1) London Transport is developing an Operations Digital Twin to provide a real-time simulation of traffic conditions on London's roads.
2) The Digital Twin integrates multiple real-time and historical data sources into a common framework and graph database aligned by road links and time.
3) This allows the Digital Twin to identify traffic incidents and disruptions, help manage traffic, and support planning and analysis across Transport for London.
SITA WorldTracer - the global Lost and Found solution built on Neo4j cuts costs and speeds delivery at airports worldwide by returning lost property to travelers.
Sopra Steria: Intelligent Network Analysis in a Telecommunications EnvironmentNeo4j
The Intelligent Network Analyzer (INA) uses the graph database by Neo4j to build a digital twin of the mobile telecommunications network. Based on this digital twin, INA can be used to efficiently perform various analyses to support network operators in their daily business. In our talk, we will show some features of INA and explain how they draw on the particular strengths of the Neo4j graph database.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
SITA WorldTracer - the global Lost and Found solution built on Neo4j cuts costs and speeds delivery at airports worldwide by returning lost property to travelers.
Sopra Steria: Intelligent Network Analysis in a Telecommunications EnvironmentNeo4j
The Intelligent Network Analyzer (INA) uses the graph database by Neo4j to build a digital twin of the mobile telecommunications network. Based on this digital twin, INA can be used to efficiently perform various analyses to support network operators in their daily business. In our talk, we will show some features of INA and explain how they draw on the particular strengths of the Neo4j graph database.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Sopra Steria: Intelligent Network Analysis in a Telecommunications EnvironmentNeo4j
The Intelligent Network Analyzer (INA) uses the graph database by Neo4j to build a digital twin of the mobile telecommunications network. Based on this digital twin, INA can be used to efficiently perform various analyses to support network operators in their daily business. In our talk, we will show some features of INA and explain how they draw on the particular strengths of the Neo4j graph database.
Building Biomedical Knowledge Graphs for In-Silico Drug DiscoveryVaticle
The rapid development and spread of analytical tools in the biomedical sciences has produced a variety of information about all sorts of biological components and their functions. Though important individually, their biological characteristics need to be understood in relation to the interactions they have with other biological components, which requires the integration of vast amounts of complex, semantically-rich, heterogenous data.
Traditional systems are inadequate at accurately modelling and handling data at this scale and complexity, making solutions that speed up the integration and querying of such data a necessity.
In this talk, we present various approaches being used in organisations to build biomedical computational pipelines to address these problems using tools such as Machine Learning and TypeDB. In particular, we discuss how to create an accurate and scalable semantic representation of molecular level biomedical data by presenting examples from drug discovery, precision medicine and competitive intelligence.
Speaker: Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle, dedicated to building a strongly-typed database for intelligent systems. He works directly with TypeDB's open source and enterprise users so they can fulfil their potential with TypeDB and change the world. He focuses mainly in life sciences, cyber security, finance and robotics.
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Andrea Bielli, IT Architect Global Digital Solution, Enel
Davide Gimondo, Software Engineer, Enel
Enel mostra come neo4j aiuta nella gestione delle reti elettriche in 8 paesi nel mondo.
Con l’obiettivo di ottimizzare gli algoritmi di percorrenza della rete elettrica, in modo da rendere le reti sempre più efficienti e resilienti.
L’obiettivo di Enel è una gestione ottimale della topologia della rete per garantire gli obiettivi del gruppo: la transizione energetica e l’elettrificazione dei paesi in cui opera, verso l’obiettivo Net Zero, relativo alla riduzione delle emissioni nella produzione e distribuzione dell’energia elettrica.
ASTRAZENECA. Knowledge Graphs Powering a Fast-moving Global Life Sciences Org...Neo4j
AstraZeneca share their experience of share their experience of building a knowledge graph platform and central service, to power the next generation of insights and analytics at AstraZeneca.
Data product thinking-Will the Data Mesh save us from analytics historyRogier Werschkull
Data Mesh: What is it, for Who, for who definitely not?
What are it's foundational principles and how could we take some of them to our current Data Analytical Architectures?
