting, and
StorageTasks,Tools, andTechnologies are maturing fast
Big Data platforms, patterns, practices, products, processes and
infrastructures are being developed to streamline big data analytics