Hosted by BDV PPP. BigDataStack, I-BiDaaS, Track & Know and Policy Cloud join forces in a series of online demonstrations of innovative Big Data Technologies unlocking the potential of various applications.
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BDV PPP projects I-BiDaaS, BigDataStack, and Track & Know, deliver innovative technologies to address the emerging needs of data operations and applications. To fully exploit the sustainability and take full advantage of the developed technologies, the projects onboarded pilots that exhibit their applicability in a wide variety of sectors. In the Big Data Pilot Demo Days the projects will showcase the developed and implemented technologies to interested end-users from the industry as well as technology providers, for further adoption.
One of the main goals of the I-BiDaaS project is to provide a Big Data as a self-service solution that will empower the actual employees of European companies in targeted sectors (banking, manufacturing, telecom), i.e., the true decision-makers, with the insights and tools they need in order to make the right decisions in an agile way. In this big data pilot webinar, we will demonstrate in a step by step fashion the I-BiDaaS self-service solution and its application to the banking sector. In more detail, we will present an overview of the I-BiDaaS project focusing on the requirements of the CaixaBank pilot study, the I-BiDaaS architecture with its core technologies, and a step by step demo of the I-BiDaaS solution. Last but not least, we will show through CaixaBank's success story how I-BiDaaS can resolve data availability, data sharing, and breaking silos challenges in the banking domain.
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BDV PPP projects I-BiDaaS, BigDataStack, Track & Know and Policy Cloud deliver innovative technologies to address the emerging needs of data operations and applications. To fully exploit the sustainability and take full advantage of the developed technologies, the projects onboarded pilots that exhibit their applicability in a wide variety of sectors. In the Big Data Pilot Demo Days, the projects will showcase the developed and implemented technologies to interested end-users from the industry as well as technology providers, for further adoption.
L'economia europea dei dati. Politiche europee e opportunità di finanziamento...Data Driven Innovation
L'economia europea dei dati: soluzioni politiche e giuridiche per realizzare un'economia dei dati a livello di Unione Europea, nell'ambito della strategia per il mercato unico digitale. La consultazione pubblica 'Building the European Data Economy'. Il paternariato pubblico privato (PPP) Big Data Value ed opportunità di finanziamento in Horizon 2020. L'incubatore Data Pitch: opportunità per Start-up e Piccole e Medie Imprese.
Sotiris is currently working as Research Director with the Institute of Computer Science at the Foundation for Research and Technology - Hellas, where his research interests include systems, networks, and security. He is also a member of the European Union Agency for Network and Information Security (ENISA) Permanent Stakeholders Group! During Data Science Conference, Sotiris will talk about how data sharing between private companies and research facilities may lead to monetization.
One of the main goals of the I-BiDaaS project is to provide a Big Data as a self-service solution that will empower the actual employees of European companies in targeted sectors (banking, manufacturing, telecom), i.e., the true decision-makers, with the insights and tools they need in order to make the right decisions in an agile way. In this big data pilot webinar, we will demonstrate in a step by step fashion the I-BiDaaS self-service solution and its application to the banking sector. In more detail, we will present an overview of the I-BiDaaS project focusing on the requirements of the CaixaBank pilot study, the I-BiDaaS architecture with its core technologies, and a step by step demo of the I-BiDaaS solution. Last but not least, we will show through CaixaBank's success story how I-BiDaaS can resolve data availability, data sharing, and breaking silos challenges in the banking domain.
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BDV PPP projects I-BiDaaS, BigDataStack, Track & Know and Policy Cloud deliver innovative technologies to address the emerging needs of data operations and applications. To fully exploit the sustainability and take full advantage of the developed technologies, the projects onboarded pilots that exhibit their applicability in a wide variety of sectors. In the Big Data Pilot Demo Days, the projects will showcase the developed and implemented technologies to interested end-users from the industry as well as technology providers, for further adoption.
L'economia europea dei dati. Politiche europee e opportunità di finanziamento...Data Driven Innovation
L'economia europea dei dati: soluzioni politiche e giuridiche per realizzare un'economia dei dati a livello di Unione Europea, nell'ambito della strategia per il mercato unico digitale. La consultazione pubblica 'Building the European Data Economy'. Il paternariato pubblico privato (PPP) Big Data Value ed opportunità di finanziamento in Horizon 2020. L'incubatore Data Pitch: opportunità per Start-up e Piccole e Medie Imprese.
Sotiris is currently working as Research Director with the Institute of Computer Science at the Foundation for Research and Technology - Hellas, where his research interests include systems, networks, and security. He is also a member of the European Union Agency for Network and Information Security (ENISA) Permanent Stakeholders Group! During Data Science Conference, Sotiris will talk about how data sharing between private companies and research facilities may lead to monetization.
Con Microsoft Hololens affiancato dalla potenza di servizi cloud come Bluemix e Azure daremo un assaggio di un nuovo metodo di Data Visualization. Nella sessione useremo ologrammi per visionare e interagire con Big Data provenienti da sorgenti di OpenData pubblici e elaborati in cloud.
