Tesla Motors is transitioning to using Dassault Systemes' 3DExperience platform to improve its product development process as it ramps up production of its Model 3 electric vehicle. The transition involves migrating Tesla's engineering teams and data to the new platform, which will integrate CAD, bill of materials management, and other tools. While the migration has presented challenges, Tesla believes 3DExperience will help improve efficiency as it aims to produce many more vehicles at higher volumes.
TypeDB Academy- Getting Started with Schema DesignVaticle
In this TypeDB Academy, we start by gaining an understanding of the fundamental components of TypeDB's type system and what makes it unique. We will see how we can download, install, and run TypeDB, and learn to perform basic database operations.
We'll then explore what a schema looks like in TypeDB, starting with clarifying the motivation for schema, the conceptual schema of TypeDB, and its relationship to the Enhanced Entity-Relationship model.
Good for:
- Beginners to TypeDB and TypeQL
- Those who have been using TypeDB and want a refresher on schema and TypeQL
- Experienced database administrators and software engineers
Takeaways:
- Understanding of fundamental components of TypeDB
- How to download, install, and run TypeDB on your computer
- Be able to articulate why schema is so beneficial when using TypeDB, why we use one, and how it enables a more expressive model
- Write a TypeDB schema in TypeQL
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
TypeDB Academy- Getting Started with Schema DesignVaticle
In this TypeDB Academy, we start by gaining an understanding of the fundamental components of TypeDB's type system and what makes it unique. We will see how we can download, install, and run TypeDB, and learn to perform basic database operations.
We'll then explore what a schema looks like in TypeDB, starting with clarifying the motivation for schema, the conceptual schema of TypeDB, and its relationship to the Enhanced Entity-Relationship model.
Good for:
- Beginners to TypeDB and TypeQL
- Those who have been using TypeDB and want a refresher on schema and TypeQL
- Experienced database administrators and software engineers
Takeaways:
- Understanding of fundamental components of TypeDB
- How to download, install, and run TypeDB on your computer
- Be able to articulate why schema is so beneficial when using TypeDB, why we use one, and how it enables a more expressive model
- Write a TypeDB schema in TypeQL
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
Unifying Space Mission Knowledge with NLP & Knowledge GraphVaticle
Synopsis
The number of space missions being designed and launched worldwide is growing exponentially. Information on these missions, such as their objectives, orbit, or payload, is disseminated across various documents and datasets. Facilitating access to this information is key to accelerating the design of future missions, enabling experts to link an application to a mission, and following various stakeholders' activities.
This presentation introduces recent research done at the ESA to combine the latest Language Models with Knowledge Graphs, unifying our knowledge on space missions. Language Models such as GPT-3 and BERT are trained to understand the patterns of human (natural) language. These models have revolutionised the field of NLP, the branch of AI enabling machines to understand human language in all its complexity. In this work, key information on a mission is parsed from documents with the GPT-3 model, and the parsed data is then migrated to a TypeDB Knowledge Graph to be easily queried. Although this work focuses on an application in the space sector, the method can be transferred to other engineering fields.
Presenters
Dr. Audrey Berquand is a Research Fellow at the ESA. Her research aims at enhancing space mission design and knowledge management with text mining, NLP, and Knowledge Graphs. She was awarded her PhD in 2021 from the University of Strathclyde (Scotland) for her thesis on “Text Mining and Natural Language Processing for the Early Stages of Space Mission Design”. Audrey has a background in space systems engineering, she holds an MSc in Aerospace Engineering from the Royal Institute of Technology KTH (Sweden), and a diplôme d'ingénieur from the EPF Graduate School of Engineering (France). Before diving into the world of AI, she spent 3 years at ESA being involved in the early design phases of future Earth Observation missions.
Ana Victória Ladeira works with Knowledge Management at the ESA, using automated methods to exploit the information contained in the piles and piles of documents that ESA generates every day. With a Masters degree in Data Science from Maastricht University, Ana is particularly excited about how NLP methods can help large organizations connect different documents and highlight the bigger picture over a big universe of data sources, as well as using Knowledge Graphs to help connect people to the expertise and information they need.
