Using technology intelligence tools, companies can cut the time spent on research and development from weeks or months to seconds or minutes. Technology intelligence refers to identifying technological opportunities and threats that could impact a company's future growth. These tools provide contextual access to relevant information and insights by combining web content, scientific journals, and patents with search technology and analysis. For example, a company could search for ways to reduce energy consumption and the tool would return a summary of solutions from various categories, such as approaches from the EPA and Department of Energy, in under a minute. This represents a shift from traditional research methods to quickly gaining actionable intelligence through intuitive searches.
Enterprise Search White Paper: Beyond the Enterprise Data Warehouse - The Eme...Findwise
This white paper elaborates the role of the enterprise search technology as an intelligent retrieval platform for structured data, a role traditionally held by the Relational Database Management Systems (RDBMS). Furthermore it investigates the great possibility by enterprise search solutions to derive insights and patterns by also analyzing the unstructured data, which is not possible to do with traditional data warehouse systems based on RDBMS.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Big Data 101 - Creating Real Value from the Data Lifecycle - Happiest Mindshappiestmindstech
The big impact of Big Data in the post-modern world is
unquestionable, un-ignorable and unstoppable today.
While there are certain discussions around Big Data being
really big, here to stay or just an over hyped fad; there are
facts as shared in the following sections of this whitepaper
that validate one thing - there is no knowing of the limits
and dimensions that data in the digital world can assume.
Defining a Practical Path to Artificial Intelligence Roman Chanclor
With the evolution of purpose built AI Infrastructures and the advancement of Graphics Processing Units (GPUs) that enable massively parallel, deep analysis in real-time; cognitive computing may be the norm in data centers in record time. But how?
Enterprise Search White Paper: Beyond the Enterprise Data Warehouse - The Eme...Findwise
This white paper elaborates the role of the enterprise search technology as an intelligent retrieval platform for structured data, a role traditionally held by the Relational Database Management Systems (RDBMS). Furthermore it investigates the great possibility by enterprise search solutions to derive insights and patterns by also analyzing the unstructured data, which is not possible to do with traditional data warehouse systems based on RDBMS.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Big Data 101 - Creating Real Value from the Data Lifecycle - Happiest Mindshappiestmindstech
The big impact of Big Data in the post-modern world is
unquestionable, un-ignorable and unstoppable today.
While there are certain discussions around Big Data being
really big, here to stay or just an over hyped fad; there are
facts as shared in the following sections of this whitepaper
that validate one thing - there is no knowing of the limits
and dimensions that data in the digital world can assume.
Defining a Practical Path to Artificial Intelligence Roman Chanclor
With the evolution of purpose built AI Infrastructures and the advancement of Graphics Processing Units (GPUs) that enable massively parallel, deep analysis in real-time; cognitive computing may be the norm in data centers in record time. But how?
Make compliance fulfillment count doubleDirk Ortloff
This whitepaper gives an overview about the requirements and the approaches to
make your compliance initiative count double. Not only to fulfill compliance but to go
the next step bringing your documentation and knowledge handling to a stage where
future projects can learn from previous successes and mistakes. This will make your
R&D department ready for future challenges, faster markets and global
partnerships.
HPE IDOL 10 (Intelligent Data Operating Layer)Andrey Karpov
Understand virtually all of your information with high-performance analytics: Over 500 analytical functions available for text, audio, video, and image
• Derive actionable insights: Process data in near real time to gain a competitive edge
• Maximize your information reach: Connect to over 400 systems with support for over 1000 file formats, so you can find all relevant information
• Let social media work for you: Detect emerging trends and influencers in this powerful media with sophisticated sentiment analysis and clustering technology
Why Big Data Analytics Needs Business Intelligence Too Barry Devlin
Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.
In this April 2014 BrightTALK webinar, Dr. Barry Devlin describes the thinking, architecture, tools and methods needed to achieve a new joined-up, comprehensive data environment.
