AS = {Companies(Name, Address, Year), Grants(Gid, Recipient, Amount, Supervisor)}
Organizations:
Code
Fundings:
FId
Finances:
FinId
Budget
Phone
AT = {Organizations(Code), Fundings(FId), Finances(FinId, Budget, Phone)}
Information Systems Group Leila Jalali, Candidacy Exam
Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Logical Associations
Build larger associaitons in Source (AS) and Target (AT)
AS = {Companies(
2010 VLDB - TRAMP: Understanding the Behavior of Schema Mappings through Prov...Boris Glavic
Though partially automated, developing schema mappings remains a complex and potentially error-prone task. In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries. TRAMP combines and extends data provenance with two novel notions, transformation provenance and mapping provenance, to explain the relationship between transformed data and those transformations and mappings that produced that data. In addition we provide query support for transformations, data, and all forms of provenance. We formally define transformation and mapping provenance, present an efficient implementation of both forms of provenance, and evaluate the resulting system through extensive experiments.
Despite all the recent advancements in the operations management field, data center management today still largely remains as a black art. Administrators have limited visibility into their data center operations today and yet they have to make important operations management decisions every day. A typical data center generates about a Billion data points every day. A lot of insight could be gathered from this data but due to the large volume and scale, on-premise software solutions only collect limited subset of this data. This limits them to a very narrow view of the data center. We at CloudPhysics have taken a different approach to this problem. We created an analytics platform in the cloud, that provides the ability to query, slice and dice and mashup the data with multiple data-sources. This approach not only yields incredible insights but also solves many of the teething operational management issues that have not been solved before. In this talk we give an overview of the data center metadata and provide details on how CloudPhysics handles this data at scale using its platform.
The focus of the presentation is to develop a framework and platform that supports the integration of multiple models, simulations, and data. My aim is to develop methods to integrate a set of simulated environments to make it possible to combine various independent simulators, developed by different domain experts. This would make it possible for researchers to build complex, multi-domain simulations by integrating existing and well-established simulators, so they can explore different alternatives and conduct low cost experiments.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
2010 VLDB - TRAMP: Understanding the Behavior of Schema Mappings through Prov...Boris Glavic
Though partially automated, developing schema mappings remains a complex and potentially error-prone task. In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries. TRAMP combines and extends data provenance with two novel notions, transformation provenance and mapping provenance, to explain the relationship between transformed data and those transformations and mappings that produced that data. In addition we provide query support for transformations, data, and all forms of provenance. We formally define transformation and mapping provenance, present an efficient implementation of both forms of provenance, and evaluate the resulting system through extensive experiments.
Despite all the recent advancements in the operations management field, data center management today still largely remains as a black art. Administrators have limited visibility into their data center operations today and yet they have to make important operations management decisions every day. A typical data center generates about a Billion data points every day. A lot of insight could be gathered from this data but due to the large volume and scale, on-premise software solutions only collect limited subset of this data. This limits them to a very narrow view of the data center. We at CloudPhysics have taken a different approach to this problem. We created an analytics platform in the cloud, that provides the ability to query, slice and dice and mashup the data with multiple data-sources. This approach not only yields incredible insights but also solves many of the teething operational management issues that have not been solved before. In this talk we give an overview of the data center metadata and provide details on how CloudPhysics handles this data at scale using its platform.
The focus of the presentation is to develop a framework and platform that supports the integration of multiple models, simulations, and data. My aim is to develop methods to integrate a set of simulated environments to make it possible to combine various independent simulators, developed by different domain experts. This would make it possible for researchers to build complex, multi-domain simulations by integrating existing and well-established simulators, so they can explore different alternatives and conduct low cost experiments.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
This article is all about what AI trends will emerge in the field of creative operations in 2024. All the marketers and brand builders should be aware of these trends for their further use and save themselves some time!
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...Applitools
During this webinar, Anand Bagmar demonstrates how AI tools such as ChatGPT can be applied to various stages of the software development life cycle (SDLC) using an eCommerce application case study. Find the on-demand recording and more info at https://applitools.info/b59
Key takeaways:
• Learn how to use ChatGPT to add AI power to your testing and test automation
• Understand the limitations of the technology and where human expertise is crucial
• Gain insight into different AI-based tools
• Adopt AI-based tools to stay relevant and optimize work for developers and testers
* ChatGPT and OpenAI belong to OpenAI, L.L.C.