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptxNeo4j
Knowledge Graphs and Generative AI, together, enable contextual and semantic information retrieval from both structured and unstructured data sources. LLMs and Neo4j labeled property graphs synergize seamlessly, whether for querying your enterprise graph with natural language or converting unstructured data into a knowledge graph. Join our session to understand how the deep dynamic context that Knowledge Graphs provide helps ensure answers from an LLM are accurate, explainable, and contextual.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
Climbing the Ontology Mountain to Achieve a Successful Knowledge GraphEnterprise Knowledge
Tatiana Baquero Cakici, Senior KM Consultant, and Jennifer Doughty, Senior Solution Consultant from Enterprise Knowledge’s Data and Information Management (DIME) Division presented at the Taxonomy Boot Camp (KMWorld 2022) on November 17, 2022. KMWorld is the world’s leading knowledge management event that takes place every year in Washington, DC.
Their presentation “Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph” focused on how ontologies have gained momentum as a strong foundation for resolving business challenges through semantic search solutions, recommendation engines, and AI strategies. Cakici and Doughty explained that taxonomists are now faced with the challenge of gaining knowledge and experience in designing and documenting complex solutions that involve the integration of taxonomies, ontologies, and knowledge graphs. They also emphasized that taxonomists are well poised to learn how to design user-centric ontologies, analyze and map data from various systems, and understand the technological architecture of knowledge graph solutions. After describing the key roles and responsibilities needed for a team to successfully implement Knowledge Graph projects, Cakici and Doughty shared practical ontology design considerations and best practices based on their own experience. Lastly, Cakici and Doughty reviewed the most common use cases for knowledge graphs and presented real world applications through a case study that illustrated ontology design and the value of knowledge graphs.
2013年7月16日にシンガポールで開催された第一回アジア組み合わせテストワークショップ(1st Asian Workshop on Combinatorial Testing for Complex Computer Systems)で発表した"Combinatorial Testing in Japan"のスライドを日本語にしました(だいぶ遅くなりましたが)。
英語版はこちら
https://www.slideshare.net/Bugler/combinatorial-testing-injapan20130616
Here's the deck we used for our Series-A round. We raised $26M led by Benchmark, 2 months after our Seed round with Accel.
Even though we didn't necessarily show the appendix slides, we sent them along with the rest of the deck.
See https://airbyte.com
Predicting Flight Delays with Spark Machine LearningCarol McDonald
Apache Spark's MLlib makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In this webinar, we will go over an example from the ebook Getting Started with Apache Spark 2.x.: predicting flight delays using Apache Spark machine learning.
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven RecipesOntotext
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
Using an employee knowledge graph for employee engagement and career mobilityNeo4j
Learn what a knowledge graph is and how it plays a salient role in enterprises and how to apply knowledge graphs for various business use cases across the data spectrum – from management to analytics and machine learning.
Knowledge Graphs for Supply Chain Operations.pdfVaticle
Agility in supply chain operations has never been so important, especially with today's nonlinear and complex world. That is why companies with supply chains need knowledge graphs.
So how do enterprises unleash the power of their own supply chain data to make smarter decisions? This is where bops comes into play. Bops activates supply chain data from existing operating systems (ERPs, Pos, OMS, etc) simplifying how operators optimize working capital in every decision.
In this session, bops will showcase a few use cases that portray the power of a knowledge graph to represent a supply chain network composed of an end to end product flow driven by actions among plants, customers and suppliers.
Supply chain operations visibility:
- Story of a Product and an SKU: from raw material to finished goods track trace & bill of material deviations
- Story of a Supplier – risk assessments – “the most influential supplier”
- Story of a Process – anomaly detection – “what went wrong?”
Join us for a lively discussion to learn how using knowledge graphs is already helping supply chain companies to better collect, unify, and activate their data.
Speaker: Jorge Risquez
Jorge is the Co-founder and CEO of bops, a headless supply chain intelligence platform helping manufacturers and distributors source, make, and deliver their products, and unlock working capital. Previously, Jorge spent a decade as a Supply Chain Consultant for Deloitte, where he worked with Fortune 500 companies such as Tyson and Cargill. In his spare time, he enjoys going for a run in Central Park and spending time with family and friends.
