This document discusses digital transformation in the rail industry using GS1 international standards. It begins with an overview of why European rail industries adopted GS1 standards for safety, efficiency and competitiveness. It then covers global examples of GS1 standard implementation in countries like Sweden, Germany, Poland, Austria, Switzerland, France, UK, Netherlands, Belgium, Italy, India and Australia. Key GS1 standards like identification codes for products, assets, locations and logistics are introduced. The presentation emphasizes how GS1 standards provide common languages and data sharing to automate and simplify supply chains in the rail industry.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
SAP Reference Architecture based on LeanIXLeanIX GmbH
At EA Connect Days 2018, Florian Masjosthusmann und Manuel Männle from itelligence tried to solve the follwing questions: How can you adapt your business from where it is today to where it needs to be tomorrow? How can you document a SAP centric IT-Landscape with LeanIX? How will your IT landscape evolve to meet the demands of the digital transformation with SAP? Based on LeanIX they developed a concept to describe your SAP centric IT-Landscape and Business Architecture. On this basis, an overall strategy for S/4 HANA Transformation can be developed which is oriented on best practices and SAP Reference Architectures. The itelligence EA-Approach is to connect the SAP Best Practice E2E Views and the Capability Views with die IT-Solution Landscape.
DAS Slides: Data Modeling Case Study — Business Data Modeling at KiewitDATAVERSITY
The document discusses data modeling efforts at Kiewit, a large construction company. It describes how Kiewit created conceptual data models to gain clarity on key business data assets and drive their data strategy. The models were organized by business capability and resonated well with stakeholders. This allowed Kiewit to identify data issues and gain insights that informed activities like data literacy training and system decommissioning planning. The modeling process highlighted differences between IT and business views of data and helped improve communication.
Data Governance Program Powerpoint Presentation SlidesSlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as different groups being unable to communicate and coordinate. It then contrasts manual versus automated approaches to data governance. The rest of the document provides details on key aspects of establishing a successful data governance program, including defining a framework, roles and responsibilities, and developing a roadmap for continuous improvement.
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
(ENT305) Develop an Enterprise-wide Cloud Adoption Strategy | AWS re:Invent 2014Amazon Web Services
Taking a "cloud first" approach requires a different approach than you probably had to consider for your initial few workloads in the cloud. You'll be diving into the deep end of hybrid environments, and that means taking a broad view of your IT strategy, architecture, and organizational design.
Through our experience in helping enterprises navigate this change, AWS has developed the Cloud Adoption Framework (CAF) to assist with planning, creating, managing, and supporting the shift. In this session, we cover how the CAF offers practical guidance and comprehensive guidelines to enterprise organizations, particularly around roles, governance, and efficiency.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
SAP Reference Architecture based on LeanIXLeanIX GmbH
At EA Connect Days 2018, Florian Masjosthusmann und Manuel Männle from itelligence tried to solve the follwing questions: How can you adapt your business from where it is today to where it needs to be tomorrow? How can you document a SAP centric IT-Landscape with LeanIX? How will your IT landscape evolve to meet the demands of the digital transformation with SAP? Based on LeanIX they developed a concept to describe your SAP centric IT-Landscape and Business Architecture. On this basis, an overall strategy for S/4 HANA Transformation can be developed which is oriented on best practices and SAP Reference Architectures. The itelligence EA-Approach is to connect the SAP Best Practice E2E Views and the Capability Views with die IT-Solution Landscape.
DAS Slides: Data Modeling Case Study — Business Data Modeling at KiewitDATAVERSITY
The document discusses data modeling efforts at Kiewit, a large construction company. It describes how Kiewit created conceptual data models to gain clarity on key business data assets and drive their data strategy. The models were organized by business capability and resonated well with stakeholders. This allowed Kiewit to identify data issues and gain insights that informed activities like data literacy training and system decommissioning planning. The modeling process highlighted differences between IT and business views of data and helped improve communication.
Data Governance Program Powerpoint Presentation SlidesSlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as different groups being unable to communicate and coordinate. It then contrasts manual versus automated approaches to data governance. The rest of the document provides details on key aspects of establishing a successful data governance program, including defining a framework, roles and responsibilities, and developing a roadmap for continuous improvement.