Understanding DataOps and Its Impact on Application QualityDevOps.com
Modern day applications are data driven and data rich. The infrastructure your backends run on are a critical aspect of your environment, and require unique monitoring tools and techniques. In this webinar learn about what DataOps is, and how critical good data ops is to the integrity of your application. Intelligent APM for your data is critical to the success of modern applications. In this webinar you will learn:
The power of APM tailored for Data Operations
The importance of visibility into your data infrastructure
How AIOps makes data ops actionable
DataOps: An Agile Method for Data-Driven OrganizationsEllen Friedman
DataOps expands DevOps philosophy to include data-heavy roles (data engineering & data science). DataOps uses better cross-functional collaboration for flexibility, fast time to value and an agile workflow for data-intensive applications including machine learning pipelines. (Strata Data San Jose March 2018)
Streetlight poles and luminaires are ideal hosts for connecting sensors. They provide fine-grained information about the urban environment, and are used to provide adaptive lighting but also feed into many municipal systems and departments. Examples include minute-by-minute traffic analytics or street-by-street air quality monitoring.
But if street lighting is to become the catalyst for “smart city” applications, lighting professionals need to understand when to harness sensor data, and when to consider application data or predictive “big data”. The world is changing and we need to take a wider view. Keith will focus on deployed use cases to help to make sense of the practical and economic implications of these important developments.
Talk by Keith Henry AMILP, Telensa
LIDAR Magizine 2015: The Birth of 3D Mapping Artificial IntelligenceJason Creadore 🌐
Artificial intelligence (AI) has the potential to take the LiDAR
mapping market into hypergrowth. Following Moore’s law, with computation capacity doubling every 2 years, it is now possible for point cloud feature extraction to outpace the speed of data generation from laser scanning systems using artificial intelligence.
Sopra Steria: Intelligent Network Analysis in a Telecommunications EnvironmentNeo4j
The Intelligent Network Analyzer (INA) uses the graph database by Neo4j to build a digital twin of the mobile telecommunications network. Based on this digital twin, INA can be used to efficiently perform various analyses to support network operators in their daily business. In our talk, we will show some features of INA and explain how they draw on the particular strengths of the Neo4j graph database.
Building Biomedical Knowledge Graphs for In-Silico Drug DiscoveryVaticle
The rapid development and spread of analytical tools in the biomedical sciences has produced a variety of information about all sorts of biological components and their functions. Though important individually, their biological characteristics need to be understood in relation to the interactions they have with other biological components, which requires the integration of vast amounts of complex, semantically-rich, heterogenous data.
Traditional systems are inadequate at accurately modelling and handling data at this scale and complexity, making solutions that speed up the integration and querying of such data a necessity.
In this talk, we present various approaches being used in organisations to build biomedical computational pipelines to address these problems using tools such as Machine Learning and TypeDB. In particular, we discuss how to create an accurate and scalable semantic representation of molecular level biomedical data by presenting examples from drug discovery, precision medicine and competitive intelligence.
Speaker: Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle, dedicated to building a strongly-typed database for intelligent systems. He works directly with TypeDB's open source and enterprise users so they can fulfil their potential with TypeDB and change the world. He focuses mainly in life sciences, cyber security, finance and robotics.
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Andrea Bielli, IT Architect Global Digital Solution, Enel
Davide Gimondo, Software Engineer, Enel
Enel mostra come neo4j aiuta nella gestione delle reti elettriche in 8 paesi nel mondo.
Con l’obiettivo di ottimizzare gli algoritmi di percorrenza della rete elettrica, in modo da rendere le reti sempre più efficienti e resilienti.
L’obiettivo di Enel è una gestione ottimale della topologia della rete per garantire gli obiettivi del gruppo: la transizione energetica e l’elettrificazione dei paesi in cui opera, verso l’obiettivo Net Zero, relativo alla riduzione delle emissioni nella produzione e distribuzione dell’energia elettrica.
