Knowledge Graphs for Network Digital Twins

Jan 13, 20230 likes239 views

The document discusses how graph databases are well-suited for telecommunications networks due to their ability to model complex relationships. It provides examples of large telecom companies using Neo4j to power applications involving network topology analysis, digital twins, and other use cases. Key benefits highlighted include stronger support for dynamic connectivity modeling, improved path calculation, and more flexible approaches compared to relational databases. The presentation outlines Neo4j's adoption among top telecom firms and leadership in the graph database market.

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
1
Graph & Telecom:
A Natural Match of Network and
Connections

© 2022 Neo4j, Inc. All rights reserved.
Agenda
• Welcome and housekeeping
• Topology Analysis for large Telecom Operators (Sopra Steria)
• Knowledge Graphs for Network Digital Twins (Neo4j)
• Q&A

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
3
Knowledge Graphs for Network
Digital Twins
Dr. J. Barrasa
Neo4j - Senior Director SA

© 2022 Neo4j, Inc. All rights reserved.
4
The company. The graph space.

© 2022 Neo4j, Inc. All rights reserved.
Neo4j: Helping the world make sense of their data
160M+ Downloads
230K+ Devs & Data Scientists
$390M Series F (June 2021)
Largest investment in Database history
The Most Trusted
Graph Data Platform
7 of the World’s Top 10 Retailers
3 of the Top 5 Aircraft Manufacturers
All of North America’s Top 20 Banks
7 of the Top 10 Telcos
Neo4j, Inc. All rights reserved 2021
Graph market leader; 1000s of
deployments around the globe

© 2022 Neo4j, Inc. All rights reserved.
Neo4j, Inc. All rights reserved 2021
Graph is the fastest growing DBMS, Neo4j leading the category
6
FASTEST GROWING CATEGORY
STRONGEST COMMUNITY
LinkedIn Skills
41k+
members with
230k+
Meetups
72k+
Members globally
GRAPH PLATFORMS CLOUD DBS

© 2022 Neo4j, Inc. All rights reserved.
7
Why Graphs in Telco?

© 2022 Neo4j, Inc. All rights reserved.
8
Common challenges
1 2
Need to capture
complexity: rich
model
Allow flexibility:
dynamic, real-time
view
3 4
Hi-perf: used in
operational
scenarios.
Requirement for
parallelism and
low latency.
Bridge SME - Data
gap: Maintainable
and humane models

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
9
Graph databases are much better at modeling complex environments
like networks, where many dynamic relationships exist between data
points. Modeling the network as a graph makes network planning
decisions much easier. Graph databases also help with path
calculation by providing a connectivity model
across heterogeneous vendor and data environments.
“
BP Inventory Data Sheet (*)
(*) https://www.blueplanet.com/resources/inventory-ds.html

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
10
Graph data structures provide enormous
advantages over relational databases overall.
Ericsson has chosen to take this approach because
of the tremendous advantage it has to service
providers
“
Graeme Jones. Strategic Product Manager (*)
(*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
11
A database that natively supports graphs is optimal for capturing
and implementing different operational functions.
Topology information, needed to complement event information in
correlation, is persisted as a graph and retrieved as needed in
support of correlation
The sequence of steps, commands, and associated responses
are captured as a graph
The rules engine handles the reception of events from external
OSSs and BSSs and indicates the next branch to be traversed in
the decision tree based on inputs and inferences in real time
“
HPE UCA Data Sheet (*)
(*) https://h20195.www2.hpe.com/v2/Getdocument.aspx?docname=4AA5-1098ENW
“
“
“

© 2022 Neo4j, Inc. All rights reserved.
12
Adoption

© 2022 Neo4j, Inc. All rights reserved.
13
Largest
Telcos
2 of the 3 world’s
largest CSP use
Neo4j in mission
critical solutions
3/5 of the leaders in the
latest Gartner MQ for
OSS embed Neo4j in
their products
2 of the Top 5 US
multichannel video
service providers have
chosen Neo4j
60% OSS
Leaders
Top
MVPD

© 2022 Neo4j, Inc. All rights reserved.
14
Use Cases

© 2022 Neo4j, Inc. All rights reserved.
15
Common Areas / Graph Use Cases
OSS, IoT,
Operations
BSS,
Services
Customer,
CXM,
Personalisation
Regulatory,
compliance

© 2022 Neo4j, Inc. All rights reserved.
16
A Network Digital Twin

© 2022 Neo4j, Inc. All rights reserved.
17
NDT: At the core of all business decisions
https://www.slideshare.net/neo4j/blueplanet-inventory-federation-alvaro-osle-blueplanet

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
• Unified Network Inventory
• Network Reconciliation
• Network and Service
Orchestration: Topology
• Service Assurance: Fault
Management
• Planning, Traffic Intelligence
• Performance and Quality
Management and Analytics
18
Network-Facing Use Cases:
Success Stories

© 2022 Neo4j, Inc. All rights reserved.
19
What next?
https://neo4j.com/cloud

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
20
Thank you!