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
(ENT305) Develop an Enterprise-wide Cloud Adoption Strategy | AWS re:Invent 2014Amazon Web Services
Taking a "cloud first" approach requires a different approach than you probably had to consider for your initial few workloads in the cloud. You'll be diving into the deep end of hybrid environments, and that means taking a broad view of your IT strategy, architecture, and organizational design.
Through our experience in helping enterprises navigate this change, AWS has developed the Cloud Adoption Framework (CAF) to assist with planning, creating, managing, and supporting the shift. In this session, we cover how the CAF offers practical guidance and comprehensive guidelines to enterprise organizations, particularly around roles, governance, and efficiency.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
Hybrid integration reference architectureKim Clark
The ownership boundary of the typical enterprise now encompasses a much broader IT landscape. It is common to see that landscape stretch out to cloud native development platforms, software as a service, dependencies on external APIs from business partners, a mobile workforce and an ever growing range of digital channels. The integration surface area is dramatically increased and the integration patterns to support it are evolving just as quickly. These are the challenges we recognise as "hybrid integration". We will explore what a reference architecture for hybrid integration might look like, and how IBM's integration portfolio is growing and changing to meet the needs of digital transformation. This deck comes from the following article http://ibm.biz/HybridIntRefArch and is also described in this video http://ibm.biz/HybridIntRefArchYouTube
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
EA Intensive Course "Building Enterprise Architecture" by mr.danairatSoftware Park Thailand
This document outlines the agenda for a two-day course on building enterprise architecture. Day one covers introductions, current architecture challenges, the need for enterprise architecture, definitions of enterprise architecture, reference architecture frameworks, and group workshops. Day two covers maturity models, technology platforms, the TOGAF standard, cloud computing roadmaps, governance, and building a target architecture.
Data Mess to Data Mesh | Jay Kreps, CEO, Confluent | Kafka Summit Americas 20...HostedbyConfluent
Companies are increasingly becoming software-driven, requiring new approaches to software architecture and data integration. The "data mesh" architectural pattern decentralizes data management by organizing it around domain experts and treating data as products that can be accessed on-demand. This helps address issues with centralized data warehouses by evolving data modeling with business needs, avoiding bottlenecks, and giving autonomy to domain teams. Key principles of the data mesh include domain ownership of data, treating data as self-service products, and establishing federated governance to coordinate the decentralized system.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Data Mesh at CMC Markets: Past, Present and FutureLorenzo Nicora
This document discusses CMC Markets' implementation of a data mesh to improve data management and sharing. It provides an overview of CMC Markets, the challenges of their existing decentralized data landscape, and their goals in adopting a data mesh. The key sections describe what data is included in the data mesh, how they are using cloud infrastructure and tools to enable self-service, their implementation of a data discovery tool to make data findable, and how they are making on-premise data natively accessible in the cloud. Adopting the data mesh framework requires organizational changes, but enables autonomy, innovation and using data to power new products.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Business Intelligence (BI) and Data Management Basics amorshed
This document provides an overview of business intelligence (BI) and data management basics. It discusses topics such as digital transformation requirements, data strategy, data governance, data literacy, and becoming a data-driven organization. The document emphasizes that in the digital age, data is a key asset and organizations need to focus on data management in order to make informed decisions. It also stresses the importance of data culture and competency for successful BI and data initiatives.
The document is a slide deck from STKI, an Israeli market research and strategic analyst firm, discussing the impact of global events on IT budgets in Israel in 2022-2024. It notes that while IT budget increases were forecasted to be large in 2022, events like the war in Ukraine, inflation, interest rate hikes, and political instability have changed the outlook. IT budgets in Israel are still expected to rise 12-13% in 2022 but forecasts beyond that are difficult given uncertainties. Digital transformation alone is no longer sufficient - companies must undergo smart business transformations to deliver personalized, data-driven experiences to customers.
An overview of The Open Group IT4IT Reference Architecture. It is a vendor and product-agnostic value chain-based operating model for managing the business of IT. While providing guidance on the design, procurement and implementation of the functionality needed to run IT, it also enables the systematic tracking of the state of IT services across the service life-cycle using four value streams - Strategy to Portfolio, Request to Fulfill, Requirement to Deploy, and Detect to Correct.
