The document discusses metamodeling and the Model Driven Architecture (MDA). It provides an overview of model driven engineering and metamodeling. Specifically, it discusses how metamodels define the structure of models through concepts like classes and relationships. The Model Driven Architecture uses metamodels and modeling to develop software systems from models.
Best practices to deliver data analytics to the business with power biSatya Shyam K Jayanty
Get your data to life with Power BI visualization and insights!
With the changing landscape of Power BI features it is essential to get hold of configuration and deployment practices within your data platform that will ensure you are on-par with compliance & security practices. In this session we will overview from the basics leading into advanced tricks on this landscape:
How to deploy Power BI?
How to implement configuration parameters and package BI features as a part of Office 365 roll out in your organisation?
What are newest features and enhancements on this Power BI landscape?
How to manage on-premise vs on-cloud connectivity?
How can you help and support the Power BI community as well?
Having said that within the objectives of this session, cloud computing is another aspect of this technology made is possible to get data within few clicks and ticks to the end-user. Let us review how to manage & connect on-premise data to cloud capabilities that can offer full advantage of data catalogue capabilities by keeping data secure as per Information Governance standards. Not just with nuts and bolts, performance is another aspect that every Admin is keeping up, let us look into few settings on how to maximize performance to optimize access to data as required. Gain understanding and insight into number of tools that are available for your Business Intelligence needs. There will be a showcase of events to demonstrate where to begin and how to proceed in BI world.
- D BI A Consulting
consulting@dbia.uk
Accelerate Your ML Pipeline with AutoML and MLflowDatabricks
Building ML models is a time consuming endeavor that requires a thorough understanding of feature engineering, selecting useful features, choosing an appropriate algorithm, and performing hyper-parameter tuning. Extensive experimentation is required to arrive at a robust and performant model. Additionally, keeping track of the models that have been developed and deployed may be complex. Solving these challenges is key for successfully implementing end-to-end ML pipelines at scale.
In this talk, we will present a seamless integration of automated machine learning within a Databricks notebook, thus providing a truly unified analytics lifecycle for data scientists and business users with improved speed and efficiency. Specifically, we will show an app that generates and executes a Databricks notebook to train an ML model with H2O’s Driverless AI automatically. The resulting model will be automatically tracked and managed with MLflow. Furthermore, we will show several deployment options to score new data on a Databricks cluster or with an external REST server, all within the app.
How to use Azure Machine Learning service to manage the lifecycle of your models. Azure Machine Learning uses a Machine Learning Operations (MLOps) approach, which improves the quality and consistency of your machine learning solutions.
Best practices to deliver data analytics to the business with power biSatya Shyam K Jayanty
Get your data to life with Power BI visualization and insights!
With the changing landscape of Power BI features it is essential to get hold of configuration and deployment practices within your data platform that will ensure you are on-par with compliance & security practices. In this session we will overview from the basics leading into advanced tricks on this landscape:
How to deploy Power BI?
How to implement configuration parameters and package BI features as a part of Office 365 roll out in your organisation?
What are newest features and enhancements on this Power BI landscape?
How to manage on-premise vs on-cloud connectivity?
How can you help and support the Power BI community as well?
Having said that within the objectives of this session, cloud computing is another aspect of this technology made is possible to get data within few clicks and ticks to the end-user. Let us review how to manage & connect on-premise data to cloud capabilities that can offer full advantage of data catalogue capabilities by keeping data secure as per Information Governance standards. Not just with nuts and bolts, performance is another aspect that every Admin is keeping up, let us look into few settings on how to maximize performance to optimize access to data as required. Gain understanding and insight into number of tools that are available for your Business Intelligence needs. There will be a showcase of events to demonstrate where to begin and how to proceed in BI world.
- D BI A Consulting
consulting@dbia.uk
Accelerate Your ML Pipeline with AutoML and MLflowDatabricks
Building ML models is a time consuming endeavor that requires a thorough understanding of feature engineering, selecting useful features, choosing an appropriate algorithm, and performing hyper-parameter tuning. Extensive experimentation is required to arrive at a robust and performant model. Additionally, keeping track of the models that have been developed and deployed may be complex. Solving these challenges is key for successfully implementing end-to-end ML pipelines at scale.
In this talk, we will present a seamless integration of automated machine learning within a Databricks notebook, thus providing a truly unified analytics lifecycle for data scientists and business users with improved speed and efficiency. Specifically, we will show an app that generates and executes a Databricks notebook to train an ML model with H2O’s Driverless AI automatically. The resulting model will be automatically tracked and managed with MLflow. Furthermore, we will show several deployment options to score new data on a Databricks cluster or with an external REST server, all within the app.
How to use Azure Machine Learning service to manage the lifecycle of your models. Azure Machine Learning uses a Machine Learning Operations (MLOps) approach, which improves the quality and consistency of your machine learning solutions.
Power BI On AIR - Melissa Coates: "What You Need to Know to Administer Power BI"Bohdan Maherus
Session #1 - Melissa Coates: "What You Need to Know to Administer Power BI"
YouTube channel: https://www.youtube.com/channel/UCOAWiig6JH1i8MqcniEVbTg
LinkedIn: https://www.linkedin.com/groups/8933736/
Melissa Coates
Owner of Coates Data Strategies. Microsoft Data Platform MVP.