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.

GSK: How Knowledge Graphs Improve Clinical Reporting WorkflowsNeo4j

This document discusses GSK's efforts to use knowledge graphs to improve clinical reporting workflows. It describes GSK's current multi-step clinical data flow process and the resources required. The document envisions a future where a clinical knowledge graph could provide a single connected data model, parallel processing, and accelerated decision making. GSK plans to test building a minimum viable product knowledge graph to ingest and analyze clinical trial data and derive metrics. The goal is to demonstrate feasibility and inform further development through a phased agile approach.

Modeling Cybersecurity with Neo4j, Based on Real-Life Data InsightsNeo4j

Graph databases can help address challenges in cybersecurity by leveraging connections within datasets. Gal Bello's presentation provided an overview of using graph modeling for cybersecurity. It discussed how graph databases can assist companies in securing data by using relationships. The presentation also provided examples of modeling fraud rings and law enforcement data as graphs to improve efficiency and reveal patterns. Real-world use cases demonstrated how organizations are applying graph databases to challenges in cybersecurity.

Transforming BT’s Infrastructure Management with Graph TechnologyNeo4j

Introducing Neo4jNeo4j

This document summarizes a presentation about the graph database Neo4j. The presentation included an agenda that covered graphs and their power, how graphs change data views, and real-time recommendations with graphs. It introduced the presenters and discussed how data relationships unlock value. It described how Neo4j allows modeling data as a graph to unlock this value through relationship-based queries, evolution of applications, and high performance at scale. Examples showed how Neo4j outperforms relational and NoSQL databases when relationships are important. The presentation concluded with examples of how Neo4j customers have benefited.

LLMs BootcampFiza987241

This document provides information about a bootcamp to build applications using Large Language Models (LLMs). The bootcamp consists of 11 modules covering topics such as introduction to generative AI, text analytics techniques, neural network models for natural language processing, transformer models, embedding retrieval, semantic search, prompt engineering, fine-tuning LLMs, orchestration frameworks, the LangChain application platform, and a final project to build a custom LLM application. The bootcamp will be held in various locations and dates between September 2023 and January 2024.

Wi-Fi 6.pptxNicJones15

Optimizing Your Supply Chain with the Neo4j GraphNeo4j

Graph-Based Network Topology Analysis for Telecom OperatorsNeo4j

The document discusses the development of an Intelligent Network Analyzer (INA) tool to help telecom operators better manage their networks. INA extracts data from over 60 sources, compiles it into a graph database, and provides customizable network visualization and analytics. This allows operators to have a digital twin of their network topology and more easily identify potential problems, simulate network changes, and optimize traffic routing across the network. The tool was created through a collaboration between network experts and developers to best fuse telecom knowledge with graph database functionality.

Workshop - Build a Graph SolutionNeo4j

The document outlines an agenda for a workshop on building a graph solution using a digital twin data set. It includes sections on logistics, introductions, explaining the use case of a digital twin for a rail network, modeling the graph database solution, building the solution, and a question and answer period. Key aspects covered include an overview of Neo4j's graph database capabilities, modeling the domain entities and relationships, and exploring sample data related to operational points, sections, and points of interest for a rail network digital twin use case.

Demystifying Graph Neural NetworksNeo4j

Kerry Group: How Neo4j graph technology is delivering benefits to Kerry Group...Neo4j

Kerry is a global taste and nutrition company that sells products to food, beverage, and pharmaceutical companies. To improve customer insights, Kerry is building a Customer 360 system to combine customer data currently stored across different IT systems into a graph database. This will provide a holistic view of each customer and help address issues more quickly by giving customer service staff access to real-time order information and alerts in one centralized place.

CIMPA : Enhancing Data Exposition & Digital Twin for Airbus HelicoptersNeo4j

This presentation discusses Airbus Helicopters' use of Neo4j and a digital twin platform called HELIOS to provide a consolidated view of product data from across the organization. HELIOS aggregates data from different source systems and exposes it through a secure API to provide a single source of truth for stakeholders. Product structure data, which defines the hierarchical breakdown of a product, is modeled as a graph in Neo4j to allow for relationships and properties to be added. This helps address challenges around data segregation, standardization, and access performance in traditional systems by linking heterogeneous data sources into a unified graph.

SITA WorldTracer - Lost & Found PropertyNeo4j

Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j

Intro to Neo4jNeo4j

Neo4j is a native graph database that allows organizations to leverage connections in data to create value in real-time. Unlike traditional databases, Neo4j connects data as it stores it, enabling lightning-fast retrieval of relationships. With over 200 customers including Walmart, UBS, and adidas, Neo4j is the number one database for connected data by providing a highly scalable and flexible platform to power use cases like recommendations, fraud detection, and supply chain management through relationship queries and analytics.