Download presentation from http://opengroup.co.za/presentations
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
THE STRATEGIC BUSINESS MODEL CANVAS DESCRIBES THE CORRELATIONS BETWEEN THE COMPONENTS OF OUR STRATEGY. A JOURNEY FROM OUR CURRENT [TRANSIENT] COMPETITIVE ADVANTAGE TOWARDS THE STRATEGIC DESTINATION, WHERE OUR NEW [TRANSIENT] COMPETITIVE ADVANTAGE WILL BECOME EFFECTIVE.
This document discusses master data management (MDM). It begins by explaining that quality information is important for business strategies and that MDM bridges operational and information management solutions. It then discusses that MDM promotes efficiency, simplicity and data quality to improve business value. The document also outlines different MDM implementation styles including external databases, persistent databases, registries and composites. Finally, it provides an illustrative example of a persistent master data repository and recommends assessing the current state and selecting technologies to develop an MDM implementation roadmap.
This document discusses the importance and evolution of data modeling. It argues that data modeling is critical to all architecture disciplines, not just database development, as the data model provides common definitions and vocabulary. The document reviews the history of data management from the 1950s to today, noting how data modeling was originally used primarily for database development but now has broader applications. It discusses different types of data models for different purposes, and walks through traditional "top-down" and "bottom-up" approaches to using data models for database development. The overall message is that data modeling remains important but its uses and best practices have expanded beyond its original scope.
A Reference Process Model for Master Data ManagementBoris Otto
This document presents an overview of a reference process model for master data management. It includes an introduction discussing business requirements for master data and challenges in managing master data quality. It also describes the research methodology used to develop an iterative reference process model. The results section provides an overview of the reference process model and discusses its evaluation through three case studies. The conclusion recognizes the model's contribution in explicating the design process for master data management organizations.
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...Daeyoung Kim
In the Smart Cities Preliminary Report 2014 published by ISO/IEC JTC1, they have emphasized the importance of standardized, computer-recognizable, and actionable open data produced by various city resources. International standardization working groups such as ISO/IEC JTC1, JTC1/SC31 have been establishing new standards and also adopting existing standards for object identification, data modeling, and data acquisition which are the key features of the smart-city data platform.
ISO/IEC data standards have adopted many existing GS1 (Global Standards One) standards. GS1 is an international non-profit organization with 112 member organizations worldwide and more than two million user companies over 40 years. They develop global standards of how to identify, capture, share and use the data of real-world objects in business communication. The best-known standard is the barcode in retails, and they are expanding their area to healthcare, transport and logistics, food service, technical industries, and smart cities.
In this tutorial, we will give the introduction of GS1, and present the GS1’s standardization efforts with use case examples. Topics are as follows; 1) Identification and classification, 2) Semantic vocabulary, 3) Modeling and sharing of city resource metadata, 4) Master data, transaction, and event data modeling, 5) Data sharing system (API, distributed repository), 6) Smart data browsing, 7) Service registration, discovery, and access, 8) Traceability and block chain adoption, 9) Web vocabulary for city data, and 10) Oliot open source project.
Lastly, we would like to introduce Urban Technology Alliance (UTA) that aims to bring a complete smart city ecosystem, concerning various stakeholders such as city and government, industry, academia, non-profit organizations, and the most important, citizen.
This document discusses open data, processes, and services for an Internet of Things platform. It introduces the Oliot open source project, which aims to provide standardization for data collection, sharing and understanding on the IoT. It describes requirements for open processes, open data, and open services to support a federated IoT ecosystem. Key standards from GS1, an international standards organization, are also discussed in the context of the IoT.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
Hybrid integration reference architectureKim Clark
The ownership boundary of the typical enterprise now encompasses a much broader IT landscape. It is common to see that landscape stretch out to cloud native development platforms, software as a service, dependencies on external APIs from business partners, a mobile workforce and an ever growing range of digital channels. The integration surface area is dramatically increased and the integration patterns to support it are evolving just as quickly. These are the challenges we recognise as "hybrid integration". We will explore what a reference architecture for hybrid integration might look like, and how IBM's integration portfolio is growing and changing to meet the needs of digital transformation. This deck comes from the following article http://ibm.biz/HybridIntRefArch and is also described in this video http://ibm.biz/HybridIntRefArchYouTube
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
EA Intensive Course "Building Enterprise Architecture" by mr.danairatSoftware Park Thailand
This document outlines the agenda for a two-day course on building enterprise architecture. Day one covers introductions, current architecture challenges, the need for enterprise architecture, definitions of enterprise architecture, reference architecture frameworks, and group workshops. Day two covers maturity models, technology platforms, the TOGAF standard, cloud computing roadmaps, governance, and building a target architecture.