NTT DATA Open Innovation Contest 10.0 for startups and scaleupsTom Winstanley
NTT DATA runs a global open innovation contest annually as part of programmes to identify partners to develop new value adding services with and to scale services and go-to-market more rapidly. This 10.0 edition of the event is taking place in 16 cities globally, including in London and Edinburgh in December 2019. Find out more at http://uk.nttdata.com/openinnovationcontest
Infopulse Mobile App Development ServicesInfopulse
Infopulse offers full-cycle mobile application development services since 2008. We develop any types of mobile apps for handheld devices and wearables regardless of the platform. Let's talk: http://bit.ly/2xKWtz1
Business Boost Webinars - Introduction to SmartAgriHubsFIWARE
This webinar describes the concept of the SmartAgriHubs, the role of Digital Innovation Hubs and Competence Centres, and the added value for FIWARE members in joining the Competence Centres network. Further information about the role and responsibilities of Digital Innovation Hubs and Competence Centres within the SmartAgriHubs network can be found on the SmartAgriHubs website: https://www.smartagrihubs.eu/competence-centers
Audience: Anyone, iHubs
Speakers: Cynthia Giagnocavo (University of Almería, Spain), Ahmad Issa (IPA Fraunhofer, Germany), Jason Fox (FIWARE Foundation)
Corresponding webinar recording: https://youtu.be/v37YxaQ4hMo
International Technology Adoption & Workforce Issues Study - German SummaryCompTIA
86% of German executives indicate at least some degree of gaps in IT skills at their business exists. 69% of German executives believe the cybersecurity threat level is increasing. Find out more on how companies are adopting new technology and how it's impacting their workforce.
Building better healthcare products with AITom Winstanley
Beyond the hype of AI in health - what does it take to deliver quality products that make a difference and the #TripleWin of academic, healthcare and industrial collaborations
Federation of data services to foster the adoption of data-driven AI in Europe
Dr. Diego López-de-Ipiña, University of Deusto
BDV PPP SUMMIT, June 26-28 2019, Riga, Latvia
Towards IoT-Driven Predictive Business Process Analytics Erfan Elhami
COINS: IEEE International Conference on Omni-layer Intelligent systems // August 2020 // Online Presentation // "Business processes are not isolated from the surrounding working environment, and thus they are influenced by many contextual events, such as events generated by IoT devices. This paper proposes a holistic context-aware methodology for predictive process monitoring by incorporating IoT data. Moreover, we present a systematic method to integrate the contextual events in the runtime process using Business Process Management System (BPMS)."
FinTech and InsuranceTech case studies digitally transforming Europe's future with BigData and AI
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. The insurance and finance services industry is rapidly transformed by data-intensive operations and applications. FinTech and InsuranceTech combine very large datasets from legacy banking systems with other data sources such as financial markets data, regulatory datasets, real-time retail transactions, and more, improving financial services and activities for customers.
Con Microsoft Hololens affiancato dalla potenza di servizi cloud come Bluemix e Azure daremo un assaggio di un nuovo metodo di Data Visualization. Nella sessione useremo ologrammi per visionare e interagire con Big Data provenienti da sorgenti di OpenData pubblici e elaborati in cloud.
NTT DATA Open Innovation Contest 10.0 for startups and scaleupsTom Winstanley
NTT DATA runs a global open innovation contest annually as part of programmes to identify partners to develop new value adding services with and to scale services and go-to-market more rapidly. This 10.0 edition of the event is taking place in 16 cities globally, including in London and Edinburgh in December 2019. Find out more at http://uk.nttdata.com/openinnovationcontest
Infopulse Mobile App Development ServicesInfopulse
Infopulse offers full-cycle mobile application development services since 2008. We develop any types of mobile apps for handheld devices and wearables regardless of the platform. Let's talk: http://bit.ly/2xKWtz1
Business Boost Webinars - Introduction to SmartAgriHubsFIWARE
This webinar describes the concept of the SmartAgriHubs, the role of Digital Innovation Hubs and Competence Centres, and the added value for FIWARE members in joining the Competence Centres network. Further information about the role and responsibilities of Digital Innovation Hubs and Competence Centres within the SmartAgriHubs network can be found on the SmartAgriHubs website: https://www.smartagrihubs.eu/competence-centers
Audience: Anyone, iHubs
Speakers: Cynthia Giagnocavo (University of Almería, Spain), Ahmad Issa (IPA Fraunhofer, Germany), Jason Fox (FIWARE Foundation)
Corresponding webinar recording: https://youtu.be/v37YxaQ4hMo
International Technology Adoption & Workforce Issues Study - German SummaryCompTIA
86% of German executives indicate at least some degree of gaps in IT skills at their business exists. 69% of German executives believe the cybersecurity threat level is increasing. Find out more on how companies are adopting new technology and how it's impacting their workforce.
Building better healthcare products with AITom Winstanley
Beyond the hype of AI in health - what does it take to deliver quality products that make a difference and the #TripleWin of academic, healthcare and industrial collaborations
Federation of data services to foster the adoption of data-driven AI in Europe
Dr. Diego López-de-Ipiña, University of Deusto
BDV PPP SUMMIT, June 26-28 2019, Riga, Latvia
Towards IoT-Driven Predictive Business Process Analytics Erfan Elhami
COINS: IEEE International Conference on Omni-layer Intelligent systems // August 2020 // Online Presentation // "Business processes are not isolated from the surrounding working environment, and thus they are influenced by many contextual events, such as events generated by IoT devices. This paper proposes a holistic context-aware methodology for predictive process monitoring by incorporating IoT data. Moreover, we present a systematic method to integrate the contextual events in the runtime process using Business Process Management System (BPMS)."
FinTech and InsuranceTech case studies digitally transforming Europe's future with BigData and AI
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. The insurance and finance services industry is rapidly transformed by data-intensive operations and applications. FinTech and InsuranceTech combine very large datasets from legacy banking systems with other data sources such as financial markets data, regulatory datasets, real-time retail transactions, and more, improving financial services and activities for customers.