ASTRAZENECA. Knowledge Graphs Powering a Fast-moving Global Life Sciences Org...Neo4j
AstraZeneca share their experience of share their experience of building a knowledge graph platform and central service, to power the next generation of insights and analytics at AstraZeneca.
Data product thinking-Will the Data Mesh save us from analytics historyRogier Werschkull
Data Mesh: What is it, for Who, for who definitely not?
What are it's foundational principles and how could we take some of them to our current Data Analytical Architectures?
Understanding DataOps and Its Impact on Application QualityDevOps.com
Modern day applications are data driven and data rich. The infrastructure your backends run on are a critical aspect of your environment, and require unique monitoring tools and techniques. In this webinar learn about what DataOps is, and how critical good data ops is to the integrity of your application. Intelligent APM for your data is critical to the success of modern applications. In this webinar you will learn:
The power of APM tailored for Data Operations
The importance of visibility into your data infrastructure
How AIOps makes data ops actionable
DataOps: An Agile Method for Data-Driven OrganizationsEllen Friedman
DataOps expands DevOps philosophy to include data-heavy roles (data engineering & data science). DataOps uses better cross-functional collaboration for flexibility, fast time to value and an agile workflow for data-intensive applications including machine learning pipelines. (Strata Data San Jose March 2018)
Streetlight poles and luminaires are ideal hosts for connecting sensors. They provide fine-grained information about the urban environment, and are used to provide adaptive lighting but also feed into many municipal systems and departments. Examples include minute-by-minute traffic analytics or street-by-street air quality monitoring.
But if street lighting is to become the catalyst for “smart city” applications, lighting professionals need to understand when to harness sensor data, and when to consider application data or predictive “big data”. The world is changing and we need to take a wider view. Keith will focus on deployed use cases to help to make sense of the practical and economic implications of these important developments.
Talk by Keith Henry AMILP, Telensa
LIDAR Magizine 2015: The Birth of 3D Mapping Artificial IntelligenceJason Creadore 🌐
Artificial intelligence (AI) has the potential to take the LiDAR
mapping market into hypergrowth. Following Moore’s law, with computation capacity doubling every 2 years, it is now possible for point cloud feature extraction to outpace the speed of data generation from laser scanning systems using artificial intelligence.
DEVNET-1145 How APIs are Driving City DigitizationCisco DevNet
Smart-city solutions connected over an intelligent network platform are the foundation for city digitization. Cisco now offers application program interface (API) tools for developers to create applications for smart cities that frame and focus big-data streams, delivering relevant and timely content to improve city operations and enhance daily life. See how select visionary cities are already working with Cisco to leverage Smart+Connected Communities solutions and how an enormous opportunity now exists to develop applications that transform the data into useful information for city leaders, businesses, citizens and visitors as well as for use in other city processes. Cities are attracting new businesses and entrepreneurs and generating an economic boom in application development to meet urban service requirements of every stripe in cities today and broadly drive city digitization.
A Big Data Telco Solution by Dr. Laura Wynterwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Intelligent Mobility: Business Value of IoT and ML in LogisticsBigML, Inc
BigML’s partners, A1 Digital, introduce how the Internet of Things and Machine Learning can bring business value in Logistics.
Speaker: Francis Cepero, Head of Vertical Market Solutions at A1 Digital.
*Intelligent Mobility 2021: Virtual Conference.
TfL has been working on broader integration projects where we focus to get the most efficient use of our road networks and public transport. We bring together a wide range of data from multiple disconnected systems which we not only use for operational purposes, but also make more of them open and available; in real time. This session presents how we brought together IoT and Big Data techniques to understand current and predicted transport network status and plan to evolve the base solution into a broader product.
Asset information and data management smart railJames Nesbitt
The convergence of technology and infrastructure has the ability to transform our communities and economy, reduce emissions as well provide an opportunity for business leaders to optimise asset performance and reduce cost.
Asset information and data management will allow more precise decisions to be made to balance cost, risk and performance, supporting operational effectiveness and efficiency.
We will be addressing how the European rail sector are developing and implementing asset information strategies, managing data across multiple disparate systems and leveraging new technologies to succeed.