Data Mess to Data Mesh | Jay Kreps, CEO, Confluent | Kafka Summit Americas 20...HostedbyConfluent
Companies are increasingly becoming software-driven, requiring new approaches to software architecture and data integration. The "data mesh" architectural pattern decentralizes data management by organizing it around domain experts and treating data as products that can be accessed on-demand. This helps address issues with centralized data warehouses by evolving data modeling with business needs, avoiding bottlenecks, and giving autonomy to domain teams. Key principles of the data mesh include domain ownership of data, treating data as self-service products, and establishing federated governance to coordinate the decentralized system.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Data Mesh at CMC Markets: Past, Present and FutureLorenzo Nicora
This document discusses CMC Markets' implementation of a data mesh to improve data management and sharing. It provides an overview of CMC Markets, the challenges of their existing decentralized data landscape, and their goals in adopting a data mesh. The key sections describe what data is included in the data mesh, how they are using cloud infrastructure and tools to enable self-service, their implementation of a data discovery tool to make data findable, and how they are making on-premise data natively accessible in the cloud. Adopting the data mesh framework requires organizational changes, but enables autonomy, innovation and using data to power new products.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Business Intelligence (BI) and Data Management Basics amorshed
This document provides an overview of business intelligence (BI) and data management basics. It discusses topics such as digital transformation requirements, data strategy, data governance, data literacy, and becoming a data-driven organization. The document emphasizes that in the digital age, data is a key asset and organizations need to focus on data management in order to make informed decisions. It also stresses the importance of data culture and competency for successful BI and data initiatives.
The document is a slide deck from STKI, an Israeli market research and strategic analyst firm, discussing the impact of global events on IT budgets in Israel in 2022-2024. It notes that while IT budget increases were forecasted to be large in 2022, events like the war in Ukraine, inflation, interest rate hikes, and political instability have changed the outlook. IT budgets in Israel are still expected to rise 12-13% in 2022 but forecasts beyond that are difficult given uncertainties. Digital transformation alone is no longer sufficient - companies must undergo smart business transformations to deliver personalized, data-driven experiences to customers.
An overview of The Open Group IT4IT Reference Architecture. It is a vendor and product-agnostic value chain-based operating model for managing the business of IT. While providing guidance on the design, procurement and implementation of the functionality needed to run IT, it also enables the systematic tracking of the state of IT services across the service life-cycle using four value streams - Strategy to Portfolio, Request to Fulfill, Requirement to Deploy, and Detect to Correct.
Download presentation from http://opengroup.co.za/presentations
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
THE STRATEGIC BUSINESS MODEL CANVAS DESCRIBES THE CORRELATIONS BETWEEN THE COMPONENTS OF OUR STRATEGY. A JOURNEY FROM OUR CURRENT [TRANSIENT] COMPETITIVE ADVANTAGE TOWARDS THE STRATEGIC DESTINATION, WHERE OUR NEW [TRANSIENT] COMPETITIVE ADVANTAGE WILL BECOME EFFECTIVE.
This document discusses master data management (MDM). It begins by explaining that quality information is important for business strategies and that MDM bridges operational and information management solutions. It then discusses that MDM promotes efficiency, simplicity and data quality to improve business value. The document also outlines different MDM implementation styles including external databases, persistent databases, registries and composites. Finally, it provides an illustrative example of a persistent master data repository and recommends assessing the current state and selecting technologies to develop an MDM implementation roadmap.
This document discusses the importance and evolution of data modeling. It argues that data modeling is critical to all architecture disciplines, not just database development, as the data model provides common definitions and vocabulary. The document reviews the history of data management from the 1950s to today, noting how data modeling was originally used primarily for database development but now has broader applications. It discusses different types of data models for different purposes, and walks through traditional "top-down" and "bottom-up" approaches to using data models for database development. The overall message is that data modeling remains important but its uses and best practices have expanded beyond its original scope.