The problem of radicalisation is very high on the European agenda as increasing numbers of young European radicals return from Syria and use the internet to disseminate propaganda. To enable policy makers to design policies to address radicalisation effectively, Policy Cloud consortium will collect data from social media and other sources including the open-source Global Terrorism Database (GTD), the Onion City search engine which accesses data over the TOR dark web sites, and Twitter ( through Firehose). The data will be analysed using sentiment analysis and opinion mining software.
The problem of radicalisation is very high on the European agenda as increasing numbers of young European radicals return from Syria and use the internet to disseminate propaganda. To enable policy makers to design policies to address radicalisation effectively, Policy Cloud consortium will collect data from social media and other sources including the open-source Global Terrorism Database (GTD), the Onion City search engine which accesses data over the TOR dark web sites, and Twitter ( through Firehose). The data will be analysed using sentiment analysis and opinion mining software.
BigDataStack Connected Consumer Pilot Demo
BigDataStack will provide retailers with optimal insights into consumer preferences and increase the effectiveness of marketing strategies to improve the consumer shopping experience. Led by Worldline, a roadmap for a major Spanish food retailer has been defined, allowing them to offer predictive shopping lists, and tailored recommendations and promotions, improving consumers’ experiences.
Webinar takeaways
The Connected Consumer use case utilizes the BigDataStack environment to implement and offer a recommender system for the grocery market.
All of the data that are used to train the analytic algorithms of the use case are corporate data provided by one of the top food retailers companies in Spain.
Data Virtualization: Fulfilling The Digital Transformation Requirement In Ban...Denodo
Watch full webinar here: https://bit.ly/3szm3PV
In the digital transformation era, banks need a single view of all their data and a way to establish security controls across the entire infrastructure. This can be achieved with Data Virtualization.
Banking institutions need to update their legacy systems and implement strategies and services that will transform them into digital financial organizations.
They need agile access to information that can be leveraged to make timely business decisions, yet still fulfill the regulatory requirements. In the digital transformation era, banks need a single view of all their data and a way to establish security controls across the entire infrastructure.
This webinar presents:
- How data virtualization can help update and modernize data architectures,
- Success stories of financial companies that already use this technology to differentiate themselves from the competition, optimize processes, and create new business opportunities through more agile data management.
Transforming the European Data Economy: A Strategic Research and Innovation A...Edward Curry
Transforming the European Data Economy: A Strategic Research and Innovation Agenda
Keynote at European Data Forum 2016
Prof. Dr. Milan Petković, Vice President BDVA, Philips
Dr. Edward Curry, Vice President BDVA, Insight
FinTech and InsuranceTech case studies digitally transforming Europe's future with BigData and AI
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. The insurance and finance services industry is rapidly transformed by data-intensive operations and applications. FinTech and InsuranceTech combine very large datasets from legacy banking systems with other data sources such as financial markets data, regulatory datasets, real-time retail transactions, and more, improving financial services and activities for customers.
Big Data lay at the core of the strong data economy that is emerging in Europe. Although both large enterprises and SMEs acknowledge the potential of Big Data in disrupting the market and business models, this is not reflected in the growth of the data economy. The lack of trusted, secure, ethical-driven personal data platforms and privacy-aware analytics, hinders the growth of the data economy and creates concerns. The main considerations are related to the secure sharing of personal and proprietary/industrial data, and the definition of a fair remuneration mechanism that will be able to capture, produce, release and cash out the value of data, always for the benefit of all the involved stakeholders.
This webinar will focus on how such concerns that pertain to privacy, ethics and intellectual property rights can be tackled, by allowing individuals to take ownership and control of their data and share them at will, through flexible data sharing and fair compensation schemes with other entities (companies or not), as researched by the DataVaults project.
Big Data lay at the core of the strong data economy that is emerging in Europe. Although both large enterprises and SMEs acknowledge the potential of Big Data in disrupting the market and business models, this is not reflected in the growth of the data economy. The lack of trusted, secure, ethical-driven personal data platforms and privacy-aware analytics, hinders the growth of the data economy and creates concerns. The main considerations are related to the secure sharing of personal and proprietary/industrial data, and the definition of a fair remuneration mechanism that will be able to capture, produce, release and cash out the value of data, always for the benefit of all the involved stakeholders.
This webinar will focus on how such concerns that pertain to privacy, ethics and intellectual property rights can be tackled, by allowing individuals to take ownership and control of their data and share them at will, through flexible data sharing and fair compensation schemes with other entities (companies or not), as researched by the DataVaults project.
Big Data lay at the core of the strong data economy that is emerging in Europe. Although both large enterprises and SMEs acknowledge the potential of Big Data in disrupting the market and business models, this is not reflected in the growth of the data economy. The lack of trusted, secure, ethical-driven personal data platforms and privacy-aware analytics, hinders the growth of the data economy and creates concerns. The main considerations are related to the secure sharing of personal and proprietary/industrial data, and the definition of a fair remuneration mechanism that will be able to capture, produce, release and cash out the value of data, always for the benefit of all the involved stakeholders.
This webinar will focus on how such concerns that pertain to privacy, ethics and intellectual property rights can be tackled, by allowing individuals to take ownership and control of their data and share them at will, through flexible data sharing and fair compensation schemes with other entities (companies or not), as researched by the DataVaults project.
Intro - Three pillars for building a Smart Data Ecosystem: Trust, Security an...Big Data Value Association
Today’s data marketplaces are large, closed ecosystems that are in the hands of few established players or a consortium that decide on the rules, policies, etc.