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey GraingerOpenSource Connections
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
Unifying Space Mission Knowledge with NLP & Knowledge GraphVaticle
Synopsis
The number of space missions being designed and launched worldwide is growing exponentially. Information on these missions, such as their objectives, orbit, or payload, is disseminated across various documents and datasets. Facilitating access to this information is key to accelerating the design of future missions, enabling experts to link an application to a mission, and following various stakeholders' activities.
This presentation introduces recent research done at the ESA to combine the latest Language Models with Knowledge Graphs, unifying our knowledge on space missions. Language Models such as GPT-3 and BERT are trained to understand the patterns of human (natural) language. These models have revolutionised the field of NLP, the branch of AI enabling machines to understand human language in all its complexity. In this work, key information on a mission is parsed from documents with the GPT-3 model, and the parsed data is then migrated to a TypeDB Knowledge Graph to be easily queried. Although this work focuses on an application in the space sector, the method can be transferred to other engineering fields.
Presenters
Dr. Audrey Berquand is a Research Fellow at the ESA. Her research aims at enhancing space mission design and knowledge management with text mining, NLP, and Knowledge Graphs. She was awarded her PhD in 2021 from the University of Strathclyde (Scotland) for her thesis on “Text Mining and Natural Language Processing for the Early Stages of Space Mission Design”. Audrey has a background in space systems engineering, she holds an MSc in Aerospace Engineering from the Royal Institute of Technology KTH (Sweden), and a diplôme d'ingénieur from the EPF Graduate School of Engineering (France). Before diving into the world of AI, she spent 3 years at ESA being involved in the early design phases of future Earth Observation missions.
Ana Victória Ladeira works with Knowledge Management at the ESA, using automated methods to exploit the information contained in the piles and piles of documents that ESA generates every day. With a Masters degree in Data Science from Maastricht University, Ana is particularly excited about how NLP methods can help large organizations connect different documents and highlight the bigger picture over a big universe of data sources, as well as using Knowledge Graphs to help connect people to the expertise and information they need.
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptxNeo4j
Knowledge Graphs and Generative AI, together, enable contextual and semantic information retrieval from both structured and unstructured data sources. LLMs and Neo4j labeled property graphs synergize seamlessly, whether for querying your enterprise graph with natural language or converting unstructured data into a knowledge graph. Join our session to understand how the deep dynamic context that Knowledge Graphs provide helps ensure answers from an LLM are accurate, explainable, and contextual.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
Climbing the Ontology Mountain to Achieve a Successful Knowledge GraphEnterprise Knowledge
Tatiana Baquero Cakici, Senior KM Consultant, and Jennifer Doughty, Senior Solution Consultant from Enterprise Knowledge’s Data and Information Management (DIME) Division presented at the Taxonomy Boot Camp (KMWorld 2022) on November 17, 2022. KMWorld is the world’s leading knowledge management event that takes place every year in Washington, DC.
Their presentation “Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph” focused on how ontologies have gained momentum as a strong foundation for resolving business challenges through semantic search solutions, recommendation engines, and AI strategies. Cakici and Doughty explained that taxonomists are now faced with the challenge of gaining knowledge and experience in designing and documenting complex solutions that involve the integration of taxonomies, ontologies, and knowledge graphs. They also emphasized that taxonomists are well poised to learn how to design user-centric ontologies, analyze and map data from various systems, and understand the technological architecture of knowledge graph solutions. After describing the key roles and responsibilities needed for a team to successfully implement Knowledge Graph projects, Cakici and Doughty shared practical ontology design considerations and best practices based on their own experience. Lastly, Cakici and Doughty reviewed the most common use cases for knowledge graphs and presented real world applications through a case study that illustrated ontology design and the value of knowledge graphs.