A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
Business unIntelligence - a Whistle Stop TourBarry Devlin
The old world of business intelligence is being transformed into a new biz-tech ecosystem. Analytics is forcing the recombination of operational and informational systems in a consistent and coherent IT environment for all business activities. Big data—despite the hype—introduces two very different types of information that transform how business processes interact with the external world. Together, these directions are driving a new BI, so different to its prior form that I call it “Business unIntelligence”. This session covers:
- Business drivers and results of the biz-tech ecosystem
- Modern conceptual and logical architectures for information, process and people
- Positioning of all forms of business analytic and big data
Scaling the mirrorworld with knowledge graphsAlan Morrison
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
This takes a look at the architectural constructs that are used for building business intelligence systems and how they are used in business processes to improve marketing, better serve customers, and maximize organizational efficiency.
TechWise with Eric Kavanagh, Dr. Robin Bloor and Dr. Kirk Borne
Live Webcast on July 23, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=59d50a520542ee7ed00a0c38e8319b54
Analytical applications are everywhere these days, and for good reason. Organizations large and small are using analytics to better understand any aspect of their business: customers, processes, behaviors, even competitors. There are several critical success factors for using analytics effectively: 1) know which kind of apps make sense for your company; 2) figure out which data sets you can use, both internal and external; 3) determine optimal roles and responsibilities for your team; 4) identify where you need help, either by hiring new employees or using consultants 5) manage your program effectively over time.
Register for this episode of TechWise to learn from two of the most experienced analysts in the business: Dr. Robin Bloor, Chief Analyst of The Bloor Group, and Dr. Kirk Borne, Data Scientist, George Mason University. Each will provide their perspective on how companies can address each of the key success factors in building, refining and using analytics to improve their business. There will then be an extensive Q&A session in which attendees can ask detailed questions of our experts and get answers in real time. Registrants will also receive a consolidated deck of slides, not just from the main presenters, but also from a variety of software vendors who provide targeted solutions.
Visit InsideAnlaysis.com for more information.
Mission Critical Use Cases Show How Analytics Architectures Usher in an Artif...Dana Gardner
A discussion on how artificial intelligence and advanced analytics solutions coalesce into top competitive differentiators that prove indispensable for digital business transformation.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Analytics 3.0.pdfArtwork Chad Hagen, Nonsensical Infographic .docxSHIVA101531
Analytics 3.0.pdf
Artwork: Chad Hagen, Nonsensical Infographic No. 5, 2009, digital
Those of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case.
Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.
I’ll develop this argument in what follows, making the case that just as the early applications of big data marked a major break from the 1.0 past, the current innovations of a few industry leaders are evidence that a new era is dawning. When a new way of thinking about and applying a strength begins to take hold, managers are challenged to respond in many ways. Change comes fast to every part of a business’s world. New players emerge, competitive positions shift, novel technologies must be mastered, and talent gravitates toward the most exciting new work.
Managers will see all these things in the coming months and years. The ones who respond most effectively will be those who have connected the dots and recognized that competing on analytics is being rethought on a large scale. Indeed, the first companies to perceive the general direction of change—those with a sneak peek at Analytics 3.0—will be best positioned to drive that change.
The Evolution of Analytics
My purpose here is not to make abstract observations about the unfolding history of analytics. Still, it is useful to look back at the last big shift and the context in which it occurred. The use of data to make decisions is, of course, not a new idea; it is as old as decision making itself. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could.
Today it isn’t just online and information firms that can create products and services from analyses of data. It’s every firm in every industry.
Analytics 1.0—the era of “business intelligence.”
What we are here calling Analytics 1.0 was a time of real progress in gaining an objective, deep understanding of important business phenomena and giving managers.
Enterprise Search White Paper: Increase Your Competitiveness - Make a Knowled...Findwise
With data volumes growing by 200 percent a year, knowledge workers are spending around 30 percent of their time trying to extract useful information. Furthermore a recent U.S. study asserted that knowledge workers spend more than twice as much time re-creating already created content as they spend creating new content. In addition to this time spent on maintaining structures for storing incoming unstructured information (e.g. mail, documents etc) is increasing rapidly.