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
Data Integration
1. Clio: Schema Mapping Creation and
Data Exchange
Presented by
Leila Jalali
Information Systems Group Candidacy Exam, Jan. 2010
2. the Clio project
•Wants data from S
•Understands T
•May not understand S Q
Source
Schema Mapping Target
schema T
schema S
“conforms to” “conforms to”
Data Exchange
data
to transform data
Clio addresses two main problems:
How to generate schema mappings and how to use them for data exchange?
exchange
Information Systems Group Leila Jalali, Candidacy Exam
3. Outline
The Motivating Example
2. Schema Mapping Generation
Mapping generation algorithm
2. Data Exchange
Query generation algorithm
Conclusions
Information Systems Group Leila Jalali, Candidacy Exam
4. A Motivating Example
Schema S:
Companies: Set of Rcd Schema T:
Name v1 Organizations: Set of Rcd
Address Code
Year Year
f1
Fundings: Set of Rcd
Grants : Set of Rcd v2 FId
Gid FinId
Recipient
f4
Amount Finances: Set of Rcd
v3
Supervisor FinId
f2 Manager Budget
f3 Phone
Contacts : Set of Rcd
v4 Correspondences
Cid
(given by a "schema matcher“ or
Email
a“user”)
Phone
Information Systems Group Leila Jalali, Candidacy Exam
5. Correspondences
Companies Using tuple generating dependency(tgd):
Name v1 Organizations
Address Code ∀n,d,y Companies(n,d,y) →
v1:
∃y',F Organizations(n,y',F))
Year Year
f1 Grants
Fundings
Gid v2 FId
Recipient FinId
Amount
foreach c in companies
f2 Supervisor v3 Finances f4
f3 exists o in organizations,
Manager FinId
Contacts Budget with o.code = c.name
Cid Phone
Email
Phone
v4
Information Systems Group Leila Jalali, Candidacy Exam
6. More complex mappings
Companies ∀n,d,y,g,a,s,m Companies(n,d,y),
Name v1 Organizations Grants(g,n,a,s,m) →
Address Code ∃y',F,f, p
Year Year
f1 Grants Organizations(n,y',F)),
Fundings
v2 F(g,f),
Gid FId
Recipient FinId Finances(f,a,p)
Amount
foreach c in companies, g in grants
f2 Supervisor v3 Finances f4
f3 where c.name=g.recipient
Manager FinId exists o in organizations,
Contacts Budget f in o.fundings,
Cid Phone i in finances
Email where f.finId = i.finId
v4
Phone with o.code = c.name
and f.fId = g.gId
and i.budget = g.amount
Information Systems Group Leila Jalali, Candidacy Exam
7. More complex mappings
Companies ∀n,d,y,g,a,s,m Companies(n,d,y),
Name v1 Organizations Grants(g,n,a,s,m) →
Address Code ∃y',F,f, p
Year Year
f1 Grants Organizations(n,y',F)),
Fundings
v2 F(g,f),
Gid FId
Recipient FinId Finances(f,a,p)
Amount
foreach c in companies, g in grants
f2 Supervisor v3 Finances f4
f3 where c.name=g.recipient
Manager FinId exists o in organizations,
Contacts Budget f in o.fundings,
Cid Phone i in finances
Email where f.finId = i.finId
v4
Phone query on the with o.code = c.name
source:QS and f.fId = g.gId
and i.budget = g.amount
query on the
Correspondences QS QT target: QT
Information Systems Group Leila Jalali, Candidacy Exam
8. Outline
The Motivating Example
2. Schema Mapping Generation
Mapping generation algorithm
2. Data Exchange
Query generation algorithm
Conclusions
Information Systems Group Leila Jalali, Candidacy Exam
9. Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Information Systems Group Leila Jalali, Candidacy Exam
10. Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Companies:
Name Organizations:
f1 Address from p in companies Code
Year Year from o in organizations
Grants: from g in grants Fundings:
Gid FId
f4
f2 Recipient FinId
f3 Finances:
Amount
Supervisor FinId
Manager Budget
Contacts: Phone
Cid
Email
Information Systems Group Leila Jalali, Candidacy Exam
11. Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Logical Associations
Build larger associaitons in Source (AS) and Target (AT)
Information Systems Group Leila Jalali, Candidacy Exam
12. Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Logical Associations
Build larger associaitons in Source (AS) and Target (AT)
Companies:
Name starting with a structural association and "chasing" constraints
f1 Address
AS :
Year
Grants:
Gid
f2 Recipient
f3 Amount
Supervisor
Manager
Contacts:
Information Systems Group Leila Jalali, Candidacy Exam
13. Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Logical Associations
Build larger associaitons in Source (AS) and Target (AT)
Use a pair of <AS,AT > and Correspondeces covered by <AS , AT> to generate a
Clio Mapping: foreach AS exists AT with W
W is the conjunction of equalities h (eS )=h’(eT ) (captured from correspondences)
Information Systems Group Leila Jalali, Candidacy Exam
14. Clio mapping, example
Generate a Clio Mapping: foreach AS exists AT with W
Companies
W is the conjunction of equalities h (eS )=h’(eT )
