Rail Ticketing Assistance from the Graph Way, KCOM

•Download as PPTX, PDF•

1 like•226 views

This document summarizes the challenges faced by a transportation ticketing system and how a graph database solution using Neo4j was implemented to address these challenges. The transportation system had to handle flexible bookings, integration of different data sources, and complex reporting and demand management needs. Testing showed Neo4j could meet the performance requirements, handling over 10,000 transactions per second with response times under 3 milliseconds. Future plans include further load testing and upgrading to new Neo4j features to improve availability.

Technology

Rail Ticketing Assistance from the Graph Way
Andy Smale

2
Agenda
KCOM and Me - Introduction
The challenges in transport
Solutions and why neo4j?

3
Provides communications services and IT solutions to
organisations and consumers
£350m turnover, 1700 employees
Head Office: Hull, UK City of Culture 2017
Development Base: Ipswich, East Anglia

4
Contact & Collaboration
Cloud
Development
Integration
AWS
Azure I&AM
API
Java Microsoft
UI/UX
Project
Management
Security
Business
Analysis
Testing
Digital
Databases
Data
Voice
Video
Architecture

5
“A bit of a Geek”
Farmer in Devon
Got a Sinclair ZX80 kit. BASIC!
Developed in C, Pascal and Prolog
Student of Artificial Intelligence
Solution Architect
Work in Mobile and Transport Sectors
neo4j Certified Architect

6
Transport ticketing - Functional Overview

7
Transport Service Challenges
Multiple businesses
Thousands of locations
Flexible retailing of products
Fulfilled in different formats (paper, barcode, ITSO)
>30,000 Services per day, 1.7billion journeys/year
Layers of complexity
It even looks like a graph!

8
API challenges
Flexible bookings
- File-based
- Existing APIs are fine-grained
- Aligned with entity/table
- Clients relate data
- Personal Identifiable Data

9
Integration and APIs
Fast imports of file-based data feeds
Design new, richer APIs
• Unrestricted by underlying data models
• More “graph-friendly”
Adapt legacy APIs in service layer
Migrate clients to richer APIs
Data Protection

10
Demand Management Best Product at the right Price
at the right Time
Capacity
DemandPrice
products
Yield
Management
products

11
Demand Management Solutions
Nests, groups, hierarchies, rules… all configurable
• “Sweet spot” for graph technology
Decision Support System integration
Dynamic updates
• Ripple through the graph
• “What if…?” modelling
Secure, role-based access protects strategies

12
Reporting
Real-time Operational Reports
Data exports
Scheduled reports
Ad hoc Reports
Data Analysis
Streaming Updates

13
Reporting Solutions
Delivered in real time through published interfaces
Ability to monitor and update based on changes
Dedicated read replicas serving operational reports
Analytic tool integration
Visualisation tooling
Integration with AWS Kinesis for streaming of data

14
Technical Architecture Challenges
100% Availability Targets
Fast Recovery from Failure
Resilient to Network Issues
Redundancy built in to Components
Automated build and deploy
Fast Performance (<50ms writes)
Consistently scalable for reads (<30ms, 10k TPS)
Thousands of Client systems

15
Software Engineering Solution
Highly Relational Complex Data Model
Thousands of queries per second
Consistent Writes (hundreds/sec)
Security of Commercial Strategies (Yield)
Break the problem into bounded domains
Autonomous Software Components
Continuous Integration and Load Testing
Proactive Monitoring and Alerting

16
Why do we choose Neo4J?
Data Model
Transactional
Scalable
Reliable
Highly Available
Consistent Fast Query Response
Enterprise Grade Support

17
How did we validate Neo4J?
Building cluster (on v2.6)
Early Load Test
Simple data model
Created using Stored Procedure
Load injectors
10,000 TPS @ <3ms reads

18
And What’s Next?
Full-Scale Automated Load Testing
• Adding demand management
Causal Clustering
• Write performance impact
Edge services
• Replication lag monitoring
Blue-Green Application Upgrades ->3.2
• Aim for zero downtime upgrades

Thank you
Contact
Andy Smale
E andrew.smale@kcom.com
T 01473 421421

This document discusses an approach to enterprise metadata integration using a multilayer metadata model. Key points include: - Status dashboards provide facts from technical, operational, application, and quality metadata layers - A graph database allows for context exploration across the entire cluster - The integration of metadata from multiple sources provides a more holistic view of business knowledge

Using Neo4j and Machine Learning to Create a Decision Engine, CluedIn

Neo4j

This document discusses taking the "magic" out of machine learning by using a practical approach with Neo4j and machine learning techniques. It describes building a decision engine with Neo4j that can learn from its decisions over time using unsupervised neural networks and pattern matching combined with statistical models. While machine learning techniques are good at solving certain problems, the document advocates that the simple idea of having a weighted decision engine that can persist graph decisions asynchronously can get very far without needing to be super fast or real-time.