Data architect with a background in data warehousing and business intelligence. Her current professional focus is enterprise-level Power BI governance, deployment, security, and administration. As the owner of Coates Data Strategies, Melissa produces training and consults to help companies strengthen and sustain their data-driven initiatives. Melissa is a big supporter of the technical community, and has been a Microsoft Data Platform MVP since 2013.
Topic: "What You Need to Know to Administer Power BI".
The Power BI administrator is a very high privilege role. Some administration activities apply consistently for every organization, whereas others depend on how Power BI is being used for self-service and corporate business intelligence initiatives. Each organization's needs related to security, governance, auditing, and data management influence the scope of responsibilities for a Power BI administrator.
Data options with hyperion planning and essbasefinitsolutions
View(active tab)
Edit
One of the great things about Essbase and Hyperion Planning is how you can design and configure them to fit virtually any analytic reporting need your business has. Well, the same goes for data movement into and within these tools. For our upcoming webinar we will explore several creative options around moving data for Planning and Essbase. We will review Hyperion Planning's "Map to Reporting Application", EPMA's Data Synchronization, FDM, XREF, XWRITE, Partitions, native Load Rules, HFM's Extended Analytics, EAL, and a few other options for Hyperion Planning and Essbase. We will then further explore moving data between other applications and other products like HFM and various reporting solutions. During this webinar you will see several methods demonstrated including:
Planning's Map to Reporting
DATAEXPORT
Load Rules
Partitions
If you have ever thought about using a data movement feature just because it is available we will take a look into why this may not always be the best choice.
Presenter: Cindy Eichner
Unable to attend Oracle OpenWorld to learn about the latest developments in Oracle EPM Cloud? It can be difficult to keep abreast of all the changes in this evolving landscape, but we’ve got you covered.
In our webinar, Perficient’s Oracle EPM leadership explored the current cloud offerings and what’s around the corner. Whether you are in IT or finance, your colleagues are driving digital transformation by including cloud in their performance management strategy.
Discussion covered:
-In-depth review of the Oracle EPM Cloud suite
-How the products can be integrated
-How SaaS products compare to on-premises editions
-Benefits of cloud strategies
Slides from AIS and Microsoft's half-day session on the recently-announced Windows Azure Infrastructure as a Service (IaaS) offering. After a brief overview of the Azure Platform as a Service (PaaS) model, we will focus on key IaaS concepts. Additionally, we will walk you through a number of scenarios enabled by Azure IaaS and several demonstrations.
Agenda:
Overview of Windows Azure Platform
Azure IaaS
Why IaaS?
IaaS Core Concepts
Supported Applications
Azure Virtual Machines
Disk Mobility
VM export / Import
Availability
Azure Virtual Network
SSAS, MDX , Cube understanding, Browsing and Tools information Vishal Pawar
Why we need SSAS Cube
What is SSAS Cube
Way to access Cube
What is Dimension and Attributes
QHP Dimension and Attributes
Process Flow and QHP Cube Browsing
MDX Basics
MDX Tools
Comparison of Queries Written in T-SQL and MDX with Construct
MDX –How to add where condition
FDMEE versus Cloud Data Management - The Real StoryJoseph Alaimo Jr
Are you considering or have you recently purchased an Oracle EPM Cloud Service? If so, you've likely heard about Cloud Data Management and how it can satisfy all of your data and master data needs. Sounds like Nirvana, right? Not so fast.
This presentation explores the capabilities of Cloud Data Management and its supporting technologies as well as its on-premises counterpart, FDMEE. It weighs the pros and cons of each option, including software and hardware costs, functionality, and sustainability.
State of mago3D, An Open Source Based Digital Twin PlatformSANGHEE SHIN
I gave this talk at the FOSS4G Thailand 2019 which was held at Chulalongkorn University, Bangkok on 4th Nov 2019. I talked about the recent achievements and improvements of mago3D project, an open source based Digital Twin platform. mago3D(http mago3d.com) is relatively new project first released in July 2017. The ultimate goal of mago3D is developing an open source based digital twin platform that can replicate and simulate the real world objects, processes, and phenomena on web environment. mago3D has been used in various industry sectors including ship building, urban management, indoor data management, and national defense. In this talk I showcased several real projects that used the mago3D and shared what I learnt from these projects. Also I introduced new features and future plan of mago3D.
Cloud-Native Integration with Apache Camel on Kubernetes (Copenhagen October ...Claus Ibsen
Cloud-native applications of the future will consist of hybrid workloads: stateful applications, batch jobs, microservices, and functions, wrapped as Linux containers and deployed via Kubernetes on any cloud.
In this session, we will explore key challenges with function interactions and coordination, addressing these problems using Enterprise Integration Patterns (EIP) and modern approaches with the latest innovations from the Apache Camel community:
- Apache Camel 3
- Camel K
- Camel Quarkus
Apache Camel is the Swiss army knife of integration, and the most powerful integration framework. In this session you will hear about the latest features in the brand new 3rd generation.