Name v1 Organizations
Address Code AS : from g in grants, c in companies,
Year Year s in contacts, m in contacts
f1 Grants where g.recipient = c.name
Fundings
Gid v2 FId
and g.supervisor = s.cid
Recipient and g.manager = m.cid
FinId
Amount AT: from o in organizations,
f2 Supervisor v3 Finances f4 f in o.fundings, i in finances
f3 Manager FinId where f.finId = i.finId
Contacts Budget
Cid Phone v1, v2, v3 are covered
Email
Phone
v4foreach g in grants, c in companies, s in contacts, m in contacts
where g.recipient = c.name and g.supervisor = s.cid and g.manager = m.cid
exists o in organizations, f in o.fundings, i in finances
where f.finId = i.finId
with c.name = o.code and g.gId = f. fId and g.amount = i.budget
Information Systems Group Leila Jalali, Candidacy Exam
15. Dominance
A2 dominates A1 (A1 ≤ A2 ) if
the from and where clauses of A1 are subsets of those of A2 (after
suitable renaming)
A2 : from g in grants, c in companies, s in contacts, m in contacts
where g.recipient = c.name and
g.supervisor = s.cid and
g.manager = m.cid
A1 : from g in grants, c in companies
where g.recipient = c.name
Information Systems Group Leila Jalali, Candidacy Exam
16. Coverage of a coresspondence
A correspondence v : foreach PS exists PT with eS=eT
is covered by a pair of associations <AS , AT> if PS ≤ AS and PT ≤ AT
with some renaming h, h’
AS : from c in companies v: foreach c in companies
Example: AT : fom o in organizations exists o in organizations
with c.name = o.code
Information Systems Group Leila Jalali, Candidacy Exam
17. Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Logical Associations
Build larger associaitons in Source (AS) and Target (AT)
Use a pair of <AS,AT > and Correspondeces covered by <AS , AT> and generate a
Clio Mapping: foreach AS exists AT with W
W is the conjunction of equalities h (eS )=h’(eT ) (captured from correspondences)
Information Systems Group Leila Jalali, Candidacy Exam
18. Mapping Generation
Source Schema Generate all possible associations within the Source
Structural Associations
Target Schema Generate all possible associations within the Target
Logical Associations
Build larger associaitons in Source (AS) and Target (AT)
Use a pair of <AS,AT > and Correspondeces covered by <AS , AT> and generate a
Clio Mapping: foreach AS exists AT with W
W is the conjunction of equalities h (eS )=h’(eT ) (captured from correspondences)
Add the Clio Mapping to the Set of Mappings
the Set of Mappings
Information Systems Group Leila Jalali, Candidacy Exam
19. Logical associations are meaningful
combinations of correspondences
Finds maximal sets of correspondences
that can be interpreted together
Discard the “larger” mapping
Generate a Clio mapping
Information Systems Group Leila Jalali, Candidacy Exam
20. Outline
The Motivating Example
1. Schema Mapping Generation
Mapping generation algorithm
2. Data Exchange
Query generation algorithm
Conclusions
Information Systems Group Leila Jalali, Candidacy Exam
21. Query generation for data exchange
Mapping
generation
Source Target
schema schema
Query
generation
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22. Overview of Query Generation
Input: A Clio Mapping
x 0.name
1. Query Graph is constructed which represents y 0 (organizations)
the key portions of the query in the graph x 0.name
x1. amount, x1.gid,
x 0.name,
y 0.year
2. Annotate the graph to generate Skolem terms y 1(fundings)
x 0.name
y 0 .code
x1.gid
x 0.name, x1.gid
3. Traverse the graph and produce the query y 0.fid y 0.finId
x1. gid
Output: the data exchange Query
(in SQL, XQuery, or XSLT)
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23. 1. Constructing the Query Graph
Adding a node for each variable in the exists clause
y0 (organizations) y2(finances)
y1(fundings)
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24. 1. Constructing the Query Graph (cont.)
Organizations:
Code
Year
Fundings:
FId
f4
Adding nodes for all the atomic type elements reachable from these FinId
nodes via record projection Finances
FinId
y0 (organizations) y2(finances) Budget
Phone
y1(fundings) y2.phone
y0.code y0.year y2.finId
y2.budget
y1.fid y1.finId
Information Systems Group Leila Jalali, Candidacy Exam
25. 1. Constructing the Query Graph (cont.)
Organizations:
Code
Year
Fundings:
FId
Add structural edges to reflect the relationships between nodes FinId
Finances
FinId
y0 (organizations) y2(finances) Budget
Phone
y1(fundings) y2.phone
y0.code y0.year y2.finId
y2.budget
y1.fid y1.finId
Information Systems Group Leila Jalali, Candidacy Exam
26. 1. Constructing the Query Graph (cont.)