A Reference Process Model for Master Data ManagementBoris Otto
This document presents an overview of a reference process model for master data management. It includes an introduction discussing business requirements for master data and challenges in managing master data quality. It also describes the research methodology used to develop an iterative reference process model. The results section provides an overview of the reference process model and discusses its evaluation through three case studies. The conclusion recognizes the model's contribution in explicating the design process for master data management organizations.
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...Daeyoung Kim
In the Smart Cities Preliminary Report 2014 published by ISO/IEC JTC1, they have emphasized the importance of standardized, computer-recognizable, and actionable open data produced by various city resources. International standardization working groups such as ISO/IEC JTC1, JTC1/SC31 have been establishing new standards and also adopting existing standards for object identification, data modeling, and data acquisition which are the key features of the smart-city data platform.
ISO/IEC data standards have adopted many existing GS1 (Global Standards One) standards. GS1 is an international non-profit organization with 112 member organizations worldwide and more than two million user companies over 40 years. They develop global standards of how to identify, capture, share and use the data of real-world objects in business communication. The best-known standard is the barcode in retails, and they are expanding their area to healthcare, transport and logistics, food service, technical industries, and smart cities.
In this tutorial, we will give the introduction of GS1, and present the GS1’s standardization efforts with use case examples. Topics are as follows; 1) Identification and classification, 2) Semantic vocabulary, 3) Modeling and sharing of city resource metadata, 4) Master data, transaction, and event data modeling, 5) Data sharing system (API, distributed repository), 6) Smart data browsing, 7) Service registration, discovery, and access, 8) Traceability and block chain adoption, 9) Web vocabulary for city data, and 10) Oliot open source project.
Lastly, we would like to introduce Urban Technology Alliance (UTA) that aims to bring a complete smart city ecosystem, concerning various stakeholders such as city and government, industry, academia, non-profit organizations, and the most important, citizen.
This document discusses open data, processes, and services for an Internet of Things platform. It introduces the Oliot open source project, which aims to provide standardization for data collection, sharing and understanding on the IoT. It describes requirements for open processes, open data, and open services to support a federated IoT ecosystem. Key standards from GS1, an international standards organization, are also discussed in the context of the IoT.
Oliot samsung-daeyoungkim-kaist wide-version-finalDaeyoung Kim
This presentation about the Oliot (Open Language for Internet of Things) open source project provides an overview of its goals and technical approach. Oliot is an ID-based IoT framework that uses GS1 standard IDs to identify, capture, control, and share information about smart things. It aims to integrate passive tags, active tags, sensor/actuator networks and other IoT devices by adapting RFID, sensor and actuator streams and providing distributed storage and interfaces for interacting with and searching smart things and their data streams. The presentation outlines Oliot's technical approach including ID and sensor stream processing, heterogeneous network and data integration, and abstracting objects using sensor/actuator frameworks.
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...Daeyoung Kim
This document summarizes a presentation on using GS1 standards and blockchain technology for traceability in the agriculture and seafood industries. It discusses how GS1 identifiers like GTIN, GLN, and GRAI can be used to identify products, locations, and assets. It also presents how the GS1 Global Data Synchronization Network (GDSN) and EPC Information Service (EPCIS) can be leveraged to share product and sensor data. Finally, it proposes how a blockchain-based GS1 Object Name Service (ONS) could provide a decentralized and transparent system for traceability across the supply chain.
Internet of Things Platform for Open Process, Open Data, and Open ServiceDaeyoung Kim
This document discusses an Internet of Things platform for open process, open data, and open service. It proposes that an IoT platform needs open standards for the lifecycle of connected things (open process), sharing of thing data (open data), and discovery and access of IoT services (open service). The document outlines GS1 standards that provide global identification of things and a common language for classifying things. It also describes the Oliot open source project, which implements GS1 standards and enhancements for an open IoT architecture based on open process, data, and service.