Camel K, is a lightweight integration platform that enables Enterprise Integration Patterns to be used natively on any Kubernetes cluster. When used in combination with Knative, a framework that adds serverless building blocks to Kubernetes, and the subatomic execution environment of Quarkus, Camel K can mix serverless features such as auto-scaling, scaling to zero, and event-based communication with the outstanding integration capabilities of Apache Camel.
We will show how Camel K works. We'll also use examples to demonstrate how Camel K makes it easier to connect to cloud services or enterprise applications using some of the 300 components that Camel provides.
Oracle Database Lifecycle Management using Enterprise Manager 12c (Release 4)
Learn about the features from Provisioning to Compliance and Everything in between you need to maintain Database environments in the regular or private database cloud.
Updated lifecycle management, improved analytics and support, and the option of Kubernetes — VMware vSphere® 7 is the biggest re-platform of vSphere in years. Learn more about the most significant vSphere evolution in a decade.
Learn more: http://ms.spr.ly/6005TmX9B
Power BI On AIR - Melissa Coates: "What You Need to Know to Administer Power BI"Bohdan Maherus
Session #1 - Melissa Coates: "What You Need to Know to Administer Power BI"
YouTube channel: https://www.youtube.com/channel/UCOAWiig6JH1i8MqcniEVbTg
LinkedIn: https://www.linkedin.com/groups/8933736/
Melissa Coates
Owner of Coates Data Strategies. Microsoft Data Platform MVP.
Data architect with a background in data warehousing and business intelligence. Her current professional focus is enterprise-level Power BI governance, deployment, security, and administration. As the owner of Coates Data Strategies, Melissa produces training and consults to help companies strengthen and sustain their data-driven initiatives. Melissa is a big supporter of the technical community, and has been a Microsoft Data Platform MVP since 2013.
Topic: "What You Need to Know to Administer Power BI".
The Power BI administrator is a very high privilege role. Some administration activities apply consistently for every organization, whereas others depend on how Power BI is being used for self-service and corporate business intelligence initiatives. Each organization's needs related to security, governance, auditing, and data management influence the scope of responsibilities for a Power BI administrator.
Data options with hyperion planning and essbasefinitsolutions
View(active tab)
Edit
One of the great things about Essbase and Hyperion Planning is how you can design and configure them to fit virtually any analytic reporting need your business has. Well, the same goes for data movement into and within these tools. For our upcoming webinar we will explore several creative options around moving data for Planning and Essbase. We will review Hyperion Planning's "Map to Reporting Application", EPMA's Data Synchronization, FDM, XREF, XWRITE, Partitions, native Load Rules, HFM's Extended Analytics, EAL, and a few other options for Hyperion Planning and Essbase. We will then further explore moving data between other applications and other products like HFM and various reporting solutions. During this webinar you will see several methods demonstrated including:
Planning's Map to Reporting
DATAEXPORT
Load Rules
Partitions
If you have ever thought about using a data movement feature just because it is available we will take a look into why this may not always be the best choice.
Presenter: Cindy Eichner
Unable to attend Oracle OpenWorld to learn about the latest developments in Oracle EPM Cloud? It can be difficult to keep abreast of all the changes in this evolving landscape, but we’ve got you covered.
In our webinar, Perficient’s Oracle EPM leadership explored the current cloud offerings and what’s around the corner. Whether you are in IT or finance, your colleagues are driving digital transformation by including cloud in their performance management strategy.
Discussion covered:
-In-depth review of the Oracle EPM Cloud suite
-How the products can be integrated
-How SaaS products compare to on-premises editions
-Benefits of cloud strategies
Slides from AIS and Microsoft's half-day session on the recently-announced Windows Azure Infrastructure as a Service (IaaS) offering. After a brief overview of the Azure Platform as a Service (PaaS) model, we will focus on key IaaS concepts. Additionally, we will walk you through a number of scenarios enabled by Azure IaaS and several demonstrations.
Agenda:
Overview of Windows Azure Platform
Azure IaaS
Why IaaS?
IaaS Core Concepts
Supported Applications
Azure Virtual Machines
Disk Mobility
VM export / Import
Availability
Azure Virtual Network
SSAS, MDX , Cube understanding, Browsing and Tools information Vishal Pawar
Why we need SSAS Cube
What is SSAS Cube
Way to access Cube
What is Dimension and Attributes
QHP Dimension and Attributes
Process Flow and QHP Cube Browsing
MDX Basics
MDX Tools
Comparison of Queries Written in T-SQL and MDX with Construct
MDX –How to add where condition
FDMEE versus Cloud Data Management - The Real StoryJoseph Alaimo Jr
Are you considering or have you recently purchased an Oracle EPM Cloud Service? If so, you've likely heard about Cloud Data Management and how it can satisfy all of your data and master data needs. Sounds like Nirvana, right? Not so fast.
This presentation explores the capabilities of Cloud Data Management and its supporting technologies as well as its on-premises counterpart, FDMEE. It weighs the pros and cons of each option, including software and hardware costs, functionality, and sustainability.