Add the source nodes for all source expressions in the with clause
y0 (organizations) y2(finances)
y1(fundings) y2.phone
y0.code y0.year y2.finId
y2.budget
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
Information Systems Group Leila Jalali, Candidacy Exam
27. 1. Constructing the Query Graph (cont.)
Attach the source nodes to the target nodes to which they are “equal”
y0 (organizations) y2(finances)
y1(fundings) y2.phone
y0.code y0.year y2.finId
y2.budget
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
Information Systems Group Leila Jalali, Candidacy Exam
28. 1. Constructing the Query Graph (cont.)
Use the equalities in the where clause to add edges between target nodes
y0 (organizations) y2(finances)
y1(fundings) y2.phone
y0.code y0.year y2.finId
y2.budget
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
Information Systems Group Leila Jalali, Candidacy Exam
29. 2. Annotating the Graph
Each node is annotated with a set of source expressions
Upward propagation: Every expression that a node acquires is propagated
to its parent node, unless the (acquiring) node is a variable.
y0 (organizations) y2(finances)
x 2.phone
x 0.name
x 1.amount y2.phone
y1(fundings) y0.code y0.year y2.finId
y2.budget
x1.gid
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
Information Systems Group Leila Jalali, Candidacy Exam
30. 2. Annotating the Graph (cont.)
Downward propagation: Every expression that a node acquires is
propagated to its children
x 0.name
x 1.amount, x 2.phone
y0 (organizations) y2(finances)
x 2.phone
x1.gid
x 0.name
x 1.amount y2.phone
y1(fundings) y0.code y0.year y2.finId
y2.budget
x1.gid x 0.name
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
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31. 2. Annotating the Graph (cont.)
Eq. propagation: Every expression that a node acquires is propagated to
the nodes related to it through equality edges.
x 0.name
x 1.amount, x 2.phone
y0 (organizations) y2(finances)
x 2.phone
x1.gid,x 0.name x 0.name x 1.amount, x 2.phone
x 0.name
x 1.amount y2.phone
y1(fundings) y0.code y0.year y2.finId
y2.budget
x1.gid,x 0.name
x1.gid
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
Information Systems Group Leila Jalali, Candidacy Exam
32. 2. Annotating the Graph (cont.)
Apply the rules until no more rules can be applied
x1.gid,x 0.name
x 0.name
x 1.amount, x 2.phone
y0 (organizations) y2(finances)
x 1.amount, x 2.phone x1.gid,x 0.name x 2.phone
x1.gid,x 0.name x 0.name x 1.amount, x 2.phone
x 0.name
x 1.amount y2.phone
y1(fundings) y0.code y0.year y2.finId
x 1.amount, x 2.phone y2.budget
x1.gid,x 0.name
x1.gid
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
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33. 3. Generation of Transformation Queries
Generate the query fragment:
The for each clause is converted to a query fragment:
Information Systems Group Leila Jalali, Candidacy Exam
34. 3. Generation of Transformation Queries
Perform a depth-first traversal on the Graph
x1.gid,x 0.name
x 0.name
x 1.amount, x 2.phone
y0 (organizations) y2(finances)
x 1.amount, x 2.phone
x1.gid,x 0.name x 2.phone
x1.gid,x 0.name x 0.name x 1.amount, x 2.phone
x 0.name
x 1.amount y2.phone
y1(fundings) y0.code y0.year y2.finId
x 1.amount, x 2.phone y2.budget
x1.gid,x 0.name
x1.gid
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
Information Systems Group Leila Jalali, Candidacy Exam
35. 3. Generation of Transformation Queries
x 0.name x1.gid,x 0.name
y0 (organizations) x 1.amount, x 2.phone
y2(finances)
x 1.amount, x 2.phone
x1.gid,x 0.name x 2.phone
x1.gid,x 0.name x 0.name x 1.amount, x 2.phone
x 0.name
x 1.amount y2.phone
y1(fundings) y0.code y0.year y2.finId
x 1.amount, x 2.phone y2.budget
x1.gid,x 0.name
x1.gid
y1.fid y1.finId x0.name
x2.phone
x1.amount
x1. gid
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36. Finally we have the Query:
Information Systems Group Leila Jalali, Candidacy Exam
37. Clio: Conclusion
Providing tools that help in automating and managing the
problem of Data Conversion
The key contributions of Clio:
Schema mapping generation
Mapping as a query discovery problem
Capable of mapping between relational and nested schemas
Query generation for data exchange
SQL, XQuery, XSLT, generating Skolems,...