H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI...

Sri Ambati

This talk was recorded in London on October 30, 2018. KNIME Analytics Platform is an easy to use and comprehensive open source data integration, analysis, and exploration platform, enabling data scientists to visually compose end to end data analysis workflows. The over 2,000 available modules ("nodes") cover each step of the analysis workflow, including blending heterogeneous data types, data transformation, wrangling and cleansing, advanced data visualization, or model training and deployment. Many of these nodes are provided through open source integrations (why reinvent the wheel?). This provides seamless access to large open source projects such as Keras and Tensorflow for deep learning, Apache Spark for big data processing, Python and R for scripting, and more. These integrations can be used in combination with other KNIME nodes meaning that data scientists can freely select from a vast variety of options when tackling an analysis problem. The integration of H2O in KNIME offers an extensive number of nodes and encapsulating functionalities of the H2O open source machine learning libraries, making it easy to use H2O algorithms from a KNIME workflow without touching any code - each of the H2O nodes looks and feels just like a normal KNIME node - and the data scientist benefits from the high performance libraries and proven quality of H2O during execution. For prototyping these algorithms are executed locally, however training and deployment can easily be scaled up using a Sparkling Water cluster. In our talk we give a short introduction to KNIME Analytics Platform and then demonstrate how data scientists benefit from using KNIME Analytics Platform and H2O Machine Learning in combination by using a real world analysis example. Bio: Christian received a Master’s degree in Computer Science from the University of Konstanz. Having gained experience as a research software engineer at the University of Konstanz, where he developed frameworks and libraries in the fields of bioimage analysis and machine learning, Christian moved on to become a software engineer at KNIME. He now focuses on developing new functionalities and extensions for KNIME Analytics Platform. Some of his recent projects include deep learning integrations built upon Keras and Tensorflow, extensions for image analysis and active learning, and the integration of H2O Machine Learning and H2O Sparkling Water in KNIME Analytics Platform.

R, Spark, Tensorflow, H20.ai Applied to Streaming Analytics

Kai Wähner

Data Preparation vs. Inline Data Wrangling in Data Science and Machine Learning

Kai Wähner

Comparison of Data Preparation vs. Data Wrangling Programming Languages, Frameworks and Tools in Machine Learning / Deep Learning Projects. A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project. This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models. Key takeaways for the audience: - Learn various options for preparing data sets to build analytic models - Understand the pros and cons and the targeted persona for each option - See different technologies and open source frameworks for data preparation - Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation Video Recording / Screencast of this Slide Deck: https://youtu.be/2MR5UynQocs

Neo4j GraphTalk Florence - Introduction to the Neo4j Graph Platform

Neo4j

1) Neo4j is a native graph database platform that allows users to store, reveal, and query data relationships in real-time. It is designed specifically for graph databases. 2) Graph databases represent data as nodes and relationships, which provides a more connected view of data compared to relational databases. This connected view of data drives insights and applications in areas like recommendations, fraud detection, and knowledge graphs. 3) Neo4j has over 250 enterprise customers across industries like retail, financial services, and telecom. It is widely used for applications like recommendations, fraud detection, network analysis, and knowledge graphs.

Network and IT Ops Series: Build Production Solutions

Neo4j

Jeff Morris, Director, Neo4j:Are you building a breakthrough product or extending an existing one? Do you need introduce new capabilities based on insights from data relationships? If so, you should consider embedding a graph database. For software providers building products to assure quality network operations or security, using an embedded graph database may open new customer opportunities. Watch this webinar to learn how you can easily differentiate your applications and take your solutions to market faster with a native graph database like Neo4j.