2013年7月16日にシンガポールで開催された第一回アジア組み合わせテストワークショップ(1st Asian Workshop on Combinatorial Testing for Complex Computer Systems)で発表した"Combinatorial Testing in Japan"のスライドを日本語にしました(だいぶ遅くなりましたが)。
英語版はこちら
https://www.slideshare.net/Bugler/combinatorial-testing-injapan20130616
Here's the deck we used for our Series-A round. We raised $26M led by Benchmark, 2 months after our Seed round with Accel.
Even though we didn't necessarily show the appendix slides, we sent them along with the rest of the deck.
See https://airbyte.com
Predicting Flight Delays with Spark Machine LearningCarol McDonald
Apache Spark's MLlib makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In this webinar, we will go over an example from the ebook Getting Started with Apache Spark 2.x.: predicting flight delays using Apache Spark machine learning.
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven RecipesOntotext
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
Using an employee knowledge graph for employee engagement and career mobilityNeo4j
Learn what a knowledge graph is and how it plays a salient role in enterprises and how to apply knowledge graphs for various business use cases across the data spectrum – from management to analytics and machine learning.
Knowledge Graphs for Supply Chain Operations.pdfVaticle
Agility in supply chain operations has never been so important, especially with today's nonlinear and complex world. That is why companies with supply chains need knowledge graphs.
So how do enterprises unleash the power of their own supply chain data to make smarter decisions? This is where bops comes into play. Bops activates supply chain data from existing operating systems (ERPs, Pos, OMS, etc) simplifying how operators optimize working capital in every decision.
In this session, bops will showcase a few use cases that portray the power of a knowledge graph to represent a supply chain network composed of an end to end product flow driven by actions among plants, customers and suppliers.
Supply chain operations visibility:
- Story of a Product and an SKU: from raw material to finished goods track trace & bill of material deviations
- Story of a Supplier – risk assessments – “the most influential supplier”
- Story of a Process – anomaly detection – “what went wrong?”
Join us for a lively discussion to learn how using knowledge graphs is already helping supply chain companies to better collect, unify, and activate their data.
Speaker: Jorge Risquez
Jorge is the Co-founder and CEO of bops, a headless supply chain intelligence platform helping manufacturers and distributors source, make, and deliver their products, and unlock working capital. Previously, Jorge spent a decade as a Supply Chain Consultant for Deloitte, where he worked with Fortune 500 companies such as Tyson and Cargill. In his spare time, he enjoys going for a run in Central Park and spending time with family and friends.
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey GraingerOpenSource Connections
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
How Sapient Razorfish Cuts Its Campaign Deployment Times from Days to Minutes...Amazon Web Services
SapientRazorfish, a leader in creating digital experiences for companies and brands, needed a solution to deploy campaigns rapidly – often within a day – to meet the needs of demanding marketers and consumers. Rackspace created an automated ‘Single Campaign per Solution’ process on AWS, with a standard protocol around Elastic Beanstalk for the Web/Application tier. Today SapientRazorfish launches campaigns within 5 minutes, compared with 3 days previously. That improvement has led to SapientRazorfish having happier customers, delivering more timely, effective campaigns, and reducing costs, all with minimal change for developers.
Touch Drive - A touch-based multi-function controller for autonomous drivingJuntima Nawilaijaroen
Msc. Interaction Design and Technologies Master Thesis Presentation
A design solution is proposed in this thesis, which aims to establish a convenient way for controlling autonomous cars and at the same time enables the user to control tertiary features unrelated with the driving task.
Building out a Global Data delivery platform - the business and technical use...AWS Chicago
AWS Community Day | Midwest 2018
Track 2
Building out a Global Data delivery platform - the business and technical use cases - Neil Dawkins, Scott Vina, and Tim Nettleton, Chicago
Puppet Camp Sydney 2014 - Evolving Design Patterns in AWSjohnpainter_id_au
A view of the past, present and future roles and architectures of Puppet Enterprise in AWS. Based on real world enterprise examples this presentation gives a in-the-trenches view of 4 key architectural patterns for Puppet Enterprise in AWS.
Architecture and demo of native integration between Puppet Enterprise and AWS Autoscaling. Dynamic autoscaled nodes are automatically signed (programatically, NOT via auto sign), and groups assigned according to AWS native metadata. Nodes are then deregistered and remove from the master as autoscale/autoheal deregisters them.