Neo4j: The path to success with Graph Database and Graph Data ScienceNeo4j

This document provides an overview of the Neo4j graph data platform and its capabilities for data science and analytics. It discusses Neo4j's native graph architecture, tools for data scientists and analysts, and how Neo4j enables graph data science across the machine learning lifecycle from feature engineering to model deployment. Algorithms, embeddings, and machine learning pipelines in Neo4j are highlighted. Integration with common data ecosystems is also covered.

Modern Data Challenges require Modern Graph TechnologyNeo4j

Graph database Use CasesMax De Marzi

This document provides an overview of graph databases and their use cases. It begins with definitions of graphs and graph databases. It then gives examples of how graph databases can be used for social networking, network management, and other domains where data is interconnected. It provides Cypher examples for creating and querying graph patterns in a social networking and IT network management scenario. Finally, it discusses the graph database ecosystem and how graphs can be deployed for both online transaction processing and batch processing use cases.

Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j

The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.

Northern Gas Networks and CKDelta at Neo4j GraphSummit London 14Nov23.pptxNeo4j

This document discusses using machine learning and graph databases to help manage water ingress in gas networks. It presents a recommendation system that uses a graph model of the gas network to identify locations at risk of water ingress. Features like centrality scores, community detection, and pipe embeddings are extracted from the graph and used in a machine learning model. The model achieves higher success than traditional methods at predicting whether syphons need to be pumped and the amount of water extracted. Other potential applications are also identified, such as estimating syphon pumping frequency and early detection of water ingress issues.

Supply Chain Twin Demo - Companion DeckNeo4j

The document is a presentation deck on building a supply chain twin using Neo4j and Google technologies. It discusses how supply chain data can be modeled as a graph and stored in Neo4j to power use cases like identifying product and part shortfalls, evaluating supply chain risk, and enabling scenario planning. The deck outlines an architecture that ingests supply chain data from Google BigQuery into Neo4j, then leverages Neo4j technologies like Graph Data Science, Bloom, and Keymaker to operationalize queries and deliver insights to applications.

Graph Data Modeling Best Practices(Eric_Monk).pptxNeo4j

The document discusses best practices for graph data modeling in Neo4j. It describes different types of modeling including whiteboarding, instance modeling, logical modeling, physical modeling, and tuned modeling. Each type of modeling has a different focus such as conceptual understanding, answering questions, enabling data loading, and optimizing performance. The document provides tips for each modeling type and examples to illustrate graph structures. It also covers topics like relationship types, constraints, indexing, and validating the model.

EY + Neo4j: Why graph technology makes sense for fraud detection and customer...Neo4j

This document discusses how graph technology can help with fraud detection and customer 360 projects in the insurance industry. It notes that insurers today struggle with identity resolution, siloed data, and reactive policies. This leads to an inability to get a full customer view or recommend next best actions. Graph databases provide a unified customer view by linking different data sources and modeling relationships. This enables capabilities like predictive analytics, personalization, and improved fraud identification. The document outlines how to build a customer golden profile with a graph database and provides examples of insights that can be gained. It also discusses proving the value of the graph approach and making graphs a long-term, sustainable solution.

The Art of the Possible with Graph - Sudhir Hasbe - GraphSummit London 14 Nov...Neo4j

The document discusses how massive trends like connected data, cloud innovation, and the rise of generative AI are transforming industries. It argues that to thrive in this new environment, organizations must turn data into insights and knowledge. Graph databases are presented as better for this task by preserving relationships that get lost with relational databases. The document promotes Neo4j's graph database platform and its capabilities for enabling insights, powering cloud applications, and combining with generative AI through knowledge graphs.

The Knowledge Graph ExplosionNeo4j

The document discusses knowledge graphs and their value for organizations. It notes that two-thirds of Neo4j customers have implemented knowledge graphs and that 88% of CXOs believe knowledge graphs will significantly improve business outcomes. Knowledge graphs are described as interconnected datasets enriched with meaning to enable complex decision-making. Examples of how knowledge graphs have helped companies with recommendations, fraud detection, and track and trace are provided.

Neo4j in DepthMax De Marzi

This document provides an overview of Neo4j, a graph database management system. It discusses how Neo4j stores data as nodes and relationships, allowing for fast querying of connected data. Traditional relational databases struggle with complex relationships, while NoSQL databases don't support relationships at all. Neo4j addresses these issues through its native graph storage and processing capabilities. The document highlights key Neo4j features like scalability, high performance, and its Cypher query language.

Neo4j GraphSummit London - The Path To Success With Graph Database and Data S...Neo4j

The document discusses Neo4j's graph data platform and graph data science capabilities. It provides an overview of Neo4j's tools for data scientists, machine learning workflows, algorithms, and ecosystem integrations. Examples are given of improved customer outcomes including increased fraud detection and better predictive models. The document also outlines new capabilities in algorithms, embeddings, machine learning pipelines, and GNN support.