CITE Start Thinking Big Data 2019 01-30 FINALJon Kostyniuk
Whatever the size or type of organization, Big Data has permeated our transportation industry. It is no longer a question of IF Big Data will be useful, but instead WHY is it useful and HOW can we best apply it. This presentation aims to address how we can leverage existing services and available partnerships in transportation, consider new and emerging technologies, and determine strategy for what’s to come in transportation, including connected and autonomous vehicles. While it may be a huge challenge to solve transportation problems with Big Data, it can help us make better travel decisions today and plan for better infrastructure tomorrow.
Transport for London: Using data to keep London movingWSO2
This talk was presented by Sriskandarajah Suhothayan (WSO2) and Roland Major (Transport for London) at the Strata Data Conference in London, May 23 2017.
Transport for London (TfL) uses a wide range of data for operational purposes, but the underlying data is typically held in multiple disconnected systems. Freedom of Information requests have helped prove the value of sharing this data. TfL is embarking on a journey to make more of this data open and available in real time.
TfL and WSO2 have been working together on broader integration projects. Roland Major and Sriskandarajah Suhothayan share the evolving big data and IoT architectures and services TfL is building to pull together these diverse datasets to better support operational teams and accelerate the identification and classification of disruption to improve response times for incidents. In particular, they explore WSO2’s solution, which emerged from the Data in Motion hackathon organized by TfL, AWS, and Geovation. The solution innovates TfL’s heterogeneous data sources through the combination of the TfL Unified API and its operational data sources, including traffic sensor, air quality, and passenger flow data, to provide better travel time and transit suggestions for Londoners and tourists using the WSO2 Data Analytics Server, WSO2 Complex Event Processor, and WSO2 API Manager, bringing together IoT and big data techniques to feed a real-time dashboard of current and predicted transport network status.
THE DEVELOPMENT AND STUDY OF THE METHODS AND ALGORITHMS FOR THE CLASSIFICATIO...IJCNCJournal
This paper represents the results of the research, which have allowed us to develop a hybrid
approach to the processing, classification, and control of traffic routes. The approach enables to
identify traffic flows in the virtual data center in real-time systems. Our solution is based on the
methods of data mining and machine learning, which enable to classify traffic more accurately
according to more criteria and parameters. As a practical result, the paper represents the
algorithmic solution of the classification of the traffic flows of cloud applications and services
embodied in a module for the controller of the software-defined network. This solution enables to
increase the efficiency of handling user requests to cloud applications and reduce the response
time, which has a positive effect on the quality of service in the network of the virtual data center
Techniques to Minimize State Transfer Cost for Dynamic Execution Offloading I...IJERA Editor
The recent advancement in cloud computing in cloud computing is leading to and excessive growth of the mobile devices that can become powerful means for the information access and mobile applications. This introducing a latent technology called Mobile cloud computing. Smart phone device supports wide range of mobile applications which require high computational power, memory, storage and energy but these resources are limited in number so act as constraints in smart phone devices. With the integration of cloud computing and mobile applications it is possible to overcome these constraints by offloading the complex modules on cloud. These restrictions may be alleviated by computation offloading: sending heavy computations to resourceful servers and receiving the results from these servers. Many issues related to offloading have been investigated in the past decade.
Similar to Transport for London - London's Operations Digital Twin (20)
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
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.
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.
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.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
2. 2
Operations Digital Twin - The Presentation
Digital Twin
• Definition and a simple model of complexity
Context of London Road Space
• Business requirements – New paradigm
• Proof of Concept – ULEX zone – October 25, 2021 – Demo
Components and Critical Factors for Success
• The five layers of the Operations Digital Twin
Applications and Next Steps
Thoughts on What it takes to Deliver a Successful Digital Twin
3. 3
What is Digital Twin - Definition
A digital twin can be defined as an integrated simulation of a
real-life system that uses models, sensor information and
input data to mirror, predict and control the activities and
performance of its corresponding physical twin.
Kraft, E. M. (2016).