Solution 1 - Multi Master
Solution 2 - Puppet Controlling AWS - Uplift of Puppet CloudPack to support AWS, Rackspace and Joyent.
Solution 3 - Masterless puppet via AWS S3 and local apply.
Solution 4 - Puppet Enterprise natively interfacing with AWS Autoscale via the Sourced Autoscale Broker.
Solution 5 - A Scale out architecture for autoscaled PaaS platforms with Puppet Enterprise providing a compliance tier.
Improvement of strip thickness control through the process of data analyticsSri Raghavan
o The aim of this research study is to perform data mining for the improvement of strip thickness control on a cold reduction mill using data analytics. This project is done for Cogent Power (a subsidiary company of Tata Steel) located at Newport, UK. Further to this, a software was developed in python to perform data mining to avoid developing codes for future purposes
#TSA2016 New Trends in Talent Acquisition by Jonathan Kestenbaum, Talent Tech...Svetla Simidchieva
New Trends in Talent Acquisition Technology:
1. Matching Systems that work:
Products features:
Placement Feed
Switch
Job and Talent
Wade & Wendy
2. New Delivery Models for Staffing Firms
Products featured:
Interview Jet
Talentswype
Upwork
3. CRM Tools get rapid adoption
Products featured:
Phenom People
Smashfly
Clinch
TextUS
Text Recruit
4. Games to Replace Lengthy Questionnaires in Assessment
Featured products:
Arctic Shores
Mercer Match
Lens
GapJumpers
Interviewed
Hacker Rank
ABOUT ENEL OPEN DATA
- Launched in 2011 among the first business organisations investing in Open Data worldwide “SOME LEADING BUSINESSES, LIKE ENEL, ITALY’S LARGEST POWER COMPANY, AND NIKE, ARE MORE PROACTIVE, PUBLISHING THEIR DATA TO DEMONSTRATE A COMMITMENT TO TRANSPARENCY AND SUSTAINABILITY” DELOITTE ON OPEN DATA IN 2012
- 725 datasets (424 IT, 301 EN), 5 main categories (FINANCE World, Finance Italy, SUSTAINABILITY, ENVIRONMENT, TERRITORY)
Linked Open GeoData for Enel Drive (W3C LOD2014)Andrea Volpini
Presenting Linked Open Data for Enel Electric Vehicle Charging Network. A solution developed for Enel Drive by Insideout10 using Redlink Linked Data publishing APIs.
The triplification (transforming source legacy data in RDF triples) provides an information context around each stations and helps EV drivers locate a charging stations in various ways.
The project wants to contribute to the development of electric mobility in Italy
Linked Open GeoData for Electric Vehicle Charging Stations by ENELRedlink GmbH
Originally published by Raffaele Cirullo (http://www.slideshare.net/cirullo/enel-drive-linked-open-geo-data) - the presentation describes the triplication (RDF publishing) of real-time data provided by Enel Drive: Italy's largest EV Charging network.
Using Redlink the information is shared in real-time as Linked Open Data. The data is also published to OpenStreetMap and foursquare.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
4. M I S S I O N
To accelerate the world’s transition to sustainable transport.