Enabling search solutions makes information easy to find, however the key is to transform this information into knowledge. This is normally not done by simple intranet search functionality, however the intranet portal can act as a portal to a knowledge management system based on advanced search functionality withadded collaborative functions. This transforms your organization into a “knowledge finding organization”, creating an even more competitive organization.
Knowledge Management systems based on an Enterprise Search Platform (ESP) can, if implemented properly, significantly improve the efficiency of an organization. IDC Research suggests in their latest report (April 2006) “Hidden cost of information Work” that the cost for wasted time on the part of professional searching, but not finding relevant information, amounts to $5.3 million annually for an enterprise with 1000 knowledge workers.
Make compliance fulfillment count doubleDirk Ortloff
This whitepaper gives an overview about the requirements and the approaches to
make your compliance initiative count double. Not only to fulfill compliance but to go
the next step bringing your documentation and knowledge handling to a stage where
future projects can learn from previous successes and mistakes. This will make your
R&D department ready for future challenges, faster markets and global
partnerships.
HPE IDOL 10 (Intelligent Data Operating Layer)Andrey Karpov
Understand virtually all of your information with high-performance analytics: Over 500 analytical functions available for text, audio, video, and image
• Derive actionable insights: Process data in near real time to gain a competitive edge
• Maximize your information reach: Connect to over 400 systems with support for over 1000 file formats, so you can find all relevant information
• Let social media work for you: Detect emerging trends and influencers in this powerful media with sophisticated sentiment analysis and clustering technology
Why Big Data Analytics Needs Business Intelligence Too Barry Devlin
Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.
In this April 2014 BrightTALK webinar, Dr. Barry Devlin describes the thinking, architecture, tools and methods needed to achieve a new joined-up, comprehensive data environment.
A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
Business unIntelligence - a Whistle Stop TourBarry Devlin
The old world of business intelligence is being transformed into a new biz-tech ecosystem. Analytics is forcing the recombination of operational and informational systems in a consistent and coherent IT environment for all business activities. Big data—despite the hype—introduces two very different types of information that transform how business processes interact with the external world. Together, these directions are driving a new BI, so different to its prior form that I call it “Business unIntelligence”. This session covers:
- Business drivers and results of the biz-tech ecosystem
- Modern conceptual and logical architectures for information, process and people
- Positioning of all forms of business analytic and big data
Scaling the mirrorworld with knowledge graphsAlan Morrison
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
This takes a look at the architectural constructs that are used for building business intelligence systems and how they are used in business processes to improve marketing, better serve customers, and maximize organizational efficiency.
TechWise with Eric Kavanagh, Dr. Robin Bloor and Dr. Kirk Borne
Live Webcast on July 23, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=59d50a520542ee7ed00a0c38e8319b54
Analytical applications are everywhere these days, and for good reason. Organizations large and small are using analytics to better understand any aspect of their business: customers, processes, behaviors, even competitors. There are several critical success factors for using analytics effectively: 1) know which kind of apps make sense for your company; 2) figure out which data sets you can use, both internal and external; 3) determine optimal roles and responsibilities for your team; 4) identify where you need help, either by hiring new employees or using consultants 5) manage your program effectively over time.
Register for this episode of TechWise to learn from two of the most experienced analysts in the business: Dr. Robin Bloor, Chief Analyst of The Bloor Group, and Dr. Kirk Borne, Data Scientist, George Mason University. Each will provide their perspective on how companies can address each of the key success factors in building, refining and using analytics to improve their business. There will then be an extensive Q&A session in which attendees can ask detailed questions of our experts and get answers in real time. Registrants will also receive a consolidated deck of slides, not just from the main presenters, but also from a variety of software vendors who provide targeted solutions.
Visit InsideAnlaysis.com for more information.
Mission Critical Use Cases Show How Analytics Architectures Usher in an Artif...Dana Gardner
A discussion on how artificial intelligence and advanced analytics solutions coalesce into top competitive differentiators that prove indispensable for digital business transformation.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Analytics 3.0.pdfArtwork Chad Hagen, Nonsensical Infographic .docxSHIVA101531
Analytics 3.0.pdf
Artwork: Chad Hagen, Nonsensical Infographic No. 5, 2009, digital
Those of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case.
Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.
I’ll develop this argument in what follows, making the case that just as the early applications of big data marked a major break from the 1.0 past, the current innovations of a few industry leaders are evidence that a new era is dawning. When a new way of thinking about and applying a strength begins to take hold, managers are challenged to respond in many ways. Change comes fast to every part of a business’s world. New players emerge, competitive positions shift, novel technologies must be mastered, and talent gravitates toward the most exciting new work.
Managers will see all these things in the coming months and years. The ones who respond most effectively will be those who have connected the dots and recognized that competing on analytics is being rethought on a large scale. Indeed, the first companies to perceive the general direction of change—those with a sneak peek at Analytics 3.0—will be best positioned to drive that change.
The Evolution of Analytics
My purpose here is not to make abstract observations about the unfolding history of analytics. Still, it is useful to look back at the last big shift and the context in which it occurred. The use of data to make decisions is, of course, not a new idea; it is as old as decision making itself. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could.
Today it isn’t just online and information firms that can create products and services from analyses of data. It’s every firm in every industry.
Analytics 1.0—the era of “business intelligence.”
What we are here calling Analytics 1.0 was a time of real progress in gaining an objective, deep understanding of important business phenomena and giving managers.
Enterprise Search White Paper: Increase Your Competitiveness - Make a Knowled...Findwise
With data volumes growing by 200 percent a year, knowledge workers are spending around 30 percent of their time trying to extract useful information. Furthermore a recent U.S. study asserted that knowledge workers spend more than twice as much time re-creating already created content as they spend creating new content. In addition to this time spent on maintaining structures for storing incoming unstructured information (e.g. mail, documents etc) is increasing rapidly.
Enabling search solutions makes information easy to find, however the key is to transform this information into knowledge. This is normally not done by simple intranet search functionality, however the intranet portal can act as a portal to a knowledge management system based on advanced search functionality withadded collaborative functions. This transforms your organization into a “knowledge finding organization”, creating an even more competitive organization.
Knowledge Management systems based on an Enterprise Search Platform (ESP) can, if implemented properly, significantly improve the efficiency of an organization. IDC Research suggests in their latest report (April 2006) “Hidden cost of information Work” that the cost for wasted time on the part of professional searching, but not finding relevant information, amounts to $5.3 million annually for an enterprise with 1000 knowledge workers.
Big Data & Investment Management: The Potential to Quantify Traditionally Qua...Ken Cutroneo
Big data is a catchphrase for a new way of conducting analysis. Big data principles are being adopted across many industries and in many varieties. However, adoption so far by investment managers has been limited. This may be creating a window of opportunity in the industry.
This is a discussion of how firms that build strong skills in software development and data analytics, especially intelligent analytics, will dominate their industries in the next few years.
Machine Intelligence: The Hypergiant EditionHypergiant
Today, the enhancing technology that is machine learning has matured to the point where its commoditization allows us to apply intelligence horizontally across an organization. Oil and gas companies, consumer packaged goods brands, airlines, space technologists, and information officers from all industries are invested in the continuous development of these applications and, so, bring them more and more into business discussions and large-scale solutions. Curious how we do it here at Hypergiant? Flip through our playbook, delve into our ethos, and embrace the improvements brought on by machine intelligence.
After reading and downloading this PDF, be sure to check out more exclusive materials on core machine intelligence capabilities available at Hypergiant.com/Transcripts.
How Data Virtualization Puts Machine Learning into Production (APAC)Denodo
Watch full webinar here: https://bit.ly/3mJJ4w9
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
Facilitating Collaborative Life Science Research in Commercial & Enterprise E...Chris Dagdigian
This is a talk I put together for a http://www.neren.org/ seminar called "Bridging the Gap: Research Facilitation". Tried to give a biotech/pharma view for a mostly academic audience.
Can content management be used as an asset to boost productivity and collaboration?
Atos’ performance at the Olympic Games has proved the ideal challenge to improving the way we deliver world-beating business technology for our clients. This new Fast Track Guide on ECM is quick to read and formed from the very latest thinking. It describes how, in the economic climate, enterprise collaboration tools have been embraced by organizations looking to become leaner and more flexible to boost productivity and efficiency across an increasingly dispersed and mobile workforce.