Two essential elements stand out:
1. There is a connection between the physical model and the corresponding virtual model and
2. this connection is established by generating real-time data (e.g., through using sensors).
4. 4
A Simple Model of Digital Twin Complexity
1 = Simple 5= Advanced
A basic model of a map.
Click to add text
.
Some capacity for feedback
and control,
Provide some predictive
maintenance, analytics and
insights
Capacity to learn efficiently &
automated recommendations
Ability to autonomously
reason & replace humans
Click to add text
Autonomy - The autonomy
of a system
Intelligence - Can it replicate
human cognitive processes
and perform tasks.
Learning
The ability of the system to
learn from data
Fidelity - The level of detail:
• No of parameters,
• Synchronisation frequency
• Accuracy
6. 6
Context
for Digital
Twin Data
London Road Network – Challenges
Factors that
make it
difficult to
deliver
effective
intelligence
Data Network Attributes
Open network with little
control over demand
Heterogenous road
layout especially London
Dynamic changes in
network design and layout
Don’t know how to
measure demand and
capacity effectively
Poor quality data
Poor spatial and temporal
coverage
Lack of Cycle, Ped and
freight data
Poor innovation in
sensors and new data
§Understand network
outcomes but not what
influences them
Network Properties
Stochastic properties
of traffic because they
are driven by driver
behaviour
Exhibit “Emergent
behaviour” and the
same values for many
measures can be
arrived at for different
traffic assignment
configurations on the
network
7. 7
Disparate Data Sets
Explain outcomes
Real Time Data
Cars to Multi Modal
Lack of sensors
Changing objectives
Trips and Routing
One version of the truth
What caused the problem?
Operational responsiveness
Multiple objectives, safety, AQ, etc.
Constant improvement data quality
Flex priorities by location and time of day
How did our customers respond?
Why Build a Digital Twin for the London Road Network?
Business Needs
Business Challenge
Framework to Align Data
Lots of Meta-data
Graph Database
Agile & Adaptive Response
Scalability & Data Fusion
Low Granular Data
Graph Database
How to Respond?
8. 8
Normal Paradigm Alternative Paradigm
• Adapt a current business data architecture
• Build a bespoke application to answer a set of very
specific requirement/s
• Scope creep leads to over engineering
Ends in Redundancy
What if we turn this on its head and built a model
that could:
• Answer 90% of all business cases
• Respond to changes in business needs
• Can answer a range of complex problems
• Scale and adapt to new data sources and London
road contexts
This thought process led to the
Operations Digital Twin
New Technologies and Capabilities allows us to rethink
this:
Cloud Services / Graph Data Bases / Open-Source
software, e.g., R / Data Science skills
10. 10
Digital Twin Proof
of Concept
Introduction of
ULEX – 25th
October 2021
Detected 5
Incidents unseen
by the Traffic
Control Centre
11.
12. 12
Process
Business requirements were assessed across the full
customer base, Network Management, TDM, City Planning,
sponsorship etc.
Data
components
Example
outcomes
Each requirement is broken down to its lowest granularity in
space and time. Focusing on key roads dimensions such as
JT's, flows, OD's, trips & Metadata
Tactical level
vehicle
emissions
Delta – speeds
Mode share
Demand & JTs
AIR QUALITY
Different data brought together in a Common Framework same space and time
Road Safety both
Links and Junctions
Prioritise PT
Bus JTs
Peds crossing at junctions
Mode demand link
Modal speeds by link
Bus demand & JT
GT demand & JT
Roadworks etc
ROAD SAFETY BUS PRIORITY
1. Digital
Twin Data
14. 14
• Aggregated main strategic roads only
• Split by Major junction; Traffic control points; Road characteristics; Directionality
• Process creation from OS Highways to CORN fully automated in FME renewed
every 6 months
Rules/Principles
Common Operational Road Network - CORN
An operational common road geography which will align road network control and reporting
60,000 inks for London including Boroughs 1:7 decrease on OS Highways, avg 200 m
• Computationally efficient
• Common geography for whole business to use
• Appropriate scale for operational reporting and road network control
2. Framework
15. 15
CORN
Analysis
Decision
Support
Predictive
analysis
Input
Data
Aligned Layer Output Function Business Use
Alignment of Data and Analytics in Space and Time
Incident
identification
Visuals
Traffic
Management
Modelling
Analysis
Business
Cases
Big Data
Sets
Assets
Works –
planned
events
Traffic
management
Inputs
include all
Meta Data
Such as
Road
attributes;
Operating
Parameters
2. Framework
16. 16
.