The document discusses applying international standards in building smart cities. It introduces GS1, an organization that provides global standards for identifying, capturing, and sharing data. GS1 standards can help integrate fragmented legacy city platforms by enabling interoperable and standardized data sharing. This allows for insights from aggregated data and the development of new smart city services. The document argues that GS1 standards provide a framework to identify people, places, things and services, and enable data and service sharing across different industry domains and for smart cities and IoT applications.
Eu fp7-h2020-experiences-daeyoung kim-kaistDaeyoung Kim
This document summarizes Dr. Daeyoung Kim's presentations on Internet of Things (IoT) research collaborations between Korea and the European Union. It discusses three projects: IoT6, an EU FP7 project from 2011-2014 that developed an IPv6-based IoT architecture; Wise-IoT, an ongoing 2016-2018 EU-KR collaboration project testing IoT applications; and IoF2020, an EU Horizon 2020 project from 2017-2020 focusing on IoT in the food and agriculture industry. KAIST is leading Korean involvement in these projects, including developing agricultural cloud services and establishing a food traceability testbed with China. The document outlines the objectives and achievements of the projects and emphasizes benefits of
GS1 EPCglobal framework and Oliot Project OverviewDaeyoung Kim
The document provides an overview of the GS1/EPCglobal standards and the Oliot project. It discusses key aspects of the GS1/EPCglobal standards including the Global Product Classification, Application Identifiers, GS1 Keys like the Global Trade Item Number and Serial Shipping Container Code, and the Electronic Product Code. It also provides an overview of the scope and components of the Oliot project, an open source IoT framework based on GS1 standards that aims to identify, capture, control and share information about smart things through technologies like RFID, sensors and actuators.
Oliot daeyoungkim-kaist-2015 - final - shortDaeyoung Kim
This document provides an overview of the Oliot (Open Language for Internet of Things) IoT platform and its applications. It discusses the Internet of Things ecosystem and requirements. It then describes the Oliot platform, which is based on GS1 standards for identifying smart objects. The platform includes EPC Information Services to store and share visibility data. It also extends services like Object Naming Service for IoT discovery. Several ongoing Oliot applications are highlighted, including healthcare monitoring, smart agriculture/food safety, and bridge management.
Iot ecosystem-challenges-daeyoungkim-auto-id-labs-kaistDaeyoung Kim
This document outlines a presentation on realizing an Internet of Things (IoT) ecosystem through technology and strategies. It discusses definitions of IoT and its ecosystem, visions and market analysis for IoT, experiences with RFID/USN technologies, global IoT technology trends, challenges and case studies, and policy proposals. The presentation covers topics such as smart sensors, networking standards, IoT platforms, killer application domains, and extending EPCglobal standards to integrate sensor network protocols and support IoT use cases.
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...Daeyoung Kim
In the Smart Cities Preliminary Report 2014 published by ISO/IEC JTC1, they have emphasized the importance of standardized, computer-recognizable, and actionable open data produced by various city resources. International standardization working groups such as ISO/IEC JTC1, JTC1/SC31 have been establishing new standards and also adopting existing standards for object identification, data modeling, and data acquisition which are the key features of the smart-city data platform.
ISO/IEC data standards have adopted many existing GS1 (Global Standards One) standards. GS1 is an international non-profit organization with 112 member organizations worldwide and more than two million user companies over 40 years. They develop global standards of how to identify, capture, share and use the data of real-world objects in business communication. The best-known standard is the barcode in retails, and they are expanding their area to healthcare, transport and logistics, food service, technical industries, and smart cities.
In this tutorial, we will give the introduction of GS1, and present the GS1’s standardization efforts with use case examples. Topics are as follows; 1) Identification and classification, 2) Semantic vocabulary, 3) Modeling and sharing of city resource metadata, 4) Master data, transaction, and event data modeling, 5) Data sharing system (API, distributed repository), 6) Smart data browsing, 7) Service registration, discovery, and access, 8) Traceability and block chain adoption, 9) Web vocabulary for city data, and 10) Oliot open source project.
Lastly, we would like to introduce Urban Technology Alliance (UTA) that aims to bring a complete smart city ecosystem, concerning various stakeholders such as city and government, industry, academia, non-profit organizations, and the most important, citizen.