Munich Re: Driving a Big Data Transformation

DataWorks Summit

Data and analytics are at the heart of the digital transformation. Implementing a modern data platform can be challenging; moreover, success requires a shift in culture. Andreas will discuss the ways Munich Re drives cultural and technological change within their company, focusing on three key elements: people, processes, and technology. What does it mean to be a data-driven organization? How can we provide self-service analytics to our internal and external customers in an agile way? How do we get the most value out of our big data lake? How does Munich Re balance technology and culture to meet the data demands of their business? Speaker Andreas Kohlmaier, Head of Data Engineering, Munich Re

Standard Bank South Africa is a Hortonworks client, with several multi-node clusters hosting Hortonworks Data Platform (HDP) and Hortonworks Data Flow (HDF). This presentation will discuss the technical detail of implementing security, governance and multi-tenancy on a "Data Lake" within the finance industry. The talk will address the team's experiences, challenges, failures and learnings that we took away from this behemoth of an adventure. After introducing Standard Bank and the Hadoop admin team, the presentation will describe the security and governance journey Standard Bank has undergone since the project's inception in 2015, as well as the roadmap for the future ahead. Presentation structure: 1. Team introduction with background information 2. Environment overview (Where we are - Current) -----Security ---------Authentication through Kerberos and LDAP/ AD ---------Authorization through Ranger and Centrify ---------Transparent Data Encryption (TDE) at rest -----Governance ---------Centralized auditing ---------Ranger policies and data steward ownership -----Multi-Tenancy ---------Data lake Vs. data analytics platform ---------Edge nodes Vs. API framework through Knox 3. How did we get to this stage? (Past) -----Challenges faced (Kerberos, AD integration, SSL) -----How we overcame these challenges 4. Future challenges we foresee (Future) -----How we are planning to prepare for them Speakers Ian Pillay, Hadoop Administrator, Standard Bank Brad Smith, Hadoop Administrator, Standard Bank

Neo4j GraphDay Seattle- Sept19- graphs are ai

Neo4j

This document discusses how graph databases and machine intelligence are closely related. It provides three examples of how graphs can enhance artificial intelligence: 1. Graph-based algorithms like PageRank and pattern matching can power AI systems. Global graph algorithms can analyze large networks while transactional algorithms can match patterns in graphs. 2. Graphs can assist machine learning by providing features for models. Graph queries can extract smarter features from connected data for ML algorithms. Knowledge graphs also organize information for machine intelligence. 3. Machine intelligence systems can be made more intelligent through the use of graphs. Conversational commerce agents can be built using knowledge graphs to power natural language interactions. Graphs can also provide rich context, enhance AI models,

Surviving the Hadoop Revolution

DataWorks Summit/Hadoop Summit

The document discusses how open source software has disrupted traditional software development models. It describes how companies have had to adapt from closed, proprietary development to more open and community-based development. Specifically, companies have had to learn to release more control, work within open source communities, adopt agile development practices, and deal with the many unknowns of integrating diverse open source projects. The document suggests open source will continue pushing technological innovation and forcing the industry to evolve.

Future of Data Platform in Cloud Native world

Srivatsan Srinivasan

Presentation of my talk given in Phoenix Data Conference 2019. In this we will look at challenges with current Apache Hadoop ecosystem Apache Hadoop is still relevant but way of doing Hadoop and enterprise data architecture has to be re-looked as we enter Cognitive and Cloud Native Era We need  Architecture that is enabled by common run time layer across on premise and cloud  Architecture that can abstract away dependency and version conflicts with tons of open source machine learning out there. Yarn did not scale up in that aspect until one want to deal with multiple conda environment  Architecture that can enable real Hybrid Cloud and Multi Cloud portability  And many more challenges that one has to overcome to keep architecture simple, infrastructure agile and better utilized

Building Intelligent Solutions with Graphs, Stefan Kolmar, Neo4j

Neo4j

Production Grade Data Science for Hadoop

DataWorks Summit/Hadoop Summit

This document discusses scaling machine learning models from a laboratory setting to production. It proposes using a standardized representation called PMML to capture models produced by R and Scikit-Learn. PMML allows models to be deployed across different frameworks and languages. The document outlines APIs for evaluating, maintaining, and integrating models as reusable functions within data pipelines in Hadoop ecosystems like Spark, Pig, and Cascading. The goal is a portable, platform-agnostic architecture for operationalizing machine learning based on open standards.

What's New in Neo4j

Neo4j

The document provides an overview of the Neo4j Graph Platform. It highlights improvements in several areas including administration, drivers, analytics tooling, graph algorithms, and the Neo4j database. Key updates include rolling upgrades, multi-cluster routing, Neo4j Bloom for visualization, and performance enhancements in Cypher and indexing. The platform brings together capabilities for transactions, analytics, integration, discovery and administration on graphs.