State of mago3D, An Open Source Based Digital Twin PlatformSANGHEE SHIN
I gave this talk at the FOSS4G Thailand 2019 which was held at Chulalongkorn University, Bangkok on 4th Nov 2019. I talked about the recent achievements and improvements of mago3D project, an open source based Digital Twin platform. mago3D(http mago3d.com) is relatively new project first released in July 2017. The ultimate goal of mago3D is developing an open source based digital twin platform that can replicate and simulate the real world objects, processes, and phenomena on web environment. mago3D has been used in various industry sectors including ship building, urban management, indoor data management, and national defense. In this talk I showcased several real projects that used the mago3D and shared what I learnt from these projects. Also I introduced new features and future plan of mago3D.
Cloud-Native Integration with Apache Camel on Kubernetes (Copenhagen October ...Claus Ibsen
Cloud-native applications of the future will consist of hybrid workloads: stateful applications, batch jobs, microservices, and functions, wrapped as Linux containers and deployed via Kubernetes on any cloud.
In this session, we will explore key challenges with function interactions and coordination, addressing these problems using Enterprise Integration Patterns (EIP) and modern approaches with the latest innovations from the Apache Camel community:
- Apache Camel 3
- Camel K
- Camel Quarkus
Apache Camel is the Swiss army knife of integration, and the most powerful integration framework. In this session you will hear about the latest features in the brand new 3rd generation.
Camel K, is a lightweight integration platform that enables Enterprise Integration Patterns to be used natively on any Kubernetes cluster. When used in combination with Knative, a framework that adds serverless building blocks to Kubernetes, and the subatomic execution environment of Quarkus, Camel K can mix serverless features such as auto-scaling, scaling to zero, and event-based communication with the outstanding integration capabilities of Apache Camel.
We will show how Camel K works. We'll also use examples to demonstrate how Camel K makes it easier to connect to cloud services or enterprise applications using some of the 300 components that Camel provides.
Oracle Database Lifecycle Management using Enterprise Manager 12c (Release 4)
Learn about the features from Provisioning to Compliance and Everything in between you need to maintain Database environments in the regular or private database cloud.
Updated lifecycle management, improved analytics and support, and the option of Kubernetes — VMware vSphere® 7 is the biggest re-platform of vSphere in years. Learn more about the most significant vSphere evolution in a decade.
Learn more: http://ms.spr.ly/6005TmX9B
Meta-modeling: concepts, tools and applicationsSaïd Assar
Presentation made as a tutorial at the rcis2015 conference in Athens, Greece, on May 13, 2015.
Video recording available online on IEEE Education (http://www.computer.org/web/computingnow/education)
In this paper we present an approach of Model Versioning and Model Repository in context of Living
Models view. The idea of Living Models is a step forward from Model Based Software Development
(MBSD) in a sense that there is tight coupling between various artifacts of software development process.
These artifacts include System Models, Test Models, Executable artifacts etc. We explore the issues of
storage (import/export) of model elements into repository, inputs of cross link information, version
management and system analysis. The modeling environment in which these issues will be discussed is a
heterogeneous modeling environment, where different models types and different modeling tools are used
in the development process. An overview of the tool architecture is also presented..
Advanced Software Engineering course - Guest Lecture
Weaving Models
This presentation has been developed in the context of the Advanced Software Engineering course at the DISIM Department of the University of L’Aquila (Italy).
http://www.di.univaq.it/malavolta
When talking about modeling, I think there will be a bundle of terms that will come to our mind, UML, domain driven development, DSL, forward/reverse enginerring, MDD, MDA, BPMN. These technology or methodology have been there for years; And obviously, modeling has proven itself to provide value by improving communication, business-alignment, quality, and productivity. Its applicability includes a number of disciplines such as analysis, design, or development. But why aren’t we all doing Model Driven Development yet?
Evolution in the Large and in the Small in Model-Driven DevelopmentAlfonso Pierantonio
Model Driven Engineering (MDE) is increasingly gaining acceptance in the development of software systems as a mean to leverage abstraction and render business logic resilient to technological changes. Coordinated collections of models and modeling languages are used to describe
applications on different abstraction levels and from different perspectives. In general, both models and metamodels are not preserved from the evolutionary pressure which inevitably affects almost any artifacts, possibly causing a cascade of adaptations which severely affects the modeling languages or the model population.