Optimizing Your Supply Chain with Neo4jNeo4j

This document discusses optimizing supply chains with Neo4j graph databases. It notes that supply chains have complex relationships that are naturally modeled as graphs. It promotes Neo4j's graph database, tools, and algorithms for building digital twins of supply chains for purposes like visibility, optimization, and calculating scope 3 carbon emissions. Examples are given of companies using Neo4j for supply chain applications like routing, asset management, and equipment maintenance.

The Data Platform for Today's Intelligent Applications.pdfNeo4j

More Related Content

What's hot (20)

Graph-Based Network Topology Analysis for Telecom OperatorsNeo4j

Workshop - Build a Graph SolutionNeo4j

Demystifying Graph Neural NetworksNeo4j

Kerry Group: How Neo4j graph technology is delivering benefits to Kerry Group...Neo4j

CIMPA : Enhancing Data Exposition & Digital Twin for Airbus HelicoptersNeo4j

SITA WorldTracer - Lost & Found PropertyNeo4j

Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j

Intro to Neo4jNeo4j

Neo4j: The path to success with Graph Database and Graph Data ScienceNeo4j

Modern Data Challenges require Modern Graph TechnologyNeo4j

Graph database Use CasesMax De Marzi

Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j

Northern Gas Networks and CKDelta at Neo4j GraphSummit London 14Nov23.pptxNeo4j

Supply Chain Twin Demo - Companion DeckNeo4j

Graph Data Modeling Best Practices(Eric_Monk).pptxNeo4j

EY + Neo4j: Why graph technology makes sense for fraud detection and customer...Neo4j

The Art of the Possible with Graph - Sudhir Hasbe - GraphSummit London 14 Nov...Neo4j

The Knowledge Graph ExplosionNeo4j

Neo4j in DepthMax De Marzi

Neo4j GraphSummit London - The Path To Success With Graph Database and Data S...Neo4j

Graph-Based Network Topology Analysis for Telecom OperatorsNeo4j

Workshop - Build a Graph SolutionNeo4j

Demystifying Graph Neural NetworksNeo4j

Kerry Group: How Neo4j graph technology is delivering benefits to Kerry Group...Neo4j

CIMPA : Enhancing Data Exposition & Digital Twin for Airbus HelicoptersNeo4j

SITA WorldTracer - Lost & Found PropertyNeo4j

Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j

Intro to Neo4jNeo4j

Neo4j: The path to success with Graph Database and Graph Data ScienceNeo4j

Modern Data Challenges require Modern Graph TechnologyNeo4j

Graph database Use CasesMax De Marzi

Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j

Northern Gas Networks and CKDelta at Neo4j GraphSummit London 14Nov23.pptxNeo4j

Supply Chain Twin Demo - Companion DeckNeo4j

Graph Data Modeling Best Practices(Eric_Monk).pptxNeo4j

EY + Neo4j: Why graph technology makes sense for fraud detection and customer...Neo4j

The Art of the Possible with Graph - Sudhir Hasbe - GraphSummit London 14 Nov...Neo4j

The Knowledge Graph ExplosionNeo4j

Neo4j in DepthMax De Marzi

Neo4j GraphSummit London - The Path To Success With Graph Database and Data S...Neo4j

Similar to Knowledge Graphs for Network Digital Twins (20)

Optimizing Your Supply Chain with Neo4jNeo4j

The Data Platform for Today's Intelligent Applications.pdfNeo4j

Neo4j Keynote: The Art of the Possible with Graph TechnologyNeo4j

Keynote: Art of the Possible - MooreNeo4j

Are you drowning in data but lacking in insight? 80% of business leaders say data is critical in decision-making, yet 41% cite a lack of understanding of data because it is too complex or not accessible enough. You’ll learn how companies are using graph technology to leverage the relationships in their connected data to reveal new ways of solving their most pressing business problems and creating new business value for their enterprises. You’ll see real-world use cases that include fraud detection, AI/ML, supply chain management, real-time recommendations, Customer 360, network/IT operations and more.

Government GraphSummit: Optimizing the Supply ChainNeo4j

Telecoms Service Assurance & Service Fulfillment with Neo4j Graph DatabaseNeo4j

This document discusses how graph databases can help with telecom network management. It presents a sample telecom network data model with nodes for customers, services, equipment, and connections linked by relationships. Graph databases are well-suited because they can efficiently query the complex relationships in telecom network topology data. The document describes how graph queries can help with tasks like capacity planning, path finding, service impact analysis, and alarm correlation that are important for telecom network assurance and service fulfillment.

The Neo4j Data Platform for Today & Tomorrow.pdfNeo4j

The document discusses the Neo4j graph data platform. It highlights that connected data is growing exponentially and graphs are well-suited to model real-world relationships. Neo4j provides a native graph database, tools, and services to store, query, and analyze graph data. Key capabilities include high performance, flexible schemas, developer productivity, and supporting transactions and analytics workloads.

Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j

El Arte de lo PossibleNeo4j

The document discusses how the world is changing due to trends like connected data, cloud innovation, and the rise of generative AI. It states that for organizations to thrive, they must turn data into insights and knowledge. Relationship data is often lost with relational databases. The document promotes Neo4j as a graph database that can uncover relationships in data to generate insights and knowledge. It provides examples of companies like Caterpillar and the US Army that have benefited from using Neo4j to create knowledge graphs and gain operational benefits. The document also discusses how Neo4j supports cloud deployment and can integrate with generative AI systems to create more intelligent applications.

Cloud Computing and Edge Computing(CTO Kieun Park) - Edge Computing SeminarNAVER CLOUD PLATFORMㅣ네이버 클라우드 플랫폼

Cloud computing provides centralized computing resources via the internet while edge computing distributes some computing capabilities to local endpoints. As technologies like IoT and 5G emerge, edge computing is growing in importance to support applications requiring low latency. Edge computing complements cloud computing by handling data and tasks locally when immediate response times are needed, while still utilizing cloud infrastructure for storage and analytics. Both cloud and edge computing are key to enabling technologies like smart cities that generate large amounts of data from distributed devices.

Neo4j : L’art des Possibles avec la Technologie des GraphesNeo4j

Getting to the Edge – Exploring 4G/5G Cloud-RAN Deployable SolutionsRadisys Corporation

Road to NODES 2023: Graphing Relational DatabasesNeo4j

The document discusses strategies for migrating data from a relational database to a graph database. It introduces some common patterns found in relational databases and how they can be modeled as graph structures. These include simple joins, self joins, pivot tables, and polymorphic relationships. The document also provides an example migration project and demonstrates how queries can be simplified by modeling the data as a graph. Key aspects of a migration are identified such as mapping the relational schema to graph nodes and relationships.

Lifecycle MEP design for buildingsTero Järvinen

Actionable Carbon Tracking and Analysis with the Neo4j Graph Data PlatformNeo4j

Graph databases are well-suited for tracking carbon emissions across complex supply chains. Building a graph digital twin allows organizations to: 1. Model the entities and relationships in their value chain to calculate scope 3 emissions. 2. Ingest multiple data sources and map emission factors to estimate upstream and downstream carbon impacts. 3. Continuously improve data quality and refine carbon accounting as more information is added to the unified graph model.

Nordics Edition - The Neo4j Graph Data Platform Today & TomorrowNeo4j

Data Con LA 2022 - Who Owns That Yacht? How Graphs Are Used to Identify Asset...Data Con LA

Mark Quinsland, Sr. Field Engineer at Neo4j Luxury yachts, football teams, and mansions are no longer safe havens for the illicit profits of Russian Oligarchs with ties to Putin. Assets are being identified and seized with benefits flowing to causes in Ukraine. This presentation covers: - How are friends and relatives of Putin sheltering immense profits - Graphs and other tools being used to identify sources & destinations of illicit wealth - Latest asset seizures - New regulations to expose hidden investors

E-Magazine September Issue 2021VARINDIA

This document discusses the rollout of 5G networks in India. It notes that while edge computing discussions have risen globally in recent years, Asia Pacific and India are leading regions for edge computing adaptation. The document also discusses how the Ladakh institute has become the first in India's union territories to implement a LiFi network for internet access. It covers how 5G will enable new applications and industries in India through high data rates and low latency connectivity. However, it notes that the COVID-19 pandemic delayed the launch of 5G in India and that a 2021 parliamentary report found insufficient work had been done yet to launch 5G. It believes 5G rollout across India will take 2-3 more years while 4G networks remain important.

Edge Computing M&A AnalysisNetscribes

Edge computing is redefining the cloud computing space. The growing de-emphasis on the cloud’s role in connected environments is expected to lead to smarter and faster autonomous solutions that have the potential to reshape the IoT landscape. Edge computing will transform the IoT landscape into a hyperconnected environment where the restrictions related to latency and computation capacity will be eliminated. Many companies are transforming their business models to attain edge computing capabilities necessary for offering end to end services. The recent years have witnessed a number of mergers and acquisitions in the edge computing space for IoT services, with the increase in M&A activities representing the industry’s conundrum of cloud, edge, and hybrid architectures, and the race to achieve a considerable market share. This report includes an analysis of approximately 60 deals, along with a detailed technology overview and the purpose of the acquisitions. The M&A analysis section offers a comprehensive view of the transactions around edge computing, covering different technology aspects including data center, AI, security, software-defined WAN (SD-WAN), analytics, interoperability, multi-access edge computing (MEC), and others. To purchase the full report, write to us at [email protected]