Neo4j GraphTalks - Introduction to GraphDatabases and Neo4j

Neo4j

This document provides an overview of graph databases and Neo4j. It discusses how graph databases are well-suited for dynamic systems where relationships are important. Neo4j is introduced as a native graph database that is highly scalable and allows organizations to leverage connections in data. Examples are given of companies using Neo4j for applications like recommendations, fraud detection, and network management. Neo4j is presented as an enterprise-ready solution with features like ACID transactions, security, and support for popular languages.

IoT meets AI in the Clouds

Dr. Mirko Kämpf

In this presentation I do a review of the architecture of an AI application for IoT environments. Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon. The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly.

Closing Keynote

Neo4j

Dr. Jim Webber presented the closing keynote at a conference. He recapped Neo4j's recent releases, including version 3.0 which delivered new graph capabilities. He discussed future hardware trends and how native graph technology provides performance advantages. He also showed how Neo4j can handle workloads at massive scales of over 1 trillion relationships and 1 million writes per second on a single machine. Finally, he outlined Neo4j's new causal clustering architecture in version 3.1 which provides security, scalability, and fault tolerance for enterprise graph applications.

Neanex - Semantic Construction with Graphs

Neo4j

The document discusses semantic construction with graphs. It provides background on the speaker including their engineering and entrepreneurial experience. It then discusses trends in the AECO industry toward increased digitalization and use of building information modeling (BIM). The document proposes that an integrated approach using a semantic graph database can help address information gaps and complexity issues across project stages in the AECO industry.

Introduction to Neo4j

Neo4j

The Fort Meade Neo4j User Group meeting agenda included: - An introduction to Neo4j by Jason Zagalsky, discussing Neo4j's native graph database capabilities. - A presentation on using Neo4j for big data by Preston Hendrickson of Calibre Systems. - A demonstration of Neo4j Bloom for graph data visualization by Gary Mann. - An overview of what's new in Neo4j version 3.5 by David Fauth. - Time for Q&A, discussion, and networking.

Introduction to Neo4j

Neo4j

Neo4j GraphTalk Düsseldorf - Building intelligent solutions with Graphs

Neo4j

This document discusses using graphs and machine learning to build intelligent solutions. It describes Neo4j services including professional services, training, and managed services. It also outlines innovation labs that help generate graph-based use cases. The document reviews using graph analytics and machine learning together, and provides examples of how Neo4j can be leveraged throughout the machine learning life cycle from data integration to model deployment. Real-world customer examples are also presented.

Graphs for AI & ML, Jim Webber, Neo4j

Neo4j

This document provides an overview of graphs for artificial intelligence and machine learning. It discusses definitions of machine learning and AI, as well as common techniques like predictive analytics, transfer learning, and human-like AI. It then covers how graph databases and graph algorithms can be applied to domains like social networks, knowledge graphs, and recommender systems. Specific graph algorithms like triadic closure, structural balance, and graph partitioning are examined. The document also explores emerging areas like graph neural networks, graph convolutional networks, and using graph structures for causal models. It argues that representing data as graphs and applying graph algorithms can provide intelligent behavior without needing general human-level artificial intelligence.

GraphTalk Barcelona - Keynote

Neo4j

This document contains an agenda and slides for a Neo4j GraphTalk event in Barcelona on network and application management. The event will include an introduction to graph databases and Neo4j, a session on new approaches to network and application management with graphs, and a session on successful Neo4j projects. Additional slides discuss the state of the graph database market, three waves of graph adoption, how Neo4j enables graph-based solutions, and how graphs can enhance artificial intelligence and machine learning.

Neo4j GraphDay - Graphs in the Real World: Tope Use Cases for Graph Databases...

Neo4j

platform for Machine Learning

SivapriyaS12

Postgres Vision 2018: How to Consume your Database Platform On-premises

EDB

The usual model for a database platform on-premises is to run it the way IT is usually operated - silo'd and capital- and labor-intensive. In the cloud, consumption means that you pay for what you use, with less heavy lifting to operate the platform. Presented at Postgres Vision 2018, this covers how HPE can deliver EDB Postgres in the data center or on the edge in a consumption model that is pay-per-use, elastic IT, operated for you, migrated, and integrated.