This talk analyzes the different kinds of co-adaptations which are required, distinguishing among co-evolution in the large and in the small. In particular, the coupling between models and metamodels implies that when a metamodel undergoes a modification, the conforming models require to be accordingly co-adapted. Analogously, whenever a new version of a model is produced, the generated application may require an explicit adaptation of the generated artifacts, especially when specific
assets are not directly reflected by the models and transformations, as for instance when dealing with serialized objects or with page content which is persistently stored in a database.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
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
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
2. Agenda
Model Driven Engineering
Metamodeling
Metamodeling in UML
Model Driven Architecture
OMG technologies for MDA
3. Enterprise Computing
Software is a complex and expensive product
Enterprise Computing adds further requirements:
Rapid change in organizational assets
Continuous evolving partnerships
Heterogeneous end-user clients
e.g., Fat clients, Web clients, Cellular Phone, iPod, DTT TV
Need for information ubiquity
e.g., in mobile/opportunistic networking, cross-network roaming
The only thing we can predict with confidence
about the future of software platforms is that things
we cannot predict will happen
4. Motivations
There is a strong pressure on the industry to
increase
Quality
Longevity
Reusability
of software products, while reducing costs
In order to fulfill this needs the productivity
of the software industry must be improved
Most increments in software productivity are
obtained increasing the abstraction level
5. Levels of abstractions
The history of software development is a
history of raising the level of abstraction
A stack of
• In Programming Languages
Programming Languages
• e.g., Assembler, C, C++, Visual C#, …
• In Software Architectures
3GL
• e.g., Client-Server, n-tier, SOA, …
Assembly • In Operative Systems
Language
• e.g., Virtual Machines, Middleware, Grid
0s and 1s • In data representation
• e.g., File, Database, XML, …
6. Model Driven Engineering (MDE)
MDE is a software development method for handling the
complexity of software platforms and related problems
Main feature: “Everything is a model”: the models are first-
class abstractions closer to domains than to algorithmic
ideas and concerns
A model is a simplified representation of a part (i.e. a
system) of the real world
Model
the modeling space
the real world isRepresentedBy
System
7. MDE: Approaches
MDE concepts can be applied in different
ways producing different approaches, each
one relying on a specific set of tools
8. Example: EMF
The Eclipse Modeling Framework (EMF) is a modeling
framework and code generation facility for building
applications based on a structured data model
From a model specification described in XMI, EMF
produces a set of Java classes for the model, a set of
adapter classes that enable viewing and editing the
model, and a basic editor
Models can be specified using annotated Java, UML,
XML documents, or modeling tools, then imported into
EMF
EMF itself provides the foundation for interoperability with
other EMF-based tools and applications
9. Approach
Requirement model:
defines the system in its
environment
Analysis and design
model: defines the
system architecture
Realization model: defines
how the system is built
Code of the system and
configuration artifacts
10. Models represent systems
A system S is a delimited part of the world considered
as a set of elements in interaction
A model M is a representation of a given system S
satisfying the principle of substitutability
Principle of substitutability: A model M is said to be a
representation of a system S for a given set of questions
Q if, for each question q in Q, the model M will provide
exactly the same answer that the system S would have
provided in answering the same q
The relation between a model and a system is called
representation of
14. Discuss
A model which describes a model is
called metamodel
Metamodels are descriptions of
descriptions: why are they important to
develop software?
15. Genesi, 2:19
So out of the ground the Lord God
formed every beast of the field and
every bird of the air, and brought them
to the man to see what he would call
them; and whatever the man called
every living creature, that was its name
17. Meta-model
A meta-model defines concepts
and their relationships thanks to
a class diagram
A meta-model only defines
structure (no semantics)
A model is an instance of a
meta-model if it respects the
structure defined by the meta-
model
The relation between a model
and its metamodel is called
conformance
The UML meta-model defines
the structure that all UML
models must have
19. System-Model-Metamodel Relations
The extraction of an element from system S to build a
model M is guided by a meta-model MM
MM plays the role of “filter” in the selecting from the
system S the elements for building the model M
metamodel
MM
conformsTo
repOf terminal
system S Representation of
model M
20. Exercises
Describe a metamodel for libraries
Describe a metamodel for cars
Describe a metamodel for TV sets
Describe a metamodel for CRC cards
Describe a metamodel for Petri Nets
22. Four Layered Architecture
Layer Description Example
Meta-metamodel Defines metamodel metaClass,
metaAttribute,
metaOperation
Metamodel An instance of meta- Class, attribute
Metamodel. Defines a operation, component
model
Model Language for describing an Employee
information domain. Defines
a set of related objects that
represent a concept
User object An instance of the model. :Sally
An example information
domain
24. 4-Layers Metamodeling
A model, or terminal model, (M1) is a
representation of a real object (in M0)
conforming to a metamodel (M2)
A metamodel (M2) is a representation of
a set of modelling elements (in M1)
conforming to a meta-metamodel (M3)
A meta-metamodel (M3) is a set of
modeling elements used to define
metamodels (M2 ed M3) conforming to
itself
N.B. M2, M2 and M3 are all “models”. We could
represent them using the same modeling language
(e.g., UML)
26. Meta-metamodel
• A metamodel describes information about models
• A meta-metamodel describes information about
metamodels
• Metamodels defined using the same meta-
metamodel
- can be compared for conformance
- can exchange information
- can be used by the same tools that understand
the meta-metamodel
27. Meta Object Facility (MOF)
• Enables meta-metamodeling of UML level
metamodels
• It defines a small set of concepts (such as
package, class, method, attribute…) that allow to
define and manipulate models of metadata (data
about data)
• All definitions are described using a subset UML
notation
30. MOF Key Abstract Classes
• ModelElement base Class of all M3-level Classes;
every ModelElement has a name
• Namespace base Class for all M3-level Classes that
need to act as containers
• GeneralizableElement base Class for all M3-level
Classes that support generalization (i.e. inheritance)
• TypedElement base Class for M3-level Classes such
as Attribute, Parameter, and Constant whose
definition requires a type specification
• Classifier base Class for all M3-level Classes that
(notionally) define types; examples of Classifier
include Class and DataType
31. Main Concrete Classes
• The key concrete classes (or meta-metaclasses) of MOF:
- Class
- Association
- Exception (for defining abnormal behaviours)
- Attribute
- Constant
- Constraint
31
32. Key associations
• Contains: relates a ModelElement to the Namespace that
contains it
• Generalizes: relates a GeneralizableElement to its
ancestors (superclass and subclass)
• IsOfType: relates a TypedElement to the Classifier that
defines its type
- An object is an instance of a class
• DependsOn : relates a ModelElement to others that its
definition depends on
- E.g. a package depends on another package
33.