Keynote: Art of the Possible - Chandra RanganNeo4j

Optimizing Your Supply Chain with Neo4jNeo4j

The Data Platform for Today's Intelligent Applications.pdfNeo4j

Neo4j Keynote: The Art of the Possible with Graph TechnologyNeo4j

Keynote: Art of the Possible - MooreNeo4j

Government GraphSummit: Optimizing the Supply ChainNeo4j

Telecoms Service Assurance & Service Fulfillment with Neo4j Graph DatabaseNeo4j

The Neo4j Data Platform for Today & Tomorrow.pdfNeo4j

Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j

El Arte de lo PossibleNeo4j

Cloud Computing and Edge Computing(CTO Kieun Park) - Edge Computing SeminarNAVER CLOUD PLATFORMㅣ네이버 클라우드 플랫폼

Neo4j : L’art des Possibles avec la Technologie des GraphesNeo4j

Getting to the Edge – Exploring 4G/5G Cloud-RAN Deployable SolutionsRadisys Corporation

Road to NODES 2023: Graphing Relational DatabasesNeo4j

Lifecycle MEP design for buildingsTero Järvinen

Actionable Carbon Tracking and Analysis with the Neo4j Graph Data PlatformNeo4j

Nordics Edition - The Neo4j Graph Data Platform Today & TomorrowNeo4j

Data Con LA 2022 - Who Owns That Yacht? How Graphs Are Used to Identify Asset...Data Con LA

E-Magazine September Issue 2021VARINDIA

Edge Computing M&A AnalysisNetscribes

Keynote: Art of the Possible - Chandra RanganNeo4j

More from Neo4j (20)

Graphs & GraphRAG - Essential Ingredients for GenAINeo4j

Knowledge graphs are emerging as useful and often necessary for bringing Enterprise GenAI projects from PoC into production. They make GenAI more dependable, transparent and secure across a wide variety of use cases. They are also helpful in GenAI application development: providing a human-navigable view of relevant knowledge that can be queried and visualised. This talk will share up-to-date learnings from the evolving field of knowledge graphs; why more & more organisations are using knowledge graphs to achieve GenAI successes; and practical definitions, tools, and tips for getting started.

Neo4j Knowledge for Customer Experience.pptxNeo4j

Discover how Neo4j-based GraphRAG and Generative AI empower organisations to deliver hyper-personalised customer experiences. Explore how graph-based knowledge empowers deep context understanding, AI-driven insights, and tailored recommendations to transform customer journeys. Learn actionable strategies for leveraging Neo4j and Generative AI to revolutionise customer engagement and build lasting relationships.

GraphTalk New Zealand - The Art of The Possible.pptxNeo4j

Neo4j: The Art of the Possible with GraphNeo4j

Smarter Knowledge Graphs For Public SectorNeo4j

GraphRAG and Knowledge Graphs Exploring AI's FutureNeo4j

Matinée GenAI & GraphRAG Paris - Décembre 24Neo4j

ANZ Presentation: GraphSummit Melbourne 2024Neo4j

In this presentation, ANZ will be sharing their journey towards AI-enabled data management at scale. The session will explore how they are modernising their data architecture to support advanced analytics and decision-making. By leveraging a knowledge graph approach, they are enhancing data integration, governance, and discovery, breaking down silos to create a unified view across diverse data sources. This enables AI applications to access and contextualise information efficiently, and drive smarter, data-driven outcomes for the bank. They will also share lessons they are learning and key steps for successfully implementing a scalable, AI-ready data framework.

Google Cloud Presentation GraphSummit Melbourne 2024: Building Generative AI ...Neo4j

Telstra Presentation GraphSummit Melbourne: Optimising Business Outcomes with...Neo4j

This session will highlight how knowledge graphs can significantly enhance business outcomes by supporting the Data Mesh approach. We’ll discuss how knowledge graphs empower organisations to create and manage data products more effectively, enabling a more agile and adaptive data strategy. By leveraging knowledge graphs, businesses can better organise and connect their data assets, driving innovation and maximising the value derived from their data, ultimately leading to more informed decision-making and improved business performance.

Hands-On GraphRAG Workshop: GraphSummit Melbourne 2024Neo4j

Building Smarter GenAI Apps with Knowledge Graphs While GenAI offers great potential, it faces challenges with hallucination and limited domain knowledge. Graph-powered retrieval augmented generation (GraphRAG) helps overcome these challenges by integrating vector search with knowledge graphs and data science techniques. This approach improves context, enhances semantic understanding, enables personalisation, and facilitates real-time updates. In this workshop, you’ll explore detailed code examples to kickstart your journey with GenAI and graphs. You’ll leave with practical skills you can immediately apply to your own projects.