Yet, the main barrier of the European data economy is the fact that current data spaces and marketplaces are “siloes”, without support for data exchange across their boundaries.
This webinar reveals how these boundaries can be overcome through the i3-MARKET “backplane”, which is an infrastructure able to connect all the stakeholders providing the suitable level of trust (consensus-based self-governing, auditability, reliability, verifiable credentials), security (P2P encryption, cryptographic proofs) and privacy (self-sovereign identity, zero-knowledge proof, explicit user consent).
Three pillars for building a Smart Data Ecosystem: Trust, Security and PrivacyBig Data Value Association
Today’s data marketplaces are large, closed ecosystems that are in the hands of few established players or a consortium that decide on the rules, policies, etc.
Yet, the main barrier of the European data economy is the fact that current data spaces and marketplaces are “siloes”, without support for data exchange across their boundaries.
This webinar reveals how these boundaries can be overcome through the i3-MARKET “backplane”, which is an infrastructure able to connect all the stakeholders providing the suitable level of trust (consensus-based self-governing, auditability, reliability, verifiable credentials), security (P2P encryption, cryptographic proofs) and privacy (self-sovereign identity, zero-knowledge proof, explicit user consent).
Market into context - Three pillars for building a Smart Data Ecosystem: Trus...Big Data Value Association
Today’s data marketplaces are large, closed ecosystems that are in the hands of few established players or a consortium that decide on the rules, policies, etc.
Yet, the main barrier of the European data economy is the fact that current data spaces and marketplaces are “siloes”, without support for data exchange across their boundaries.
This webinar reveals how these boundaries can be overcome through the i3-MARKET “backplane”, which is an infrastructure able to connect all the stakeholders providing the suitable level of trust (consensus-based self-governing, auditability, reliability, verifiable credentials), security (P2P encryption, cryptographic proofs) and privacy (self-sovereign identity, zero-knowledge proof, explicit user consent).
BDV Skills Accreditation - Future of digital skills in Europe reskilling and ...Big Data Value Association
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
BDV Skills Accreditation - Recognizing Data Science Skills with BDV Data Scie...Big Data Value Association
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
EIT label intro by Rroberto Prieto
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
Muluneh Oli (EIT Digital)
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
BDV Skills Accreditation - Definition and ensuring of digital roles and compe...Big Data Value Association
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
At the heart of this DataBench webinar is the goal to share a benchmarking process helping European organisations developing Big Data Technologies to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance.
The webinar aims to provide the audience with a framework and tools to assess the performance and impact of Big Data and AI technologies, by providing real insights coming from DataBench. In addition, representatives from other projects part of the BDV PPP such as DeepHealth and They-Buy-for-You will participate to share the challenges and opportunities they have identified on the use of Big Data, Analytics, AI. The perspective of other projects that also have looked into benchmarking, such as Track&Now and I-BiDaaS will be introduced.
At the heart of this DataBench webinar is the goal to share a benchmarking process helping European organisations developing Big Data Technologies to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance.
The webinar aims to provide the audience with a framework and tools to assess the performance and impact of Big Data and AI technologies, by providing real insights coming from DataBench. In addition, representatives from other projects part of the BDV PPP such as DeepHealth and They-Buy-for-You will participate to share the challenges and opportunities they have identified on the use of Big Data, Analytics, AI. The perspective of other projects that also have looked into benchmarking, such as Track&Now and I-BiDaaS will be introduced.
Virtual BenchLearning - I-BiDaaS - Industrial-Driven Big Data as a Self-Servi...Big Data Value Association
At the heart of this DataBench webinar is the goal to share a benchmarking process helping European organisations developing Big Data Technologies to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance.
The webinar aims to provide the audience with a framework and tools to assess the performance and impact of Big Data and AI technologies, by providing real insights coming from DataBench. In addition, representatives from other projects part of the BDV PPP such as DeepHealth and They-Buy-for-You will participate to share the challenges and opportunities they have identified on the use of Big Data, Analytics, AI. The perspective of other projects that also have looked into benchmarking, such as Track&Now and I-BiDaaS will be introduced.
The problem of radicalisation is very high on the European agenda as increasing numbers of young European radicals return from Syria and use the internet to disseminate propaganda. To enable policy makers to design policies to address radicalisation effectively, Policy Cloud consortium will collect data from social media and other sources including the open-source Global Terrorism Database (GTD), the Onion City search engine which accesses data over the TOR dark web sites, and Twitter ( through Firehose). The data will be analysed using sentiment analysis and opinion mining software.
Policy Cloud Data Driven Policies against Radicalisation - Participatory poli...Big Data Value Association
The problem of radicalisation is very high on the European agenda as increasing numbers of young European radicals return from Syria and use the internet to disseminate propaganda. To enable policy makers to design policies to address radicalisation effectively, Policy Cloud consortium will collect data from social media and other sources including the open-source Global Terrorism Database (GTD), the Onion City search engine which accesses data over the TOR dark web sites, and Twitter ( through Firehose). The data will be analysed using sentiment analysis and opinion mining software.
Data Innovation Spaces are identified by BDVA as a key instrument to foster the Data-Driven Innovation in Europe. They provide innovation and experimentation environments where companies in their respective ecosystems could have their data-driven and AI-related products and solutions piloted, tested, and exploited before going to the market. BDVA launches every year a process to identify and recognize relevant initiatives in Europe that meet specific quality criteria in infrastructures, services, projects, and sectors of application, ecosystem and sustainability (BDVA i-Spaces call for labels).