34. UML Metaclasses used in class, package,
Meta
Element
component and deployment diagrams
Model
importedElement
ModelElement *
ownedElement
name
*
0..1
0..1
Relationship Feature
Link Comment Namespace Parameter Instance
visibility * GeneralizableElement
{ordered} defaultValue
* isRoot *
* kind
child isLeaf
Generalization * * * {ordered}
* isAbstract
specialization parent
discriminator
Object
type owner
AssociationEnd *
2..* type
connection participant specification Classifier
isNavigable
aggregation * * 1..*
Association multiplicity
0..1
StructuralFeature BehaviouralFeature
multiplicity
Package *
qualifier *
Attribute Operation Method
**
initialValue isAbstract specification body
Model
resident
Class Interface DataType Subsystem Node * Component
deploymmentLocation *
AssociationClass Primitive Enumeration ProgrammingLanguageType
1..*
EnumerationLiteral
34
35. A fragment of the UML Meta-Model
not self.isAbstract implies
self.allOperations->forAll(op |
self.allMethods->exists(m |
m.specification includes (op)))
36. Model Elements
• An element is an atomic
constituent of a model
Element
• Element is the top metaclass in
the metaclass hierarchy
ModelElement
• A model element is a named
entity in a model name
• It is the base for all modeling
metaclasses in UML
- All other modeling metaclasses are
either direct or indirect subclasses
of ModelElement
37. Features
• Feature is an abstract class Feature
that declares a behavioral or visibility *
{ordered}
structural property of
- an instance of a Classifier
- the Classifier itself owner
Classifier
• A behavioral feature refers to
a dynamic feature of a model StructuralFeature BehaviouralFeature
multiplicity
element
- E.g. operation or method Attribute Operation
**
Method
initialValue isAbstract specification body
• A structural feature refers to a
static feature of a model
element
- E.g. attribute
38. Classifier
• A classifier is an element that describes behavioral and
structural features
- E.g. class, data type, interface, component
• Classifier is an abstract class that
- declares a collection of Features, such as Attributes, Methods…
- has a name, which is unique in the Namespace enclosing the
Classifier
Feature
* Namespace
Classifier
visibility {ordered}
resident
Class Interface DataType Subsystem Node * Component
deploymmentLocation *
Primitive Structure Enumeration ProgrammingLanguageType
1..*
EnumerationLiteral
38
39. Classifier
• A classifier is a generalizable element and defines a
namespace
• It can have
- association ends
- parameters
- instances
Feature
Namespace Parameter Instance
visibility * GeneralizableElement
{ordered} defaultValue
isRoot *
kind
isLeaf
*
isAbstract
Object
* type owner
AssociationEnd
Classifier type
isNavigable participant specification
aggregation * * 1..*
multiplicity
40. Relationship
Relationship
• A relationship is a connection
among model elements Generalization
discriminator
• UML defines several relationships
such as: Association
- Association
- Generalization
• UML defines other types of
relationships that are not shown in
this diagram, such as: Class
- Dependency
- Flow
AssociationClass
41. Namespace
importedElement
• A namespace is a part of a ModelElement
name
*
ownedElement
model that contains a set of *
0..1
0..1
other model elements GeneralizableElement
Namespace
- E.g. Associations and isRoot
isLeaf
Classifiers isAbstract
- the name of an owned
model element is unique Classifier
within the namespace
• Namespace is an abstract Package *
metaclass and it subclasses
are Subsystem Model
- Classifier
- Package
42. Data Types
• UML data types include
- primitive built-in types (such as integer and string)
- definable enumeration types (such as Boolean whose literals are false
and true); enumerations are a user-defined data types whose instances
are literals (specified by the user)
• Programming languages data types
- are specified according to the semantics of a particular programming
language
- are not portable among languages (except by agreement among the
languages)
- do not map into other UML classifiers
DataType
Primitive Enumeration ProgrammingLanguageType
1..