GS1 smart city platforms and case studiesDaeyoung Kim
This document discusses strategies for communications and coexistence in smart cities using GS1 standards. It begins by defining smart cities and describing fragmented data issues across different platforms currently used in cities. It then explains how GS1 standards can help integrate these platforms by providing common identifications, vocabularies, data formats, and application programming interfaces. Specifically, GS1 standards allow for unique identification of entities, sharing of data about products and services, and discovery of digital services. The document provides examples of how GS1 has achieved interoperability in other domains like healthcare and describes some smart city projects in Korea that utilize GS1 standards.
This document provides an overview of the Internet of Things presented by Daeyoung Kim, the Director of Auto-ID Labs at KAIST. It discusses several case studies and research projects around healthcare applications, smart agriculture and food traceability. It also covers the role of standards from GS1 and technologies developed at Auto-ID Labs like Oliot, SNAIL, and EPCIS that aim to realize the vision of connecting everything to the Internet and supporting the exchange of information.
This document discusses research activities related to the Internet of Things (IoT) at Auto-ID Labs at KAIST. It provides an overview of several IoT projects at the labs including SNAIL (Sensor Networks for an All-IP World), which is an IP-based wireless sensor network platform, and Oliot (Open Language for IoT), which is an ID-based IoT framework based on GS1 standards. The document also discusses several directions for IoT including technologies from Google, Apple, ARM, Qualcomm, Samsung/Intel and others. It outlines the history and goals of IoT research at Auto-ID Labs and their partnerships with other organizations like GS1.
OGF actively collaborates with other standards organizations through cooperative agreements to develop standards for distributed computing. OGF has relationships with groups like DMTF, ISO, SNIA, ETSI, ITU-T, and NIST to jointly develop standards for areas like cloud computing, identity management, and data formats. These collaborations help drive innovation while avoiding duplication of efforts between organizations.
Itu telecom-world-2017-autoidlabs-kaist-consortiumDaeyoung Kim
- GS1 is an international nonprofit organization dedicated to developing global standards for supply chain management. It has members in over 112 countries.
- Auto-ID Labs is a research consortium that develops Internet of Things technologies, including open source platforms that comply with GS1 standards. The KAIST branch in South Korea is one of six labs that comprise Auto-ID Labs.
- The document describes several open source projects developed by Auto-ID Labs at KAIST that implement various GS1 standards, including EPCIS for capturing IoT data, ONS for discovering IoT services, and Oliot which provides an overall IoT platform infrastructure.
This presentation discusses using EPCIS (Electronic Product Code Information Services) and distributed storage and processing platforms like Oliot, Cassandra, and Storm for managing real-time IoT data from RFID and sensor devices. It proposes extending EPCIS standards to support additional IoT device event types and using Cassandra for distributed storage and Storm for real-time analytics of large volumes of continuous IoT data streams.
The document discusses establishing a global seafood traceability system. It outlines the need for traceability in seafood due to safety, regulations, environmental protection, and brand value reasons. It then describes current traceability efforts in Korea and Japan and goals for a global traceability system. It discusses global activities and the GS1 global standards organization and standards for traceability. The overall goal is to connect seafood production, distribution, and consumption globally through a standardized traceability system.
GE developed a manufacturing platform called CEED that connects people, materials, models, simulation, and equipment in a secure global environment allowing for collaboration, rapid prototyping, and product development. Philips Healthcare is building its Philips HealthSuite digital platform on AWS to analyze and store 15 PB of patient data from 390 million studies at a growth rate of 1 PB per month. Kärcher Fleet Management was developed on AWS to help Kärcher customers manage their cleaning fleets in a smarter way.
Digital revolution series 1-seafood industryDaeyoung Kim
This document provides an overview of establishing a global seafood traceability system. It discusses why such a system is needed for food safety, regulations, marine life protection, certifications, anti-piracy/human rights issues, and brand value. Current traceability efforts in Korea and Japan are mentioned, along with goals of connecting global food production, logistics, and consumption ecosystems through traceability. International activities and GS1 global standards for traceability are also summarized. The presentation aims to explain how establishing a global standardized seafood traceability system can benefit multiple stakeholders.