During this session, we will present the concept of BDVA i-Spaces (as it is reflected in the BDVA SRIA), the process and steps of i-Spaces labeling, the value proposition of being an i-Space and activities and examples of collaboration. The session will also include examples of first-hand experience from three recognized i-Spaces: ITAINNOVA (DIH Aragon), UPM, and Demokritos NCSR (aheed DIH).
Data Innovation Spaces are identified by BDVA as a key instrument to foster the Data-Driven Innovation in Europe. They provide innovation and experimentation environments where companies in their respective ecosystems could have their data-driven and AI-related products and solutions piloted, tested, and exploited before going to the market. BDVA launches every year a process to identify and recognize relevant initiatives in Europe that meet specific quality criteria in infrastructures, services, projects, and sectors of application, ecosystem and sustainability (BDVA i-Spaces call for labels).
During this session, we will present the concept of BDVA i-Spaces (as it is reflected in the BDVA SRIA), the process and steps of i-Spaces labeling, the value proposition of being an i-Space and activities and examples of collaboration. The session will also include examples of first-hand experience from three recognized i-Spaces: ITAINNOVA (DIH Aragon), UPM, and Demokritos NCSR (aheed DIH).
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
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
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
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This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Big Data Pilot Demo Days – I-BiDaaS Sets the Scene
1. Big Data Pilot Demo Days
Despina Kopanaki (FORTH) dkopanaki@ics.forth.gr
Marieke Willems (Trust-IT) m.willems@trust-itservices.com
Andrea Schillaci (Trust-IT) a.schillaci@trust-itservices.com
3. Why Big Data Pilot Demo Days?
The new data-driven industrial revolution highlights the need for
big data technologies to unlock the potential in various
application domains.
BDV PPP projects I-BiDaaS, BigDataStack and Track & Know and
deliver innovative technologies to address the emerging needs of
data operations and applications.
To fully exploit the sustainability of the developed technologies,
the projects onboarded pilots that exhibit their applicability in a
wide variety of sectors.
In their third and final year, the projects are ready to demonstrate
the developed and implemented technologies to interested end-
users from industry as well as technology providers, for further
adoption.
21/05/20 3
4. BDV PPP Projects Join Forces
21/05/20 4
Industrial-Driven Big Data as a Self-Service Solution
Holistic stack for big data applications and
operations
Big Data for Mobility Tracking Knowledge
Extraction in Urban Areas
BDV PPP projects joining forces to showcase application of innovative technologies in a
variety of domains, fostering futher adoption, contributing to Europe’s digital future.
5. Big Data Pilot Demo Days - A Series of Webinars
5
I-BiDaaS Application
to the Financial Sector
1
BigDataStack Connected
Consumer demo
Track & Know applied to the
fleet management sector
2 3
456
7 8 9
A BigDataStack
Seafarer’s Tale on Real
Time Shipping
I-BiDaaS Application to the
Telecommunication Sector
I-BiDaaS Application to the
manufacturing sector
Track & Know applied to
patient mobility in the
healthcare sector
BigDataStack
Smart Insurance
Track & Know applied to
the automotive
insurance sector
6. Questions
To which of our stakeholder types do you belong?
(Big Data Provider, Big Data Technology Provider, Finance and Insurance
Sector, Research & Academia, Policy Maker, Standardisation Body, other)
Are you working with Big Data?
(Yes, No)
Are you interested in Big Data Technologies to optimize your
customer experience?
(Yes, No, Maybe)
What is the main barrier or risk preventing you from
implementing Big Data analytical solutions in your
organization?
(Costs, Lack of expertise, Uncertain Value (ROI))
21/05/20 6
7. Today’s Main Topic –
Big Data as a Self-Service Solution
I-BiDaaS overview
CaixaBank’s Pitches: Setting the requirements
I-BiDaaS architecture: Scientific & Technical view; how it
addresses the requirements set by CaixaBank.
Step by Step demonstration of I-BiDaaS solution and its
application to the banking sector.
Questions & Answers
21/05/20 7
8. Webinar Speakers
821/05/2020
Assistant Professor at the Department of
Mathematics and Informatics, Faculty of Sciences,
University of Novi Sad, Serbia
I-BiDaaS Scientific & Technical Manager.
Project Manager at
Security Innovation & Transformation,
CaixaBank, Barcelona
Dr. Dušan Jakovetić
University of Novi Sad, Serbia
Dr. Ramon Martin
de Pozuelo
CaixaBank
9. I-BiDaaS Application to the Financial Sector
Thursday, May 21, 2020 - 14:00-15:00 CEST
Big Data Pilot Demo Days
10. Dusan Jakovetic
Ass. Professor, University of Novi Sad, Faculty of Sciences, Serbia;
I-BiDaaS Scientific & Technical Manager
I-BiDaaS Overview
I-BiDaaS Application to the Financial Sector
Thursday, May 21, 2020 - 14:00-15:00 CEST
12. I-BiDaaS Consortium
1. FOUNDATION FOR RESEARCH AND TECHNOLOGY HELLAS (FORTH)
2. BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE
SUPERCOMPUTACION (BSC)
3. IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD (IBM)
4. CENTRO RICERCHE FIAT SCPA (CRF)
5. SOFTWARE AG (SAG)
6. CAIXABANK, S.A (CAIXA)
7. THE UNIVERSITY OF MANCHESTER (UNIMAN)
8. ECOLE NATIONALE DES PONTS ET CHAUSSEES (ENPC)
9. ATOS SPAIN SA (ATOS)
10. AEGIS IT RESEARCH LTD (AEGIS)
11. INFORMATION TECHNOLOGY FOR MARKET LEADERSHIP (ITML)
12. University of Novi Sad Faculty of Sciences Serbia (UNSPMF)
13. TELEFONICA INVESTIGACION Y DESARROLLO SA (TID)
1212
13. Motivation
13
EuropeanDataEconomy
Essential resource for growth,
competitiveness, innovation, job creation
and societal progress in general
Organizations leverage
data pools to drive value
The rise of the demand for platforms in the
market empowering end users to analyze
The convergence of internet of things
(IoT), cloud, and big data transforms our
economy and society
Self-service solutions are transformative
for organizations
Building a European Data Economy (Jan
2017)
Continue to struggle to turn opportunity
from big data into realized gains
Companies call upon expert analysts and
consultants to assist them
A completely new paradigm towards big
data analytics
The right knowledge, and insights decision-
makers need to make the right decisions.