*
EnumerationLiteral 42
43. Namespace
Classifier
Relationship
AssociationEnd
isNavigable
Package aggregation Class
Association multiplicity
BankSystem
Customer 1..2 * Account
accountNumber
StructuralFeature
balance Relationship
multiplicity overdraftLimit
withdraw
Attribute deposit Generalization
initialValue discriminator
BehaviouralFeature
Chequing Saving CreditCard
expiryDate
Method
Mapping of UML Models to
Metamodel Elements (Example)
45. Mapping Use Cases Model
to Metamodel Classifier
Classifier
UseCase
Actor
Relationship
Open file Generalization
discriminator
Ordinary User
Open file by Open file by
typing name browsing
Relationship
«extend» «include»
Include
Browse for file
System
Attempt to open file
Administrator
that does not exist
Relationship
Extend
47. Mapping State Machines
to Metamodel
State
State
StateVertex
SimpleState 0..1
PseudoState 0..1 +entry
Procedure
Closed Opening
Enter / pressButton Enter /
stop motor run motor forwards
closingCompleted Transition
*
openingCompleted
pressButton
0..1
Closing pressButton Open Event
Enter / Enter /
run motor in reverse stop motor
49. Example GeneralizableElement
Stereotype
{ordered} edge
<<geometry>
LinearShape 1..*
LineSegment
startPoint: Point
endPoint: Point
length : int
Path Line Polygon {startPoint <> endPoint}
length {edge->size=1} {edge->first.startPoint =
{length = edge->last.endPoint}
edge.length->sum}
RegularPolygon
{edge->forAll(e1,e2 |
e1.length = e2.length)}
Constraint
50. Model Driven Architecture (MDA)
MDA is a theory of software construction based on a set of
standards provided by the Object Management Group
MDA is a kind of MDE, that is domain engineering based on
developing models of software systems
The idea of MDA is to exploit a modeling language (such as
UML) as a programming language rather than just as a
support for design
This is not a new approach, it has notable ancestors:
Database schemas, generative programming, CASE
tools, WYSIWYG interfaces
51. MDA Base Components
MDA is based on a set of technologies that
allow:
Domain specification (domain specific
languages)
Modeling and metamodeling
Model transformations which support the
development of a system independently from:
platforms
platforms evolution
application domains
52. MDA Dimensions
Horizontal: different domains that are not more or less abstract
than others, like business or technology
E.g., marketing, engineering, sales, etc.
E.g., performance, security, fault tolerance etc.
Vertical: different level of abstraction of the same domain of a
system
From physical data, logical data models, middleware,
applications specifications, component assemblies, business
process models, business goals and strategies
Evolutionary: different systems, legacy, ongoing and upcoming,
as-is, as-was and as-could-be
This is technically similar to the horizontal dimension, but it is
more concerned with evolution-focused architectural rules
and modeling standards
53. MDA: the Big Picture
This picture gives
some insights
about the
technologies
constituting MDA
together with the
platforms, the
kinds of
application and
the domains of
application that it
addresses
54. Key Idea
create a source model
independent from platform implementation details
transform it in a target model richer of details than the
source model
Use of a proper transformation language
55. Basic Concepts
Model: a simplified representation of
a system
a part of a system
a set of system functionalities
Point of view: an abstraction technique with
the goal of highlighting some features of the
system
View: a representation of the system from the
perspective of a particular point of view
56. Basic Concepts
Refinement: it is related to a model M1
derived from model M2, where M1 has a
number of details greater than M2
M1 is a refinement of M2
Zooming: when we move from a model with a
different level of abstraction than another one
we have like a zoom-effect
System architecture: the specification of
Components
Connectors
Interaction rules of components by means of
connectors
57. Basic Concepts
Model driven: the use of models through all
the phases of software development
Platform independence: a property of a
model developed regardless of platform
details
Model transformation: the process of
converting one model into another one
concerning the same system
59. MDA: Core Standards
How do we can specify models, metamodels
and meta-metamodels?
OMG provides a set of standards supporting
such activities:
MOF 2.0
UML 2.3
XMI 2.0
CWM 1.1
QVT (Query View Transform)
60. MOF
MOF is an OMG standard to write metamodels
Can be used to model the abstract syntax of
Domain Specific Languages
Two flavors: EMOF (Essential MOF), CMOF
(complete MOF)
Kermeta is an extension to MOF allowing
executable actions to be attached to EMOF meta-
models, hence making it possible to also model a
DSL operational semantics and obtain an
interpreter for it
61. Metamodel
The correspondence between a model and a system is
defined by a metamodel:
Terminology: a collection of concepts with their properties
and relations
Assertions: a collection of additional rules by which
terminology elements can be constrained
62. Metamodels in Software Engineering
Currently, metamodels focus on:
Software process modeling (PIF)
Software product modeling (UML, CWM)
Many new fields are involved:
Model of cost
Model of resource consumption
Models of test, QoS, and software measurements
Requirements modeling (e.g. Use Cases)
Know-how modeling (e.g. Patterns)
Model validation
63. Meta-metamodel
A Meta-metamodel provides the language to define a
set of metamodels
Definition of abstract syntax: concepts, relationships, and
wellformedness rules
Definition of concrete syntax: shapes, layout, and physical
interchange formats
Definition of semantic domains: the abstract logical space in
which models find their meanings
Definition of mappings between domains
64. A Metamodel Language: MOF 2.0
The Meta-Object Facility (MOF) is an OMG
specification defining an abstract language and a
framework for specifying, constructing, managing,
and exchanging, technology-neutral metamodels
MOF defines a framework for implementing
repositories holding the persistent representation of
the metamodels
66. MOF: Features
MOF belongs to the MDA architecture
MOF meta-metamodel defined in itself
MOF 2.0 reuses a part of the UML 2.0 metamodel
MOF to XML mapping: OMG XMI (XML Metadata
Interchange) specification
MOF to Java mapping: JMI (Java Metadata
Interchange)
67. UML 2.0
UML is a language with a very broad scope covering a
large and diverse set of application domains
It is structured in two main parts:
Infrastructure
Superstructure
The UML 2.0 metamodel is aligned with the MOF 2.0
meta-metamodel for accomplishing the MDA-vision
In MDA all the four-layer models are defined by means
of UML or its extensions
69. The MDA 4-Layers Architecture (1/2)
The alignment between UML 2.0 and MOF 2.0
enables to see every UML 2.0 construct like an
instance of a MOF 2.0 element
In this way, we can see MOF 2.0 like the meta-
metamodel of UML2
Every UML 2.0 model is an instance of the
UML 2.0 metamodel
70. The MDA 4-Layers Architecture (2/2)
The UML metamodel stack is instantiated in
MDA by:
M3: MOF 2.0
M2: UML 2.0
M1: An UML 2.0 model at the “class” level
M0: A “real world” object represented by an UML 2.0
model at the “object” level
This stack represents the four-layers architecture
76. XMI
XML Metadata Interchange (XMI) is the OMG
technology for interchanging models in a
serialized form
XMI focuses on the interchange of MOF
metadata
i.e., metadata conforming to a MOF-based
metamodel
XMI can also be used for model representation
in model transformations
77. XMI
XMI uses XML for the transfer syntax and interchange format
XML Document Type Definitions (DTD) or Schema are specified
to enable the transfer and verification of:
UML-based models (e.g. mymodel.xmi, using uml.dtd or
uml.xsd)
MOF-based metamodels (e.g. uml.xmi, using mof.dtd or
mof.xsd)
Models based on other MOF-based meta-models (e.g.