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39. Back ups
Clio Requirements
Complex mappings: using association
Definitions:
Mapping language
Paths
Schema&Types
Dominance
Query Generation Challenges,the problem of Recursion in XML schema
Nested Referential Integrity (NRI) constraints
The Chase
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40. the Clio project- overview of the requirements
Q
Schema Mapping Target
Source
schema T
schema S
“conforms to” “conforms to”
no assumptions about the schemas
data A general mapping language
Mapping at different levels of granularities
Incremental mapping algorithms
Capable of mapping between relations schemas and nested schemas
Information Systems Group Leila Jalali, Candidacy Exam
41. Formalize correspondences
Companies Using tuple generating dependency(tgd):
Name v1 Organizations
Address Code ∀n,d,y Companies(n,d,y) →
v1:
∃y',F Organizations(n,y',F))
Year Year
f1 Grants
Fundings
Gid v2 FId
Recipient FinId v3:
∀g, r, a, s, m Grants(g,r,a,s,m) →
Amount
∃f,p Finances(f,a,p)
f2 Supervisor v3 Finances f4
f3 Manager
∀c, e, p Contacts(c,e,p) →
FinId
Contacts Budget v4:
Cid Phone ∃f,b Finances(f,b,p)
Email
Phone
v4
∀n,d,y,g,a,s,m Companies(n,d,y),Grants(g,n,a,s,m) →
v2:
∃ y',F,f Organizations(n,y’,F), F(g,f )
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42. Correspondences alone are not enough
How individual data values should be connected in the target?
Companies
Name v1 Organizations
Address Code
Year Year
f1 Grants
Fundings
Gid v2 FId
Recipient FinId
Amount
f4 Companies Organizations
f2 Supervisor v3 Finances Name Address Year Code Year Fundings
f3 Manager FinId MS SA 1976
FId FinId
Contacts Budget AT&T TX 1980
f3 IBM NY 1955 MS
Cid Phone
Email Grants AT&T
Phone
v4 GId Amt
Rec.t IBM
301 MS 30
301
302 MS 40
303 IBM 30 302
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43. More complex mappings are needed
Companies
Name v1 Organizations
Address Code The "association" between companies and grants in
Year Year the source is suggested by f1 (a foreign key)
f1 Grants
Fundings
Gid v2 ∀n,d,y,g,a,s,m Companies(n,d,y),Grants(g,n,a,s,m) →
FId
Recipient FinId ∃ y',F,f Organizations(n,y’,F), F(g,f )
Amount
f2 Supervisor v3 Finances f4
f3 Manager FinId
Contacts Budget Companies
Organizations
Name Address Year
Cid Phone
MS SA 1976 Code Year Fundings
Email AT&T TX 1980
v4 FId FinId
Phone f3 IBM NY 1955
MS 301
Grants
302
GId Rec.t Amt
301 MS 30 AT&T
302 MS 40 IBM 303
303 IBM 30
Information Systems Group Leila Jalali, Candidacy Exam
44. Yet more complex...
Companies
Name v1 Organizations ∀g, r, a, s, m Grants(g,r,a,s,m) →
v3:
Address Code ∃f,p Finances(f,a,p)
Year Year
f1 Grants
Fundings
Gid v2 FId ∀n,d,y,g,a,s,m Companies(n,d,y),Grants(g,n,a,s,m) →
Recipient FinId
∃y',F,f, p Organizations(n,y',F), F(g,f), Finances(f,a,p)
Amount
f2 Supervisor v3 Finances f4
f3 Manager FinId
Contacts Budget • Three tuples are generated for each pair of related
Cid Phone companies and grants
Email • The mapping specifies that there exist an f, appearing in
Phone
v4 two places, without saying what its value must be
Information Systems Group Leila Jalali, Candidacy Exam
45. Yet more complex... Companies
Name v1 Organizations
v4 ∀c, e, p Contacts(c,e,p) → Address Code
Year
∃f,b Finances(f,b,p) f1 Grants
Year
Fundings
Gid v2 FId
• How do we obtain the phone to be Recipient FinId
put in finances? Amount
• Is it the supervisor's one or the f2 Supervisor Finances f4
v3
manager's? f3 Manager FinId
• FKs suggest either (or even both) Contacts Budget
• Human intervention is needed to choose Phone
Cid
Email
Phone
v4
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46. The Mapping Language- Syntax
foreach x1 in g1, . . . , xn in gn xi in gi (generator)
where B1 •xi variable
•gi set (either the root or a set
exists y1 in g'1, . . . , ym in g'm nested within it)
where B2
B1 conjunction of equalities over
with e1 = e'1 and . . . and ek = e'k
the xi variables
The example:
e1 = e'1 … equalities between a
foreach c in companies, g in grants
source expression and a target
where c.name=g.recipient expression
exists o in organizations,
f in o.fundings,
i in finances
where f.finId = i.finId
with o.code = c.name
and f.fId = g.gId
and i.budget = g.amount
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47. Primary and Relative paths
Primary path (given a schema root R, that is a first level
element in the schema):
x1 in g1, x2 in g2, …, xn in gn
where g1 is an expression on R (just R?), gi (for i ≥ 2) g1 is an expression
on xi-1
Examples
c in companies
o in organizations, f in o.fundings
Relative path with respect to a variable x
x1 in g1, x2 in g2, …, xn in gn
