Graph-Powered Digital Asset Management with Neo4j

•

1 like•752 views

Managing digital assets and instance-level metadata is critical to many company's business. It affects everything from content availability to analysis of customer usage behavior to the ability to get insights to monetization potential, and drive business innovation. In this session Jesús will explain how companies are leveraging the advantages of a graph platform like Neo4j over traditional relational databases and other types of data and metadata stores for DAM and discuss the success stories of Scripps Networks and Adobe Behance.

Software

Graph-Powered Digital Asset
Management with Neo4j
7th March 2019

Jesús Barrasa
Director Telco&Media Solutions at Neo4j
jesus.barrasa@neo4j.com
@barrasaDV
2

➔ What is DAM + Areas where
graphs add value
➔ Two DAM success stories
➔ Q&A

What is DAM?
4
Digital asset management (DAM) solves the problem
of storing, organising, finding, retrieving, sharing and
distributing rich-media assets (text, graphics, images,
video, audio...)

5
Key Capabilities in DAM solutions
Repository and library services
Metadata and taxonomy management
Search and navigation
Lifecycle and rights management capabilities
Identity and Access Management
Production analytics
Repository and library services
Metadata and taxonomy management
Search and navigation
Lifecycle and rights management capabilities
Identity and Access Management
Production analytics

Two success stories
➔ Scripps Networks Asset Registry
➔ Adobe Behance Activity Feed

“All organisations are now media organisations,
based on the amount of content they create,
the audiences they deliver it to, and the stories
they tell to connect with customers...” (*)
(*) https://www.forrester.com/go?objectid=RES138067

Neo4j’s Property Graph
8
Capture Complexity
Flexibility
High Performance
Scale
Rich, Dynamic Graph
Model on a Native
Graph Platform
DAM Needs...
Neo4j - The worlds leading Graph Platform

Scripps Networks Asset Registry
Model Flexibility / Expressivity

Scripps Asset Registry
Single point of access to all asset data from multiple DAMs
→ Registry style MDM

The Model: Multiple DAMS in one graph
Broadcast Video
Related Assets
(recipes)
Production
Deliverables

The Model: Flexibility is key
MATCH (a:Asset)-[:PART*]-(nlv:Asset)
WHERE a.scrid = “1134”
AND nlv.type = “NLVideo”
RETURN nlv.scrid

Scripps Networks Asset Registry
Model Flexibility requirements
New asset types, attributes and relationships
need to be easy to create/add
Need to be able to query related data
https://www.youtube.com/watch?v=4jlK88WXm8I
Graph Characteristics
10M to 47M nodes in 3 years (x5)
Read/Write ~ 400/1
Peak 8K requests/min (AVG of 3K)
35Bill – 55Bill weekly requests

Adobe Behance Activity Feed
Performance and Efficiency

Solution based on Cassandra
Cluster of 48 nodes
Dataset ~50tb
Action broadcast taking 15 min (!)
Activity Feed Old Approach

MATCH (u:User { id: “11”})-[:FOLLOWS]->(f:User)-[p:PUBLISH]->(pr:Project)
RETURN f.name, p.date, pr ORDER BY p.date DESC
FOLLOWS
APPRECIATE
PUBLISH
FOLLOWS
Activity Feed New Graph Data Model

$Techniques for boosting read perf on dense nodes : Tiered Relationships MATCH (u:User { id: “11”})-[:FOLLOWS]->(f:User)-[p:LAST_200]->(pr:Project) RETURN f.name, p.date, p.type, pr ORDER BY p.date DESC$

Techniques for boosting read perf on dense nodes : Qualified Relationships
MATCH (u:User)-[:FOLLOWS]->(f:User)-[p:APPRECIATE_32]->(pr:Project)
RETURN f.name, pr
ORDER BY p.date DESC
https://maxdemarzi.com/

#cats selected Column Store Neo4j
1 .55 .21
4 1.15 .29
13 4.07 .65
Avg processing time in seconds
Improved onboarding
experience

● Human maintenance hours
down well over 300%
● 1/1000 storage required
● Lower Cost
● Improved user-facing
functionality
● Easy extensibility
Behance Activity Feed
https://www.youtube.com/watch?v=bPM9hVorPSM

Takeaways
➔ Graph → efficient / flexible / expressive
representation of asset data
➔ Neo4j platform gives you a head start
➔ Are you ready to build/enhance your DAM
solution with Neo4j?

What next?
1. Go to neo4j.com and download our graph
platform.
2. Watch/Read the resources mentioned in this
webinar. Don’t take my word for it.
3. Join our online community site to learn from
others and to share your experience
https://community.neo4j.com/
4. Reach out to us for licensing info and best
practices on building DAM solutions with Neo4j

Thanks!
https://neo4j.com/contact-us/
https://community.neo4j.com/

What's hot

Smarter Fraud Detection With Graph Data Science

Neo4j

Big Data Monetization - The Path From Internal to External

cVidya Networks

Modern Data Challenges require Modern Graph Technology

Neo4j

Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are: What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers. How much to Buy? -- what are the most and least popular items at any given time interval When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices. We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.

A Connections-first Approach to Supply Chain Optimization

Neo4j

How Will Knowledge Graphs Improve Clinical Reporting Workflows

Neo4j

Jérémy Grignard, Data & Research Scientist, Servier Les données que nous exploitons sont issues de domaines scientifiques variés comme les sciences omiques, structurales, cellulaires, chimiques ou phénotypiques, et correspondent à des concepts pharmaco-biologiques hétérogènes. Nous développons le graphe de connaissances Pegasus qui vise, en plus de capitaliser sur des données actuellement disponibles, à explorer l’environnement complexe des cibles thérapeutiques, à identifier des modalités de criblage pertinentes et à concevoir de nouvelles expériences.