Démonstration Digital Twin Building Wire ManagementNeo4j

Swiss Life - Les graphes au service de la détection de fraude dans le domaine...Neo4j

Démonstration Supply Chain - GraphTalk ParisNeo4j

The Art of Possible - GraphTalk Paris Opening SessionNeo4j

How Siemens bolstered supply chain resilience with graph-powered AI insights ...Neo4j

In this captivating session, Siemens will reveal how Neo4j’s powerful graph database technology uncovers hidden data relationships, helping businesses reach new heights in IT excellence. Just as organizations often face unseen barriers, your business may be missing critical insights buried in your data. Discover how Siemens leverages Neo4j to enhance supply chain resilience, boost sustainability, and unlock the potential of AI-driven insights. This session will demonstrate how to navigate complexity, optimize decision-making, and stay ahead in a constantly evolving market.

Knowledge Graphs for AI-Ready Data and Enterprise Deployment - Gartner IT Sym...Neo4j

Neo4j Graph Data Modelling Session - GraphTalkNeo4j

Neo4j: The Art of Possible with Graph TechnologyNeo4j

Astra Zeneca: How KG and GenAI Revolutionise Biopharma and Life SciencesNeo4j

Graphs & GraphRAG - Essential Ingredients for GenAINeo4j

Neo4j Knowledge for Customer Experience.pptxNeo4j

GraphTalk New Zealand - The Art of The Possible.pptxNeo4j

Neo4j: The Art of the Possible with GraphNeo4j

Smarter Knowledge Graphs For Public SectorNeo4j

GraphRAG and Knowledge Graphs Exploring AI's FutureNeo4j

Matinée GenAI & GraphRAG Paris - Décembre 24Neo4j

ANZ Presentation: GraphSummit Melbourne 2024Neo4j

Google Cloud Presentation GraphSummit Melbourne 2024: Building Generative AI ...Neo4j

Telstra Presentation GraphSummit Melbourne: Optimising Business Outcomes with...Neo4j

Hands-On GraphRAG Workshop: GraphSummit Melbourne 2024Neo4j

Démonstration Digital Twin Building Wire ManagementNeo4j

Swiss Life - Les graphes au service de la détection de fraude dans le domaine...Neo4j

Démonstration Supply Chain - GraphTalk ParisNeo4j

The Art of Possible - GraphTalk Paris Opening SessionNeo4j

How Siemens bolstered supply chain resilience with graph-powered AI insights ...Neo4j

Knowledge Graphs for AI-Ready Data and Enterprise Deployment - Gartner IT Sym...Neo4j

Neo4j Graph Data Modelling Session - GraphTalkNeo4j

Neo4j: The Art of Possible with Graph TechnologyNeo4j

Astra Zeneca: How KG and GenAI Revolutionise Biopharma and Life SciencesNeo4j

Recently uploaded (20)

Classification_in_Machinee_Learning.pptxwencyjorda88

EDU533 DEMO.pptxccccvbnjjkoo jhgggggbbbbJessaMaeEvangelista2

Perencanaan Pengendalian-Proyek-Konstruksi-MS-PROJECT.pptxPareaRusan

Deloitte Analytics - Applying Process Mining in an audit contextProcess mining Evangelist

Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation. Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.

IAS-slides2-ia-aaaaaaaaaaain-business.pdfmcgardenlevi9

Just-In-Timeasdfffffffghhhhhhhhhhj Systems.pptssuser5f8f49

Template_A3nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnncegiver630

Telangana State, India’s newest state that was carved from the erstwhile state of Andhra Pradesh in 2014 has launched the Water Grid Scheme named as ‘Mission Bhagiratha (MB)’ to seek a permanent and sustainable solution to the drinking water problem in the state. MB is designed to provide potable drinking water to every household in their premises through piped water supply (PWS) by 2018. The vision of the project is to ensure safe and sustainable piped drinking water supply from surface water sources

03 Daniel 2-notes.ppt seminario escatologiaAlexander Romero Arosquipa

VKS-Python Basics for Beginners and advance.pptxVinod Srivastava

md-presentHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHation.pptxfatimalazaar2004

Conic Sectionfaggavahabaayhahahahahs.pptxtaiwanesechetan

Ppt. Nikhil.pptxnshwuudgcudisisshvehsjkspanchariyasahil

VKS-Python-FIe Handling text CSV Binary.pptxVinod Srivastava

Defense Against LLM Scheming 2025_04_28.pptxGreg Makowski

https://www.meetup.com/sf-bay-acm/events/306888467/ A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold. While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence? The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces. However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces. Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)

04302025_CCC TUG_DataVista: The Design Storyccctableauusergroup

Geometry maths presentation for begginerszrjacob283

Molecular methods diagnostic and monitoring of infection - Repaired.pptx7tzn7x5kky

Thingyan is now a global treasure! See how people around the world are search...Pixellion

Ch3MCT24.pptx measure of central tendencyayeleasefa2

Adobe Analytics NOAM Central User Group April 2025 Agent AI: Uncovering the S...gmuir1066

Classification_in_Machinee_Learning.pptxwencyjorda88

EDU533 DEMO.pptxccccvbnjjkoo jhgggggbbbbJessaMaeEvangelista2

Perencanaan Pengendalian-Proyek-Konstruksi-MS-PROJECT.pptxPareaRusan

Deloitte Analytics - Applying Process Mining in an audit contextProcess mining Evangelist