Similaire à GS1 Data Revolution Series 2 - Internet of Trains (20)
This newsletter discusses digital transformation in the construction industry through using standardized identification systems like GS1 global data standards. It provides updates on industry news including building reforms in New South Wales, digital supply chains, and global case studies of implementing GS1 standards in construction projects to improve traceability, efficiency, and sustainability. Readers are also invited to learn more about GS1 and get involved in industry groups and initiatives.
GS1 Data Revolution Series #3 HealthcareDaeyoung Kim
This document provides an overview of GS1 standards for digital transformation in healthcare. It begins with four parts that discuss digital transformation in healthcare, GS1 standards, GS1 healthcare standards, and global healthcare industry trends related to GS1. It concludes with a part on digital twins, digital infrastructure, and digital systems of care infrastructure. The document provides high-level information on how GS1 standards can support digitalization, data sharing, traceability, and other benefits across the healthcare sector through the use of standardized identification and data formats.
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)Daeyoung Kim
The document discusses the GS1 Object Name Service (ONS), which uses the Domain Name System (DNS) to look up services associated with GS1 identification keys. ONS converts GS1 IDs to Application Unique Strings (AUS) and then to Fully Qualified Domain Names (FQDNs) to query the DNS. It describes how ONS uses a federated model with multiple peer root servers to improve security and accommodate different country laws. Peer roots exchange information through peer delegation files to enable cross-country lookups.
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)Daeyoung Kim
This document provides an overview of EPCIS (Electronic Product Code Information Services) and CBV (Core Business Vocabulary). EPCIS is a standard that defines how visibility event data can be captured and shared by applications within and across enterprises to provide visibility into physical and digital objects. CBV provides standard definitions for values used in EPCIS events. The document outlines the EPCIS data model, interfaces, event anatomy, applications, and relationship to master data vocabularies like CBV.
Smart city position paper - GS1 standards perspectiveDaeyoung Kim
This document discusses strategies for developing interoperable data-driven smart cities. It begins by defining key smart city stakeholders and trends, including open data hubs and living labs. It then discusses challenges like fragmented data sources and solutions like using GS1 global standards for resource identification. Use cases from Korea demonstrate how GS1 standards were applied successfully to integrate legacy systems and develop new services. Overall, the document advocates applying GS1 identification, data modeling and interface standards to address smart city interoperability issues and help realize the full potential of data-driven smart governance and services.
GS1 Standards in Building Smart CitiesDaeyoung Kim
This document discusses using GS1 standards to build digital twins of smart cities. It begins with an overview of the Auto-ID Labs at KAIST and their work developing GS1 standards. It then discusses definitions of smart cities and examples of smart city services. The document outlines some examples of smart city projects in countries like Japan, Korea, China, and Barcelona. It focuses on using GS1 standards like EPCIS, GLN, and ONS to integrate legacy city platforms and share data and services. This would allow building digital twins and providing new smart city insights and applications. The document proposes mapping Korean address standards to GLN to uniquely identify locations. It also suggests using EPCIS as a smart city data hub to
Improving safety and efficiency in the rail industry
Today’s rail industry is faced with mounting competitive and cost pressures that call for significant improvements in reliability, operating efficiencies and rail safety. Detailed risk management is becoming increasingly important— even mandatory due to current and upcoming regulations. Manufacturing, maintenance, repair and overhaul (MRO) processes have become more complex and global, with materials being sourced from all parts of the world.
More than 20 leading railway operators, manufacturers and solution providers have stepped up to develop new applications standard for rail
Enabling timely condition-based maintenance
Providing the foundation for safety-relevant information exchange
Providing improved analytics and incident investigation
Identifying more easily series faults
Enabling more effective recall management
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Drona Infotech is a premier mobile app development company in Noida, providing cutting-edge solutions for businesses.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Atelier - Innover avec l’IA Générative et les graphes de connaissances
GS1 Data Revolution Series 2 - Internet of Trains
1. 김 대 영
2019년 10월 17일
교수, 전산학부, KAIST
Director, Auto-ID Labs, KAIST
kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org
Internet of Trains: GS1 국제표준기반의 철도산업
디지털 트랜스포메이션 전략과 구축 기술
김 대 영
2020년 6월 30일
14:00 ~15:40 (Webinar)
교수, 전산학부, KAIST
Director, Auto-ID Labs, KAIST
kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org