Towards a common European data space
(Apr 2018)
Towards a thriving data-driven economy
(Jul 2014)
Digital Single Market
13
14. Our Vision
A complete and safe environment for
methodological big data experimentation
Tool and services to increase the
quality of data analytics
A Big Data as a Self-Service solution that helps
breaking industrial data silos and boosts EU's
data-driven economy
Tools and services for fast ingestion
and consolidation of both realistic
and fabricated data
Tools and services for the management
of heterogeneous infrastructures
including elasticity
Increases impact in research community
and contributes to industrial innovation
capacity
1414
15. Project Statement
I-BiDaaS aims to empower users to easily utilize and interact
with big data technologies, by designing, building, and
demonstrating, a unified framework that:
significantly increases the speed of data analysis while coping
with the rate of data asset growth, and facilitates cross-
domain data-flow towards a thriving data-driven EU
economy.
I-BiDaaS will be tangibly validated by three real-world,
industry-lead experiments.
1515
16. CAIXA
Enhance control of customers to online banking
Advanced analysis of bank transfer payment in
financial terminal
Analysis of relationships through IP address
CRF
Production process of aluminium casting
Maintenance and monitoring of production
assets
TID
Accurate location prediction with high traffic and
visibility
Optimization of placement of telecommunication
equipment
Quality of Service in Call Centers
Telecommunications
Industry
Banking/Finance Industry
Manufacturing Industry
Application / Experimentation
1616
18. Ramon Martín de Pozuelo
Project Manager at Security Innovation & Transformation at
CaixaBank
Setting the Pilot Requirements
I-BiDaaS Application to the Financial Sector
Thursday, May 21, 2020 - 14:00-15:00 CEST
19. 19
CaixaBank and the Use of Data
CaixaBank is the leading financial group in Spain,
both in banking and insurance and it is developing a
strategy of diversification with stakes in international
banks and also within leading service companies.
13.8M Customers
4.2K Branches
9.1K ATMs
4.8M Mobile Banking
5.8M On-line banking
32K Employees
In January 2014 CaixaBank created the Big Data Department
We manage 1.247 TB of information only in our Big data
Department of more than 100 internal people providing Data analytics services to all the
Organization.
Due to Regulation constraints all the infrastructure and the analysis is done internally
19
20. 20
Why do we need I-BiDaaS?
Requirements
Lack of agility in our Datapool for extracting data externally.
Security procedures and constraints are necessary but hinder and slow down data sharing
processes.
We have tons of data but confidential.
We are the data managers but the real owners of the data are our customers.
Current data sharing situation in CaixaBank and I-BiDaaS approach
Requirement Control
Data privacy
Data of customers (e.g., social graph) and external suppliers
(e.g., SIEM) can not be accessed or shared with other partners
Regulation
Compliance
All activities related to CaixaBank within the project must comply
with the relevant regulations (e.g., ISO 27001, GDPR)
Fraud and
Security
Analytics
Use cases presented will be related to the improvement of
security and the prevention of fraud.
Objective: Exploit the I-BiDaaS platform to gain agility, efficiency and flexibility in our analytics for security.
20
21. § Breaking external and cross-sectorial data silos while
complying Regulation
§ Sharing Data models with other FI
§ Sharing Data models with other sectors
§ Following ECB & Banco de España constraints, we already have
proved with I-BiDaas that this is possible
Why do we need I-BiDaaS?
21
22. § Secure Self-Service Infrastructure
§ Being able to outsource Big Data Infrastructure preserving
privacy & security
§ To grow in a dedicated and specialized infrastructure
§ To count on dedicated and specialized specialists
Why do we need I-BiDaaS?
22
23. § Competitiveness & Innovation
§ Fast, agile and specialized adaptation to new
technology.
§ Vs Current proprietary infrastructure
§ Efficiency
§ Reducing the costs and time of analyzing large datasets
Why do we need I-BiDaaS?
23
24. Dusan Jakovetic
Assistant Professor, University of Novi Sad, Serbia;
I-BiDaaS Scientific & Technical Manager
Big Data Architecture
I-BiDaaS Application to the Financial Sector
Thursday, May 21, 2020 - 14:00-15:00 CEST
25. • Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it
yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of
data analysis
Cope with the rate of
data asset growth
Intra- and inter-
domain data-
flow
Benefits of using I-BiDaaS
• Co-develop mode
The I-BiDaaS Solution: Front-end
• Co-develop: custom end-
to-end application
• Tokenize data
25
26. • Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it
yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of
data analysis
Cope with the rate of
data asset growth
Intra- and inter-
domain data-
flow
Benefits of using I-BiDaaS
• Co-develop mode
The I-BiDaaS Solution: Front-end
• Co-develop: custom end-
to-end application
• Tokenize data
26
27. • Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it
yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of
data analysis
Cope with the rate of
data asset growth
Intra- and inter-
domain data-
flow
Benefits of using I-BiDaaS
• Co-develop mode
The I-BiDaaS Solution: Front-end
• Co-develop: custom end-
to-end application
• Tokenize data
27
28. Specify data
fabrication rules
Fabricate & save the fabricated
data in the I-BiDaaS external
platform [e.g., ATOS cloud]
Visualize data/data sample
[e.g., graphs, charts, etc.]