IAS-slides2-ia-aaaaaaaaaaain-business.pdfmcgardenlevi9

Just-In-Timeasdfffffffghhhhhhhhhhj Systems.pptssuser5f8f49

Template_A3nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnncegiver630

03 Daniel 2-notes.ppt seminario escatologiaAlexander Romero Arosquipa

VKS-Python Basics for Beginners and advance.pptxVinod Srivastava

md-presentHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHation.pptxfatimalazaar2004

Conic Sectionfaggavahabaayhahahahahs.pptxtaiwanesechetan

Ppt. Nikhil.pptxnshwuudgcudisisshvehsjkspanchariyasahil

VKS-Python-FIe Handling text CSV Binary.pptxVinod Srivastava

Defense Against LLM Scheming 2025_04_28.pptxGreg Makowski

04302025_CCC TUG_DataVista: The Design Storyccctableauusergroup

Geometry maths presentation for begginerszrjacob283

Molecular methods diagnostic and monitoring of infection - Repaired.pptx7tzn7x5kky

Thingyan is now a global treasure! See how people around the world are search...Pixellion

Ch3MCT24.pptx measure of central tendencyayeleasefa2

Adobe Analytics NOAM Central User Group April 2025 Agent AI: Uncovering the S...gmuir1066

Knowledge Graphs for Network Digital Twins

5. © 2022 Neo4j, Inc. All rights reserved. Neo4j: Helping the world make sense of their data 160M+ Downloads 230K+ Devs & Data Scientists $390M Series F (June 2021) Largest investment in Database history The Most Trusted Graph Data Platform 7 of the World’s Top 10 Retailers 3 of the Top 5 Aircraft Manufacturers All of North America’s Top 20 Banks 7 of the Top 10 Telcos Neo4j, Inc. All rights reserved 2021 Graph market leader; 1000s of deployments around the globe

6. © 2022 Neo4j, Inc. All rights reserved. Neo4j, Inc. All rights reserved 2021 Graph is the fastest growing DBMS, Neo4j leading the category 6 FASTEST GROWING CATEGORY STRONGEST COMMUNITY LinkedIn Skills 41k+ members with 230k+ Meetups 72k+ Members globally GRAPH PLATFORMS CLOUD DBS

8. © 2022 Neo4j, Inc. All rights reserved. 8 Common challenges 1 2 Need to capture complexity: rich model Allow flexibility: dynamic, real-time view 3 4 Hi-perf: used in operational scenarios. Requirement for parallelism and low latency. Bridge SME - Data gap: Maintainable and humane models

9. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 9 Graph databases are much better at modeling complex environments like networks, where many dynamic relationships exist between data points. Modeling the network as a graph makes network planning decisions much easier. Graph databases also help with path calculation by providing a connectivity model across heterogeneous vendor and data environments. “ BP Inventory Data Sheet (*) (*) https://www.blueplanet.com/resources/inventory-ds.html

10. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 10 Graph data structures provide enormous advantages over relational databases overall. Ericsson has chosen to take this approach because of the tremendous advantage it has to service providers “ Graeme Jones. Strategic Product Manager (*) (*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/

11. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 11 A database that natively supports graphs is optimal for capturing and implementing different operational functions. Topology information, needed to complement event information in correlation, is persisted as a graph and retrieved as needed in support of correlation The sequence of steps, commands, and associated responses are captured as a graph The rules engine handles the reception of events from external OSSs and BSSs and indicates the next branch to be traversed in the decision tree based on inputs and inferences in real time “ HPE UCA Data Sheet (*) (*) https://h20195.www2.hpe.com/v2/Getdocument.aspx?docname=4AA5-1098ENW “ “ “

13. © 2022 Neo4j, Inc. All rights reserved. 13 Largest Telcos 2 of the 3 world’s largest CSP use Neo4j in mission critical solutions 3/5 of the leaders in the latest Gartner MQ for OSS embed Neo4j in their products 2 of the Top 5 US multichannel video service providers have chosen Neo4j 60% OSS Leaders Top MVPD

17. © 2022 Neo4j, Inc. All rights reserved. 17 NDT: At the core of all business decisions https://www.slideshare.net/neo4j/blueplanet-inventory-federation-alvaro-osle-blueplanet

18. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. • Unified Network Inventory • Network Reconciliation • Network and Service Orchestration: Topology • Service Assurance: Fault Management • Planning, Traffic Intelligence • Performance and Quality Management and Analytics 18 Network-Facing Use Cases: Success Stories

Knowledge Graphs for Network Digital Twins

Recommended

More Related Content

What's hot (20)

Similar to Knowledge Graphs for Network Digital Twins (20)

More from Neo4j (20)

Recently uploaded (20)

Knowledge Graphs for Network Digital Twins