• A Database of nodes, properties and relationships. The power behind Google, Twitter, etc.....
• Nodes are mapped to a framework of 60,000 London road links
• Node relationships are directional and allow millisecond calculations in real time
• All data is aligned spatially and temporally in real time
• It allows cause effect relationships to be established
3. Graph
Database
Technology
Model the Road Network
• Include all contextual Metadata
• Map key relationships between
entities by link, junction and direction
17. 17
Using the CORN, our directed road network, and the mirroring of our key
live road metrics we can then use the power of the graph to find and
measure disruption.
• CLUSTERING - Find clusters of connected roads where each segment’s
metrics are worthy of attention.
• CENTRALITY – Within each cluster find the least connected nodes to
get the outermost points.
• ROUTING – Within each cluster, calculate all possible routes between
the outermost points….
Then we can provide a real-time estimate on the impact the average road
user would experience in delay on any of those routes.
3. Graph
Database Ana
lytics
The primary business case for the development of the
digital twin is the detection of incidents
18. 18
Representing Reality in Real Time on London’s Roads
4. Visual Layer
• Cognitive Efficiency
• CORN Link design
• Keep it Simple – Toggle data layers
• Scaling, colours and line widths
• Focus on outcomes then causal factors
• Representing Reality in Real Time
• Partition data by meaningful thresholds
• Slow data sufficiently so it reflects reality
• Provide controls for operator to change
thresholds
• Enable data labels for context
19. 19
Visualisation of our data allows us to make better
decisions in the future.
e.g., COVID impact on traffic flows in London
4. Visual Layer
20. 20
A Modular Design allows for Partitioning of the Workload
5. Plug and Play
Situational Awareness
Air Quality / Emissions 3 D – Operations LIVE
Planning and Context
22. 22
Operations Digital Twin – Next Steps
Incident Detection Module
• Utilise Graph technology across different data sets to provide real time alerts
Decision Support System in the London Network Management Control Centre
• Expand the use of Graph technology to understand assignment across the network
Operations Live – 3 D – real time in London
• With the modelling team develop the module to be used for virtual 3D Planning and Stakeholder
Management
23. 23
Operations Digital Twin – Thoughts 1
Stick to the principle that the Business only wants one version of the truth
Be clear in understanding the path between your data and the delivery of key business insights
Be clear on purpose and objectives:- Don’t over-reach what a Digital Twin can deliver
A Digital Twin needs context:- Invest heavily in your Metadata and align via a Framework with your Graph
Database
Think through what the Graph Database can deliver by utilising its functionality. Leverage that functionality via
your Meta-data. It adds massive value to what you can do with your data
Think through how a modular design ( 5 layers in our DT) can by judicious re-combination and aggregation of your
data provide for multiple outcomes. Ultimately it simplifies your architecture and allows you to achieve much more
24. 24
Operations Digital Twin – Thoughts 2
Success needs strategic alignment of different layers
Business Requirements and Data – Break problems into their data components
and think about how you can reassemble them to answer many different questions
The Business Framework – Aligns data into same geography and time and allows you to leverage metadata. But
you must invest time and energy into your businesses metadata
The Graph Database – strategic alignment with your framework allows you deliver in real time but also to
multiply to your advantage the algorithms in the graph database, e.g., Clustering and Routing
Visualisation Layer – Achieve cognitive and computational efficiency. Make it usable by humans
Plug and Play – A modular design partitons the workload. Think about the digital, framework and graph
database componets as your real time provisoning layer of contextual data - let different modules take the strain
of processing specific algorithms to provide business intelligence