5. T E S L A ’ S S E C R E T P L A N
P R O D U C T I O N V O L U M E
1 S T G E N E R A T I O N
R O A D S T E R | 2 0 0 8
2 N D G E N E R A T I O N
M O D E L S | 2 0 1 2
M O D E L X | 2 0 1 5
AFFORABILITY
3 R D G E N E R A T I O N
M O D E L 3 | 2 0 1 7
7. T E S L A T O D A Y
>50K
C A R S
D E L I V E R E D
>1B
M I L E S
D R I V E N
150M
L I T E R S O F
G A S O L I N E S A V E D
300+
S U P E R C H A R G E R
S T A T I O N S W O R L D W I D E
>1K
C A R S B U I L T
P E R W E E K
>10K
T E S L A
E M P L O Y E E S
140+
S T O R E S & S E R V I C E
L O C A T I O N S W O R L D W I D E
8. T E S L A N E T W O R K
S E R V I C ER E T A I L S U P E R C H A R G E R S
9. E A S E O F C H A R G I N G
170 miles of range in only 30 minutes
Network funded, built and maintained by Tesla
>189 million electric miles enabled
500+ charging sites
Almost 3,000 Superchargers worldwide
11. M O S T C O N N E C T E D
C A R O N E A R T H
Tracking Available via Mobile App
Vehicle Localization When Stolen
Media | Internet, Radio, Navigation
Over-the-air Updates
Remote Vehicle Health / History Diagnosis
Remote Repairs
12. S O F T W A R E V 7 . 0
Autosteer
Auto Lane Change
Side Collision Warning
Autopark
Hill Hold
13. A U T O P I L O T
524 ft. Forward Radar
360° Sonar Sensors
12+2 Sensors & Cameras
14. S A F E T Y
Larger crumple zone to absorb impact
Cabin rigidity minimizing intrusion
Low center of gravity preventing rollover
H I G H E S T S A F E T Y R A T I N G I N A M E R I C A
NHTSA
15. A L L - W H E E L D R I V E
D U A L M O T O R
Improved Range
Faster Acceleration
Unparalleled Traction
20. C U S T O M E R - F O C U S E D A P P R O A C H
21. A W A R D S A N D A C C O L A D E S
F O R B E S W O R L D ’ S M O S T
I N N O V A T I V E C O M P A N Y
2 0 1 3 M O T O R T R E N D
C A R O F T H E Y E A R
C O N S U M E R R E P O R T S
B E S T C A R E V E R T E S T E D
22. T E S L A T O M O R R O W
A U T O P I L O T E N E R G Y S T O R A G E
S O L U T I O N S
M O D E L 3 R E L E N T L E S S
I N N O V A T I O N
23. P R I O R I T I E S
ONGOING
Development of automotive and non-automotive products
Flexible development style - Emphasis on speed and iteration
Small teams – engineering and IT
SOLUTIONS (so far)
CATIA and DELMIA V5, ENOVIA V6, SIMULIA …
CAD, Manufacturing Simulation, Product Lifecycle and CAD Management
25. P R I O R I T I E S
NEW
Planning for significantly higher volume products in Model 3 and future products
Focus on Product Platform, Engineering and Manufacturing Efficiency
Small teams – engineering and IT (still)
SOLUTION : “TESLA 3DX” (coming very soon)
Integrated end-to-end workflow
3DExperience – CATIA, DELMIA, ENOVIA, SIMULIA 2015X
Latest tools for CAD, CAD-eBOM-mBOM alignment with Option/Date effectivity,
Smart fasteners, ubiquitous visualization, business intelligence tools, …
26. T I M E L I N E
P R O D U C T I O N V O L U M E
1 S T G E N E R A T I O N
R O A D S T E R | 2 0 0 8
2 N D G E N E R A T I O N
M O D E L S | 2 0 1 2
M O D E L X | 2 0 1 5
AFFORABILITY
3 R D G E N E R A T I O N
M O D E L 3 | 2 0 1 7
27. P R O G R E S S T O T E S L A 3 D X
PLAN: Deliver the best experience for our Engineering, Design,
Manufacturing teams
Migrate all teams, current programs and working data
Phased Deployment
1. CAD Migration
2. eBOM Alignment
3. mBOM Capability
Leverage V6→V5 for Supplier Collaboration, 3rd party tools
Massive effort…
28. L E S S O N S S O F A R
CAD Transformation (V5V6) is demanding
VPM processes change the conventional engineering methods
Still looking to a consolidated, fully-functional Product Structure (CAD+BOM) in
3DExperience
Many 3rd party tools not compatible or ready
29. N E X T S T E P S
Lots of work!
Training, testing, resolving, repeat…
Latest release - Leverage DS expert resources
Figure out data migration – when and how long?
Phase in manufacturing development as engineering comes on-line
Drive and maintain Business Engagement
Develop/support a community of 3DExperience companies for shared
problem-solving, direction-setting etc.