where g1 is an expression on x, gi (for i ≥ 2) g1 is an expression on xi-1
Example
f in o.fundings
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48. Schema and types
A schema: a sequence of labels(roots) each with associated
type, defined by this grammar:
Complex types
Atomic types A set type
All and choice model-groups
Repeated elements
Instances: associates each schema root a value
A value for atomic types
setID
An unordered tuple of pairs
A pair
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50. the data exchange problem
Information Systems Group Leila Jalali, Candidacy Exam
51. Query generation challenges
1. Creation of New Values in the Target
Optional: Null
name
salary
spouse
dateofbirth
Not nullable: one-to-one Skolem function But if it is emp ID
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52. Query generation challenges
1. Creation of New Values in the Target
Refrential constraints
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54. Query generation challenges
3. Value Creation interacts with Grouping
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55. Recursion in XML schema
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56. the Chase
Given as association, repeatedly applying a chase rule to the "current"
association (initialed as the input one)
If there is a NRI constraint
foreach X exists Y where B
such that the "current" association contains X and does not contain a Y that
satisfies B
then add Y to the generators and B to the where clause
Example. If we start with
from g in grants
then we have to add various components and obtain
from g in grants, c in companies,
s in contacts, m in contacts
where g.recipient = c.name and
g.supervisor = s.cid and
g.manager = m.cid
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57. Clio: Analysis and Conclusion
Termination and Complexity of the Chase:
the Chase with general dependecies may not be terminate
Cyclic dependencies
NRIs: A weakly acyclic set
the number of Chase steps is polynomial
Conculsion
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58. Clio mapping
A Clio mapping: for each AS exists AT with E
AS , AT : logical associations (on source and target, resp.)
E a conjunction of equalities:
for each correspondence v in C covered by <AS , AT> ,
E includes the equality h(eS )=h(eT ) which is the result of the coverage,
for one of the coverages
Information Systems Group Leila Jalali, Candidacy Exam
59. Structural Association
Structural association:
− from P (with P primary path)
Starts from the Root of the schema
Companies
Name Organizations
Address Code
Year Year
Grants Fundings
Gid FId
Recipient FinId
Amount
Supervisor Finances
Manager FinId
Contacts Budget
Information Systems Group Cid Leila Jalali, Phone
Candidacy Exam
60. Nested Referential Integrity (NRI) constraints
The basis for discovery of associations: capture relation foreign key and
referential constraints as well as XML keyref constraint:
foreach P1 exists P2 where B
o in organizations, f in o.fundings
P1 is a primary path f in o.fundings
Organizations:
P2 is a primary path or a relative path with respect to a
Code
variable in P1 Year
B is a conjunction of equalities Fundings:
FId
between an expression on a variable of P1
FinId
f4
and an expression on a variable of P2 Finances
foreach o in organizations, f in o.fundings FinId
exists i in finances Budget
where f.finId = i.finId Phone
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61. Logical Association
Logical association: semantic relationships between schema
elements
Obtained by starting with a structural association
and "chasing" NRI constraints
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62. Logical Association- the Chase
start with a structural association
Companies
Name v1 Organizations
Address Code
f1 Year Year
Grants Fundings
v2
Gid FId
Recipient FinId f2
Amount Finances
f2 Supervisor v3 f4
FinId
f3 Manager Budget
Contacts Phone
Cid
f3
Email v4
Phone
Information Systems Group Leila Jalali, Candidacy Exam
63. Logical Association Relationships
A2 dominates A1 (A1 ≤ A2 ) if
the from and where clauses of A1 are subsets of those of A2 (after
suitable renaming)
A2 : from g in grants, c in companies, s in contacts, m in contacts
where g.recipient = c.name and
g.supervisor = s.cid and
g.manager = m.cid
A1 : from g in grants, c in companies
where g.recipient = c.name
Information Systems Group Leila Jalali, Candidacy Exam
64. Mapping Generation Algorithm
Inputs: S , T , Correspondences AS : from c in companies
AT : fom o in organizations
Logical associations are meaningful combinations of correspondences
Generate all Logical Associations : AS , AT
Which correspondences can be interpreted together?