Next generation Polyglot Architectures using Neo4j by Stefan Kolmar

Big Data Spain

Kcom graph connect europe, 11 may 2017

Andrew Smale

Changing Views on Integration (AUSOUG Webinar Series, May 2020)

Lucas Jellema

The document discusses changing views on integration from traditional approaches using enterprise service buses to more modern approaches using API gateways, serverless computing, and integration platforms as a service (iPaaS). It covers integration patterns like vertical integration for synchronous APIs and horizontal integration for asynchronous integration between systems. Event-driven integration using message queues is discussed as a third dimension of integration. The use of cloud-native approaches and integration platforms out of the box is presented as an alternative to traditional monolithic integration middleware.

What's hot

Securing and governing a multi-tenant data lake within the financial industry

DataWorks Summit

Neo4j GraphDay Seattle- Sept19- graphs are ai

Neo4j

Surviving the Hadoop Revolution

DataWorks Summit/Hadoop Summit

Future of Data Platform in Cloud Native world

Srivatsan Srinivasan

Building Intelligent Solutions with Graphs, Stefan Kolmar, Neo4j

Neo4j

Production Grade Data Science for Hadoop

DataWorks Summit/Hadoop Summit

What's New in Neo4j

Neo4j

Neo4j GraphTalks - Introduction to GraphDatabases and Neo4j

Neo4j

IoT meets AI in the Clouds

Dr. Mirko Kämpf

Closing Keynote

Neo4j

Neanex - Semantic Construction with Graphs

Neo4j

Introduction to Neo4j

Neo4j

Introduction to Neo4j

Neo4j

Neo4j GraphTalk Düsseldorf - Building intelligent solutions with Graphs

Neo4j

Graphs for AI & ML, Jim Webber, Neo4j

Neo4j

GraphTalk Barcelona - Keynote

Neo4j

Neo4j GraphDay - Graphs in the Real World: Tope Use Cases for Graph Databases...

Neo4j

platform for Machine Learning

SivapriyaS12

Postgres Vision 2018: How to Consume your Database Platform On-premises

EDB

Next generation Polyglot Architectures using Neo4j by Stefan Kolmar

Big Data Spain

What's hot (20)

Securing and governing a multi-tenant data lake within the financial industry

Neo4j GraphDay Seattle- Sept19- graphs are ai

Surviving the Hadoop Revolution

Future of Data Platform in Cloud Native world

Building Intelligent Solutions with Graphs, Stefan Kolmar, Neo4j

Production Grade Data Science for Hadoop

What's New in Neo4j

Neo4j GraphTalks - Introduction to GraphDatabases and Neo4j

IoT meets AI in the Clouds

Closing Keynote

Neanex - Semantic Construction with Graphs

Introduction to Neo4j

Neo4j GraphTalk Düsseldorf - Building intelligent solutions with Graphs

Graphs for AI & ML, Jim Webber, Neo4j

GraphTalk Barcelona - Keynote

Neo4j GraphDay - Graphs in the Real World: Tope Use Cases for Graph Databases...

platform for Machine Learning

Postgres Vision 2018: How to Consume your Database Platform On-premises

Next generation Polyglot Architectures using Neo4j by Stefan Kolmar

Similar to Rail Ticketing Assistance from the Graph Way, KCOM

Kcom graph connect europe, 11 may 2017

Andrew Smale

Changing Views on Integration (AUSOUG Webinar Series, May 2020)

Lucas Jellema

MindSphere: The cloud-based, open IoT operating system. Damiano Manocchia

Data Driven Innovation

GCX Cloud X Customer Presentation - Enterprise (Nov. 2014)

Ahmed Abdel-Latif

Enterprises need a Cloud platform that’s strong, smart, intuitive and responsive to constantly evolving needs. Cloud X - Changing the Paradigm of Cloud Computing: - Network-based enterprise data centers are scaled almost instantaneously; - Complex workloads are deployed to the corporate network with the click of a mouse; - Delivers on-demand computing with LAN-like performance, security and flexibility; - Offers global enterprise-class Cloud computing with unrivaled strength in the Emerging Markets Corridor.

iWAN - Cisco Application Experience Solution

xband

The document discusses Cisco's Application Experience solution which aims to optimize application delivery, improve user experience, and simplify IT operations. It highlights challenges faced by organizations regarding application sprawl, mobility, and network readiness. Cisco's approach is to provide a unified network with services for routing, security, visibility, control and optimization. Key benefits include improved application performance, network-wide visibility and control, consistent security, and lower total cost of ownership.