Select pre-defined analytics from
the pool / use code template to
write a new script (COMPSs)
Set algorithm parameters/choose
default values
Tune alg. paramet. inside the
visualis. without coding [e.g.,
sliding window]
Save the Big Data
pipeline/analytics on the
local premises [e.g.,
company’s cluster]
Specify real data sources
in the local warehouse for
processing
Execute the saved
pipeline/analytics on the real
data
Visualize execution &
results
Extract new fabrication rules &
save the rules;
save the pipeline & analytics
Fabricate/Tokenize
Experiment&test
Runatlocalprem.&realdata
Execute a trial experiment;
Select amount of resources
visualize execution time & results
I-BiDaaS – Prototypical Experimental Workflow
28
29. Specify data
fabrication rules
Fabricate & save the fabricated
data in the I-BiDaaS external
platform [e.g., ATOS cloud]
Visualize data/data sample
[e.g., graphs, charts, etc.]
Select pre-defined analytics from
the pool / use code template to
write a new script (COMPSs)
Set algorithm parameters/choose
default values
Tune alg. paramet. inside the
visualis. without coding [e.g.,
sliding window]
Save the Big Data
pipeline/analytics on the
local premises [e.g.,
company’s cluster]
Specify real data sources
in the local warehouse for
processing
Execute the saved
pipeline/analytics on the real
data
Visualize execution &
results
Extract new fabrication rules &
save the rules;
save the pipeline & analytics
Execute a trial experiment;
Select amount of resources
visualize execution time & results
Fabricate/Tokenize
Experiment&test
Runatlocalprem.&realdata
I-BiDaaS – Experimental Workflow
29
30. The I-BiDaaS Solution:
Architecture/back-end
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPsProgramming
Model(BSC)
Query Partitioning
Infrastructurelayer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource managementand orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Data ingestion
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPsRuntime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
TestData
Fabrication(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learnedpatterns correlations
30
31. WP2:
Data, user interface, visualization
Technologies:
• IBM TDF
• SAG UM
• AEGIS AVT
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
http://ibidaas.eu/tools
31
32. Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
WP3:
Batch analytics
Technologies:
• BSC COMPSs
• BSC Hecuba
• BSC Qbeast
• Advanced ML (UNSPMF)
http://ibidaas.eu/tools
32
33. Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
WP4:
Streaming analytics
Technologies:
• SAG Apama CEP
• FORTH GPU-accel. analytics
http://ibidaas.eu/tools
33
34. Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
WP5:
Resource mgmt & integration
Technologies:
• ATOS Resource mgmt
• ITML integration services
http://ibidaas.eu/tools
34
35. Data fabrication capabilities
Solution flexibility
Easy to code programming paradigm
High code reusability
Key Features & Innovations
https://www.ibidaas.eu/deliverables
35
36. Data fabrication capabilities
Solution flexibility
Easy to code programming paradigm
High code reusability
CAIXA: From 3 months to 1-2
weeks for a new proof-of-
concept Big Data technology
Key Features & Innovations
https://www.ibidaas.eu/deliverables
36
37. Data fabrication capabilities
Solution flexibility
Easy to code programming paradigm
High code reusability
CAIXA: Bank transfers use case:
~3-4 less time to analysis
Key Features & Innovations
https://www.ibidaas.eu/deliverables
37
38. GPU-accelerated analytics; Synergy of CEP and GPU-
accelerated analytics for streaming data
Feedback from analytics to data fabrication
Feedback from analytics to problem modelling
Demonstrated on use cases across 3 different data providers
and 3 different industries
https://www.ibidaas.eu/deliverables
Key Features & Innovations (Cont’d)
38
39. Heterogeneous
Data Sources
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Data ingestion and
integration
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
https://www.ibidaas.eu/deliverables
I-BiDaaS Solution:
A CaixaBank Use Case Example
39
40. Heterogeneous
Data Sources
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Data ingestion and
integration
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
https://www.ibidaas.eu/deliverables
I-BiDaaS Solution:
A CaixaBank Use Case Example
Data fabrication:
75% time reduction for data
access by external
stakeholders
Real time analytics:
Detecting relations of
users in real time
Solution flexibility:
Found more useful
results than before the
project
40
41. Ramon Martín de Pozuelo
Project Manager at Security Innovation & Transformation at
CaixaBank
Financial Pilot: step by step
I-BiDaaS Application to the Financial Sector
Thursday, May 21, 2020 - 14:00-15:00 CEST
42. Current situation
§ Fraud Detection Analytics in CaixaBank
§ Currently, CaixaBank analytics are executed in-house.
§ Analytics lifecycle:
§ Security analysis data storage: DataPool (Oracle), Qradar (IBM)
§ Data exploration phase to build a model/query (expensive to run)
§ Execution of model/query on data in production mode
§ This process is executed periodically.
Data Pool
Data
exploration
Production
Analysis
Data model,
Query/program
42
43. CaixaBank Datapool Platform
Dataset recipe or
Tokenised Data
Analytics tools and
visualization
Data generation
Streams
Batch
I-BiDaaS Platform
Algorithms, expertise
I-BiDaaS solution
43
44. CaixaBank Data Roadmap
§ Synthetic data usage:
§ We explored the synthetic
data solution in the MVP.