For each suitable pair <AS , AT>: find the correspondences covered by the pair
with some renaming <h,h‘>, Check for dominance
Generate Clio Mapping: foreach AS exists AT with W
W is the equality h(eS )=h(eT )
Add the Clio Mapping to the Set of Mappings
M: for each c in companies
Output: the set of Schema Mappings exists o in organizations
with c.name = o.code
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Editor's Notes
Providing tools that help in automating and managing the problem of Data Conversion use of Schema Mappings (specification to describe the relationship between data in two different schemas) To transform data between two different representations Schema Mappings to generate: A view to reformulates queries: Data Integration A code to transform data : Data Exchange
Contributions of the paper
Information about companies and grants…. Nested relational representation one can present both relational and xml schemas Schema S is a relational schema: with 3 tables : companies, grants and contacts The grant has grantidentifier, recipient which is the name of the company that receives, and the amount The green lines: referential constraints: foreign key or dependency The target is the XML schema: the funding that an organization receives is nested with the organization record Dashed arrows : Correspondences : the relationships between the schemas, may given by the schema matcher, or we can ask the user to draw these lines V1: the company name in the first schema referred to the organization code in the second schema Why there is no lines between year: 2 diff. concepts. The year. The time the company founded vs the time it had its first initial public offer Their approach does not care about how these correspondence are created, but consider about matchings are incompelete and sometimes incorrect For simplicity these 4 correcpondences are correct
Correspondence can be formally expressed using tuple generating dependency(tgd) Using shared variables: for each company there must be an organization whose code is the same as companies.name All the shared variables are underlined
For each x i in g i (generator) x i variable g i set (either the root or a set nested within it) where B 1 conjunction of equalities over the x i variables with e 1 = e' 1 … equalities between a source expression and a target expression The mapping as a source to target constraint: &quot;the result of Q T (over the target, projected as in the with-clause) must contain the result of Q S (over the source, projected as in the with-clause)&quot;
For each x i in g i (generator) x i variable g i set (either the root or a set nested within it) where B 1 conjunction of equalities over the x i variables with e 1 = e' 1 … equalities between a source expression and a target expression The mapping as a source to target constraint: &quot;the result of Q T (over the target, projected as in the with-clause) must contain the result of Q S (over the source, projected as in the with-clause)&quot;
Contributions of the paper
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
n ,d,y Companies( n ,d,y) → y',F Organizations( n ,y',F)) n ,d,y, g , a,s,m Companies( n ,d,y), Grants( g , n ,a,s,m) → y',F ,f Organizations( n ,y’ ,F), F( g ,f ) g, r, a , s, m Grants( g,r, a ,s,m) → f,p Finances(f, a ,p) c, e, p Contacts( c,e, p ) → f,b Finances(f,b, p )
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them
Contributions of the paper
The schema mapping specify how the data of two schemas relate to each other For data exchange an instance of the source schema must be transformed to an instance of the target schema Note the schema mapping migth not contain all the target values, and may not specify the grouping/ nested semantics for target data
When one schema is XML Clio can generate a data exchange query in Xquery or XSLT The paper describe how to generate Xquery , SQL is similar without having nested elements
Obvious relationships
Obvious relationships
Obvious relationships
finally
Annotation to facilitate generation of Skolem functions These source elements will be the arguments of the potential skolem functions
Every expression that a node acquires is propagated to its children if they do not already have it and if they are not equal to any of the source nodes. Annotation to facilitate generation of Skolem functions These source elements will be the arguments of the potential skolem functions
Annotation to facilitate generation of Skolem functions These source elements will be the arguments of the potential skolem functions
Annotation to facilitate generation of Skolem functions These source elements will be the arguments of the potential skolem functions
It is straightforward, Clio binds one variable to each term, and add the conditions in the where clause Noted it by Q S M1 It is not the complete query because it does not have the result yet It will be used repeatedly in the larger query
It will be used repeatedly in the larger query Starts at the target schema root in query graph , depth first traversal If a node is a complex type element (like y1 fundings) , the element is generated by visiting the children If the node is an atomic type, if it is linked to the source node (like y1.fid) , a simple element is created with the value equal to source, If it is an optional element, nothing generated If it is a nullable element, null value is generated else (like y1. finId) a value will be generated using a new Skolem function, with all arguments that annotate to the node (take care that all the nodes equal to this node receive the same Skolem function name) If it is a variable, For Where Return query produced, copy Q S M1 (the query fragment) rename all the variables, compare annotation with its parent variable, for each common expression correlated sub query generated
If it is a variable, For Where Return query produced, copy Q S M1 (the query fragment) rename all the variables, compare annotation with its parent variable, for each common expression correlated sub query generated
It will be used repeatedly in the larger query Starts at the target schema root in query graph , depth first traversal
The path in an NRI require matchings, to determine the variables in the path However it is exponential to the size of the path , which is often small . Some matching are not possible because of schema restrictions a Chase step can take exponential (in the worst case, it could be multiple ways of matching a variable in a path)
Providing tools that help in automating and managing the problem of Data Conversion Makes no assumption about the schemas, their relationships or how they were created The mapping language is more general than TSIMMIS, Information Manifold Able to map between relational schemas and nested schemas Mapping at different levels of granularities: fine grained mappings such as translating the salary in francs to dollars, boarder concept (documents from one schema to the other schema) Incremental mapping algorithms: sometimes the complete mapping is not the goal (we want a single concept to be mapped) or we have partial knowledge of the schemas so we want to support incomplete mappings as well
Correspondence can be formally expressed using tuple generating dependency(tgd) Using shared variables: for each company there must be an organization whose code is the same as companies.name All the shared variables are underlined
Correspondences alone do not specify how individual values should be connected in the target For e.g. fundings is nested inside organization which means there is a semantic association between them We should look for the association between organization information and funding information in the source to know about the association in the target One such association is f1, each grant is associated with a company. Thus in target we can associate with each organization a set of fundings The algorithm use logical inference to find all associations represented by referential constraints and a schema relational and nesting structure
F is a set identifier, set of fundings that an organizations tuple has This mapping tells us that if there is a pattern in source data what must be true in the target, if we join grant and a company there must be organization with the name of company as its source, and fundings inside it, with fid equal gid.