Cloud Governance within The Climate Corporation

Mohamed Ahmed

Anz cics ts v5 technical update seminar intro (half day event)

nick_garrod

The document provides information about IBM's CICS TS v5 technical update with dates for various locations in July. It discusses how systems like z Systems can act as private clouds, public clouds, or providers of services. It promotes the benefits of z13 such as improved performance, capacity, security, and analytics acceleration. It also discusses topics like mobile workload pricing, APIs, and enterprise modernization through delivering applications at the speed of mobile.

Excellent slides on the new z13s announced on 16th Feb 2016

Luigi Tommaseo

The document discusses new features and capabilities of the IBM z13s mainframe system. Key points include: - The z13s provides greater scale for Linux and z/OS workloads compared to previous models, with up to 2x increase in memory and I/O bandwidth. - It features capabilities like simultaneous multi-threading, vector processing, faster encryption, and improved compression to accelerate analytics and security workloads. - The system is designed to support hybrid cloud, blockchain, APIs, analytics, and security initiatives through integration with Linux on zSystems, IBM Cloud, and other platforms and services.

Confluent Partner Tech Talk with Reply

confluent

The power of orchestration - Inside Cisco IT - DC Cloud from IaaS to Fast IT

Cisco Canada

AWS Webcast - Datacenter Migration to AWS

Amazon Web Services

Join AWS and BlueMetal, a technology architecture firm and a member of the Amazon Partner Network, for this live webinar where we will discuss modernizing your applications when moving your data center to the AWS Cloud. Microsoft has announced that July 30, 2015, is the end of support for Windows Server 2003. This will affect customers since there will be no patches or security updates, putting applications and business at risk. Attend this webinar to learn about considerations and best practices for creating a composed solution when moving off of Windows Server 2003 and migrating your data center and applications to the cloud.

Building a reliable and scalable IoT platform with MongoDB and HiveMQ

Dominik Obermaier

Today’s Internet of Things (IoT) is enabling companies to blend together the physical and digital worlds, creating new business models and generating insights that increase productivity at once unimaginable levels. However, managing the ever growing volume of heterogeneous IoT data from disparate devices, systems and applications both on premise and in the cloud can be a challenging endeavour without a scalable and reliable IoT platform. In this webinar, we will explore why and how companies are leveraging HiveMQ and MongoDB to build exactly that: a scalable and reliable IoT platform. Based upon a sample fleet management scenario, we will explain how telematics data can be routed via MQTT and efficiently stored to provide analytics and insights into the data. Key Learnings - Common challenges and pitfalls of IoT projects - Required components for effectively handling data with an IoT platform - HiveMQ for MQTT to enable bi-directional device communication over unstable networks - MongoDB as the flexible and scalable modern data platform combining data from different sources and powering your applications - Why MongoDB and HiveMQ is such a great combination

Cloud Computing - Beyond the Hype

Presented at the New Zealand Computer Society 50th Anniversary Conference. The conference theme was about ICT Innovation. This presentation was delivered during the conference by Phil Patton, IBM NZ will focus on answering in simple terms the key questions many are asking in their quest to understand why there is so much hype around Cloud – what are the key ingredients of Cloud Computing? And what’s different about it, what are the deployment types, and what workloads are suitable for Cloud deployment? Phil will also cover the Enterprise Roadmap for Cloud adoption, the integration and connectivity between Cloud and legacy applications and address the significant security concerns related to the uptake of Cloud.

Mission Critical Applications Workloads on Amazon Web Services

Amazon Web Services

This document discusses best practices for deploying mission critical workloads on AWS. It defines mission critical workloads as those that must be secure, available, and resilient. It provides examples of customers like Capital One and News UK successfully running critical applications on AWS infrastructure. The document outlines the security, reliability, performance and scale benefits AWS provides including availability zones, encryption options, and services like CloudTrail and VPC. It argues AWS allows customers to securely run a wide variety of critical applications at a lower cost than traditional data centers.

Dinalog Breakfast Supply Chain Seminar 14-4-2011 Logistics Cross Chain Coordi...