§ New opportunities:
§ The possibility to work with
real data outside CaixaBank
in a secure way.
§ We moved from totally
synthetic approach to
tokenized real datasets.
§ We include a comparison
between synthetic and real
data to know better the
differences
44
45. Initial Data & use cases
Enhance control over
third party agencies
Facilitate the
analysis / detection
of connections
from external
suppliers.
Advanced analysis of
bank transfer payment
in financial terminal
Facilitate the
analysis / detection
of fraudulent
transfers through
Financial Terminal.
Analysis of
relationships through
IP address
Facilitate the
analysis / detection
of user
relationships with
the same
residential IP.
Building of a social
graph
Choose a graph-
oriented DB.
Test technologies
and tools for the
treatment of the
graph.
Validate the use of synthetic data for analysis, if the rules act in the same
situations as with the real data.
Establish testing
environment for new
Big Data tools without
sensitive data
constraints
45
46. Final Data & use cases
Enhance control of
customers to online banking
Facilitate the analysis /
detection of fraudulent
mobile to mobile bank
transfers in online
banking.
Advanced analysis of bank
transfer payment in financial
terminal
Facilitate the analysis /
detection of fraudulent
transfers through
Financial Terminal.
Analysis of relationships
through IP address
Facilitate the analysis /
detection of user
relationships with the
same residential IP.
Validate the use of synthetic
data for analysis, if the rules
act in the same situations as
with the real data.
Establish testing environment for new Big Data tools outside of
CaixaBank premises.
Open CaixaBank data to a wider community and explore novel data
analytics methodologies.
Syntheticand
realtokenizeddatasets
46
47. Use Case I-BiDaaS dataset Data
Enhance control of customers to
online banking
Online banking connections Real tokenized
Advanced analysis of bank transfer
payment in financial terminal
Bank transfers Real tokenized
Analysis of relationships through IP
address
IP address Synthetic / real
tokenized
Definition of the relationship
Synthetic Data Generation
Generation of non-
sensible data via rule
definition
Non-
sensitive
data Analytics
§ Business Goal:
The transaction between two people related
by an IP is not Fraud.
§ Use Case Goal:
Validation of Synthetic Data usage.
47
CAIXA 1st case MVP use case
54. Streamline CaixaBank processes to grant permits for data
access at the start of a project with an external provider
Streamline the process of establishing work environment
and scenario for PoCs without the need to use sensitive
data.
Synthetic data
54
How I-BiDaaS is helping us?
55. Big Data Analytics objective: Discover fraudulent scenarios from our data by analysing the presence or not of the client.
IDENTITY NUMBER
ADDITIONAL NOTES
TIME OF THE TRANSFER
Ensure the security of our
data: Decide what are we
going to share clear, tokenized
or encrypted.
The challenge relies on finding
the limit of what and how real
data can be shared to comply
to regulation and not lose
aditional and valuable
information for analytics.
Use case high-level objective: Breaking internal and external silos
Financial Operation Data, Security Management (SIEM), etc.
1.000kk of rows per month
55
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
56. Point of view:
• Final User
• Programmer
CONTEXTOBJECTIVES
• Identify and glue events, followed by enriching the transfer payment dataset
• Encrypt the data without lossing value
• Advanced analytics
56
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
57. 57
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
58. 58
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
59. Comparing solutions and processes to analyse
data outside CaixaBank premises
Data Analytics
comercial products
59
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
60. 60
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
61. 61
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
62. DataRobot comparison results
• Custom solutions:
• I-BiDaaS provide more flexibility in the definition of your own code, scoring metrics, etc.
• Unsupervised learning:
• DataRobot has very limited number of unsupervised learning models. I-BiDaaS can provide
much more detailed results on unsupervised learning use cases.
62
CAIXA 2nd case
Analyse bank transfers executed from employees financial terminal in the name of a customer.
Potential fraudulent transfer or bad practices (e.g. check that the client was present in the time of
the movement.)
63. Establish testing environment for new Big
Data analytics tools outside of CaixaBank
premises.
Open CaixaBank data to a wider community
and explore novel data analytics
methodologies.
High-level objective: Breaking internal and external silos
Financial Operation Data, Security Management (SIEM), etc.
Real Tokenised Data
63
How I-BiDaaS is helping us?
64. Benefits KPIs
To increase the efficiency and competitiveness in the
management of its vast and complex amounts of data.
75% time reduction data access from external
stakeholders using synthetic data (From 6 to
1.5 days).
To break data silos not only internally, but also fostering and
triggering internal procedures to open data to external
stakeholders.
Real data accessed by at least 6 different
external entities skipping long-time data
access procedures.
To evaluate Big Data analytics tools with real-life use cases of
CaixaBank in a much more agile way.
I-BiDaaS overall solution and tools
experimentation with 3 different industrial
use cases with real data.
64
CaixaBank benefits from I-BiDaaS
65. Next Pilot Demo Day 28 May
BigDataStack Connected Consumer
Marieke Willems (Trust-IT Services)
m.willems@trust-itservices.com
Big Data Pilot Demo Days
66. BigDataStack Connected Consumer Demo
21/05/20 66
I-BiDaaS Application for the Financial Sector showcased Big Data as a self-service solution that
empowers the employees of the CaixaBank. BigDataStack Connected Consumer use-case
offers retailers the real-time services to improve their customer experience.