U.S. Consumer Search Preferences Q1 2017Joe Buzzanga
Table of Contents and Figures for Fivesight Research's Report on consumer search preferences. Analyzes results of a survey of 800 U.S. consumers in Q1 2017
Intelligent personal assistant testing 1Joe Buzzanga
We test three intelligent personal assistants: Siri, Google Now and Cortana. The test considers conversational ability, specifically intersentential pronominal resolution (understanding how a pronoun refers back to an person or entity). Google Now was the only solution able to handle this advanced and essential task.
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.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Monitoring Java Application Security with JDK Tools and JFR Events
Technology Intelligence for R&D
1. SEPT. 3, 2008
Using Technology Intelligence for R&D
Instead of spending weeks and months on R&D, companies are now able to cut this time to
seconds and minutes.
B Y J O E B U Z Z A N G A , P R O D U C T M A N A G E R , E LS E V I E R
I
n today’s networked economy, the most effective What is “Technology Intelligence” — and what
R&D goes beyond a company’s four walls and does it mean for the bottom line?
explores what’s going on in the outside world
The concept of Technology Intelligence plays an
— in terms of technologies, products, strategies, in-
important role here, referring to:
novations, thought leaders — and how this infor-
mation can be harnessed and applied to internal in- “…the activity that enables companies to iden-
novation. But this is not an easy task. Consider this: tify the technological opportunities and threats
in 2007 the global top 1,250 R&D companies spent that could affect the future growth and survival of
over $479 billion on R&D. their business. It aims to capture and disseminate
the technological information needed for strategic
This has resulted in an accelerated movement
planning and decision-making. As technology life
around the information retrieval process and a
cycles shorten and business becomes more global-
hunt for technology that accomplishes for business
ized, having effective Technology Intelligence capa-
researchers what Google has done for consumers.
bilities is becoming increasingly important.”
This is because corporate R&D ends up wasting
valuable man hours, brain power, and resources on We’re moving from the “Information Age” to the
information retrieval. Some additional statistics: “Intelligence Age.” The former was all about build-
ing the database — i.e. the Web and its vast amount
• According to recent Forrester Research, the vol-
of content — but today we’ve progressed to creating
ume of the world’s data doubles approximately
contextual access to the right pieces of information
every three years;
to derive intelligence, meaning and insight.
• Per week, corporate R&D professionals (scien-
Traditional methods for extracting such “intelli-
tists and engineers) spend 5.5 hours gathering,
gence” range from manually sifting through pub-
looking for or pulling together information, and
lications and journals, networking or attending
an additional 4.7 hours analyzing and applying
tradeshows and focus groups, hiring outsourced
this information.
consultants, or just standard surfing the Internet.
Simply stated, 1) there is a lot of information out Yet both in their individual silos, as well as when
there, 2) companies recognize the value of this in- mapped out across each other, these methods still
formation and therefore are willing to spend money prove to have a number of drawbacks: difficult to
to retrieve and analyze it, but 3) seem to spend too identify relevant information, take weeks or months
much time doing so. Bottom line, people aren’t lack- to complete, resource intensive, expensive, and in
ing information, but rather need insights gleaned the end return unpredictable results.
from this information.
Going a layer deeper, the actual information that
Building upon the principles of Open Innovation, such approaches present often falls short of its po-
there are new techniques and technologies that tential. For example, any search engine to date, paid
bring structure, relevance and meaning to unlock or free, provides results in the form of records with
content on the Internet for actionable business pur- citations (i.e.: title and abstract), similar to a Google
poses. This goes beyond simple Google keyword search. But an important transition is happening in
searches, and leverages approaches that intuitively the technology intelligence field, shifting from cita-
search based on real-world problems and solutions. tions to meaning: actually understanding citations
In doing so, instead of spending weeks and months that are in the search results, and then extracting
on R&D, companies are now able to cut this time to meaning and insights from this query data.
seconds and minutes.