Jos van Hillegersberg

Fernando sousa

EuroCloud

This document provides an overview of cloud computing concepts, delivery models, and IBM's cloud computing services and experiences. The key points are: 1. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. 2. IBM has extensive experience implementing cloud computing solutions for clients and internally through projects like consolidating its own data centers. 3. IBM offers a wide range of cloud consulting, implementation, infrastructure, platform, and software-as-a-service offerings to help organizations adopt and benefit from cloud computing.

Cloud computing - dien toan dam may

Nguyen Duong

This document discusses cloud computing concepts and applications in a military context. It defines cloud computing and describes common cloud themes like scalability, on-demand access, and location independence. It outlines business benefits like automation, data intensive computing, and accessibility from any device. The document also discusses DISA's focus on infrastructure/platform capabilities and lists several of DISA's cloud-related efforts.

Denis Jannot - Towards Data Science Engineering Principles - Codemotion Milan...

Codemotion

Over the last half century we have developed and refined the discipline of software engineering in order to accelerate the development and deployment of applications. This has involved a general shift towards DevOps practices that align developer and business objectives and dramatically reduce time-to-delivery. With the recent rise of data science and data analytics, the time has come to apply the principles of DevOps to data science and leverage the lessons from software engineering (and its systematic and repeatable methodology) to the discipline of data science.

Zeller Edm Summit Agile Deployment Of Predictive Analytics

Ronald.Ramos

The document discusses leveraging predictive analytics and cloud computing. It outlines using the R programming language for predictive model development, PMML for model integration and deployment, and Amazon EC2 for scalable predictive model execution in the cloud. The overall process involves developing predictive models with R, exporting them to PMML, deploying them on EC2 using a platform like ADAPA for real-time predictive analytics in a scalable, standards-based way.

Industry 4.0: come i servizi IoT e Big Data di AWS rendono Smart il Manufactu...

Amazon Web Services

Industry 4.0, also known as "smart manufacturing", utilizes technologies like the Industrial Internet of Things (IIoT) and big data analytics to increase manufacturing flexibility, speed, productivity, quality and competitiveness. AWS services like IoT, Machine Learning and analytics can help manufacturers digitally transform their operations by connecting devices, analyzing sensor data, and predicting issues in real-time. A leading automotive supplier partnered with AWS to develop an IoT prediction platform using ML to analyze production data from connected machines and reduce waste.

Similar to Rail Ticketing Assistance from the Graph Way, KCOM (20)

Kcom graph connect europe, 11 may 2017

Changing Views on Integration (AUSOUG Webinar Series, May 2020)

MindSphere: The cloud-based, open IoT operating system. Damiano Manocchia

GCX Cloud X Customer Presentation - Enterprise (Nov. 2014)

iWAN - Cisco Application Experience Solution

Cloud Governance within The Climate Corporation

Anz cics ts v5 technical update seminar intro (half day event)

Excellent slides on the new z13s announced on 16th Feb 2016

Confluent Partner Tech Talk with Reply

The power of orchestration - Inside Cisco IT - DC Cloud from IaaS to Fast IT

AWS Webcast - Datacenter Migration to AWS

Building a reliable and scalable IoT platform with MongoDB and HiveMQ

Cloud Computing - Beyond the Hype

Mission Critical Applications Workloads on Amazon Web Services

Dinalog Breakfast Supply Chain Seminar 14-4-2011 Logistics Cross Chain Coordi...

Fernando sousa

Cloud computing - dien toan dam may

Denis Jannot - Towards Data Science Engineering Principles - Codemotion Milan...

Zeller Edm Summit Agile Deployment Of Predictive Analytics

Industry 4.0: come i servizi IoT e Big Data di AWS rendono Smart il Manufactu...

More from Neo4j

Harnessing the Power of NLP and Knowledge Graphs for Opioid Research

Neo4j

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

Neo4j

Leveraging the Graph for Clinical Trials and Standards

Neo4j

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Neo4j

Atelier - Architecture d’applications de Graphes - GraphSummit Paris

Neo4j

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 connaissances

Neo4j

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 Paris

Neo4j

Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris

Neo4j

FLOA - Détection de Fraude - GraphSummit Paris

Neo4j

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 Technology

Neo4j

GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...

Neo4j

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.

GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024

Neo4j

GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024

Neo4j

GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...

Neo4j

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 analysis

Neo4j

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 Updates

Neo4j

KLARNA - Language Models and Knowledge Graphs: A Systems Approach

Neo4j

More from Neo4j (20)