V3 does not recognize that grant amounts are associated with specific gids. Using f4 the better mapping would be this
To complete our example, consider v4, there are two ways to associate the grant amount(budget) to the phone, Using f2 supervisor phone or f3 manager phone
Consider this simple mapping An employee in the source has atomic elements A ,B, C , Employee record in the targer: A’, B’, C’, and an extra elemnt E’ A and B are mapped to A’, B’. But E’ and C’ left unmapped. Now what should be the values for C’, E’: 1. When neither used in the schema as contraints: creating null value is sufficient 2. If E’ is a key in target : not nullable, not optional like employee id: create values using one-to-one Skolem function, E’ depends only on A and B not on C
E’ is the refrence page 224
Target schema contains two levels.
One reason for XSLT is that there are no efficient, robust implementation of Xquery today I give the size of the largest schemas and some idea of compilation/interpretation times
The path in an NRI require matchings, to determine the variables in the path However it is exponential to the size of the path , which is often small . Some matching are not possible because of schema restrictions a Chase step can take exponential (in the worst case, it could be multiple ways of matching a variable in a path)
Primary path (given a schema root R, that is a first level element in the schema): x 1 in g 1 , x 2 in g 2 , …, x n in g n where g 1 is an expression on R (just R?), g i (for i ≥ 2) g 1 is an expression on x i-1 Examples c in companies o in organizations, f in o.fundings Relative path with respect to a variable x x 1 in g 1 , x 2 in g 2 , …, x n in g n where g 1 is an expression on x, g i (for i ≥ 2) g 1 is an expression on x i-1 Example f in o.fundings Given as association, repeatedly applying a chase rule to the &quot;current&quot; association (initialed as the input one) If there is a NRI constraint foreach X exists Y where B such that the &quot;current&quot; association contains X and does not contain a Y that satisfies B then add Y to the generators and B to the where clause Example. If we start with from g in grants then we have to add various components and obtain from g in grants, c in companies, s in contacts, m in contacts where g.recipient = c.name and g.supervisor = s.cid and g.manager = m.cid
NRI capture relations foreign key and referential constraints as well as xml keyref constraints Referential integrity is essential in this approach as the basis for the discovery of &quot;associations&quot; Given the nested model, they need a rather complex definition Primary path (given a schema root R, that is a first level element in the schema): x 1 in g 1 , x 2 in g 2 , …, x n in g n where g 1 is an expression on R (just R?), g i (for i ≥ 2) g 1 is an expression on x i-1 Examples c in companies o in organizations, f in o.fundings Relative path with respect to a variable x x 1 in g 1 , x 2 in g 2 , …, x n in g n where g 1 is an expression on x, g i (for i ≥ 2) g 1 is an expression on x i-1 Example f in o.fundings
Primary path (given a schema root R, that is a first level element in the schema): x 1 in g 1 , x 2 in g 2 , …, x n in g n where g 1 is an expression on R (just R?), g i (for i ≥ 2) g 1 is an expression on x i-1 Examples c in companies o in organizations, f in o.fundings Relative path with respect to a variable x x 1 in g 1 , x 2 in g 2 , …, x n in g n where g 1 is an expression on x, g i (for i ≥ 2) g 1 is an expression on x i-1 Example f in o.fundings Given as association, repeatedly applying a chase rule to the &quot;current&quot; association (initialed as the input one) If there is a NRI constraint foreach X exists Y where B such that the &quot;current&quot; association contains X and does not contain a Y that satisfies B then add Y to the generators and B to the where clause Example. If we start with from g in grants then we have to add various components and obtain from g in grants, c in companies, s in contacts, m in contacts where g.recipient = c.name and g.supervisor = s.cid and g.manager = m.cid
Logical association: An association obtained by &quot;chasing&quot; constraints (starting with a structural or a user association) Logical associations are meaningful combinations of correspondences A set of correspondences can be interpreted together if there are two logical associations (one in the source and one in the target) that cover them