mymodel.xmi using cwm.dtd or cwm.xsd)
78. CWM
The Common Warehouse Metamodel (CWM)
is the OMG standard for data warehouse
CWM is a specification for modeling metadata
for relational, non-relational, multi-dimensional,
and other objects found in a data warehouse
CWM can be used to extend UML for
supporting data warehouse environments
modeling transformations
structuring transformation specifications
The goal is to integrate development tools with
data deployment solutions
79. The Metadata Problem
CWM addresses the problems facing any company:
Many databases
Many repositories
Different schemas describing the “same” data
Moving data requires manual schema
transformation
80. CWM Integrates Data
CWM integrates existing data models
Maps to existing schemas
Supports automated schema generation
Supports automated database loading
The basis for data mining and OLAP (OnLine
Analytical Processing) across an enterprise
81. CWM Defines Metamodels
CWM Foundation OLAP
Relational Data Data Mining
Record Data Info Visualization
Multidimensional Data Business Nomenclature
XML Data Warehouse Process
Data Transformations Warehouse Operation
81
83. The CWM Metamodel
Operational Data
Warehouses
Extract Transform
Data Sources
Source Target Source Target
Source Target Source CWM Tool Target
Source Target Source Target
Pairwise Hub
(9 connections) Drill Down
(6 connections)
84. OMG Metamodel Architecture
M MOF: Class, Attribute,
I Meta-metamodel Operation,
D Layer (M3) Association
D
L
Standard Components E
W
Modeling Notation: UML A UML: Class, Attribute
R
Metadata Interchange: XMI Metamodel CWM: Table, Column
E
Layer(M2) ElementType, Attribute
Metadata API:
MOF IDL Mapping
JMI – MOF/Java Mapping A
P
P Metadata/Model Stock: name, price
CWM is based on L
I
Layer(M1)
UML C
A
MOF T
XMI I
O
User Data/Object
<Stock name=“IBM”
Layer (M0)
N price=“112”/>
85. The CWM Metamodel (1.0)
Management Warehouse Warehouse
Process Operation
Analysis Data Information Business
Transformation OLAP
Mining Visualization Nomenclature
Object Multi-
Resource (Core+Behavioral+ Relational Record XML
Relationships) Dimensional
Foundation Business Data Keys Type Software
Expressions
Information Types Index Mapping Deployment
Object Model Core Behavioral Relationships Instance
86. Metamodels, Syntaxes and Semantics
The UML language has been defined by means
of a MOF metamodel
In order to define a MOF metamodel we can use:
MOF to (formally) define the abstract syntax of a set of
modelling constructs
The natural language to (informally) describe some
parts of the semantics
The concrete syntax can be expressed by a
graphical notation or not
Example: a UML diagram can be represented in XML
by means of the XMI concrete syntax
In this case, the XMI file represents the UML diagram
87. Conclusions
MDA is a powerful family of technologies
Its basis is metamodeling
The technology of model transformations
(see next lecture) makes the approach
practical and powerful
It is important because it allows to manage
automatically the design and maintenance
problems typical of large software systems
88. Self test questions
What is a model?
What is a metamodel?
What is conformance to a metamodel?
What is MOF?
What is CWM?
Which are the main differences between
UML1.x and UML2.x?
89. References
Schmidt, Model-Driven Engineering. IEEE
Computer, 39:2(25-31), February 2006.
Frankel, Model Driven Architecture: Applying
MDA to Enterprise Computing, Wiley, 2003
Mellor, Scott, Uhl, Weise MDA Distilled:
Principles of Model-Driven Architecture,
Addison-Wesley, 2004
Mellor & Balcer, Executable UML, a foundation
for MDA, Addison-Wesley 2002
OMG, UML Infrastructure 2.3, 2010