SERVIER Pegasus - Graphe de connaissances pour les phases primaires de recher...

Neo4j

Introduction to Knowledge Graphs

mukuljoshi

The art of the possible with graph technology_Neo4j GraphSummit Dublin 2023.pptx

Neo4j

The path to success with Graph Database and Graph Data Science

Neo4j

Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.

Emerging Trends in Data Architecture – What’s the Next Big Thing?

DATAVERSITY

Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptx

Neo4j

Neo4j Generative AI workshop at GraphSummit London 14 Nov 2023.pdf

Neo4j

Graph databases are growing in popularity, with their ability to quickly discover and integrate key relationship between enterprise data sets. Business use cases such as recommendation engines, master data management, social networks, enterprise knowledge graphs and more provide valuable ways to leverage graph databases in your organization. This webinar provides an overview of graph database technologies, and how they can be used for practical applications to drive business value.

Data Architecture Strategies: The Rise of the Graph Database

DATAVERSITY

Introduction to Neo4j for the Emirates & Bahrain

Neo4j

Recommendations at Zillow

njstevens

At Neo4j we believe that “Graphs Are Everywhere”. In this session, we’ll be exploring graphs within the Retail industry. We’ll discuss a range of data that are commonly available within a retail organisation, both online and “brick and mortar". We’ll illustrate some graphs which can be created by linking together different elements of that data and discuss the retail use cases those graphs can enable and transform. We’ll specifically focus on use cases like Personalised Recommendations (with a live demo), Supply Chain Management, Logistics, and Customer 360. We'll also look at some relevant graph algorithms and talk about opportunities for integration with Artificial Intelligence/Machine Learning technologies, which can be used along with Neo4j to generate new value using retail data. Walmart, Wobi, and others already deploy Neo4j for use cases like price comparison or real-time contextual and learning recommendation engines. Read about their use cases!

Graphs in Retail: Know Your Customers and Make Your Recommendations Engine Learn

Neo4j

There is a growing interest in knowledge graphs to organize information and make it findable in organizations with large amounts of data and content. Unlike other data technologies, a knowledge graph has a structure that is typically based on a taxonomy and ontology, and thus should involve information architects. Knowledge graphs also have more benefits than information findability, including discovery, analysis, and recommendation. Knowledge graphs bring together content and data. An enterprise knowledge graph involves a change in thinking about information and its access. Instead of designing information architecture in individual applications, an intranet, or website, a knowledge graph extracts data and links to content that exists in multiple different applications and repositories, linking them in a web or graph-like structure by means of customized, semantic relationships.

Introduction to Knowledge Graphs for Information Architects.pdf

Heather Hedden

Optimizing Your Supply Chain with the Neo4j Graph

Neo4j

Neo4j Graph Platform Overview, Kurt Freytag, Neo4j

Neo4j

Visualising Data with Code

Ri Liu

What's hot (20)

Smarter Fraud Detection With Graph Data Science

Big Data Monetization - The Path From Internal to External

Modern Data Challenges require Modern Graph Technology

A Connections-first Approach to Supply Chain Optimization

How Will Knowledge Graphs Improve Clinical Reporting Workflows

SERVIER Pegasus - Graphe de connaissances pour les phases primaires de recher...

Introduction to Knowledge Graphs

The art of the possible with graph technology_Neo4j GraphSummit Dublin 2023.pptx

The path to success with Graph Database and Graph Data Science

Emerging Trends in Data Architecture – What’s the Next Big Thing?

Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptx

Neo4j Generative AI workshop at GraphSummit London 14 Nov 2023.pdf

Data Architecture Strategies: The Rise of the Graph Database

Introduction to Neo4j for the Emirates & Bahrain

Recommendations at Zillow

Graphs in Retail: Know Your Customers and Make Your Recommendations Engine Learn

Introduction to Knowledge Graphs for Information Architects.pdf

Optimizing Your Supply Chain with the Neo4j Graph

Neo4j Graph Platform Overview, Kurt Freytag, Neo4j

Visualising Data with Code

Similar to Graph-Powered Digital Asset Management with Neo4j

Big Data at DYNO

Tu Pham

A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.

RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica

Spark Summit

Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.

RISELab:Enabling Intelligent Real-Time Decisions

Jen Aman

The millions of people that use Spotify each day generate a lot of data, roughly a few terabytes per day. What does it take to handle datasets of that scale, and what can be done with it? I will briefly cover how Spotify uses data to provide a better music listening experience, and to strengthen their busineess. Most of the talk will be spent on our data processing architecture, and how we leverage state of the art data processing and storage tools, such as Hadoop, Cassandra, Kafka, Storm, Hive, and Crunch. Last, I'll present observations and thoughts on innovation in the data processing aka Big Data field.

Data Infrastructure for a World of Music

Lars Albertsson

Building search and discovery services for Schibsted (LSRS '17)

Sandra Garcia

Yale University Shares Digital Asset Management Success Story See how Yale University uses Extensis Portfolio Server™ to: - Create catalogs using images, PDFs, audio and video files for student review and course work - Enhance teaching with digital resources - Improve visual literacy amongst students - Implement digital asset management best practices Presenters: Yale University Sr. Instructional Technologist, Pamela Patterson

Yale University: Digital Asset Collections and Course Materials

Extensis

Data Warehouse to Data Science

Chandan Rajah

Introduction to Azure DocumentDB

Denny Lee

Scaling up with Cisco Big Data: Data + Science = Data Science

eRic Choo

Multiplatform Spark solution for Graph datasources by Javier Dominguez

Big Data Spain

"Data mesh is a relatively recent architectural innovation, espoused as one of the best ways to fix analytic data. We renegotiate aged social conventions by focusing on treating data as a product, with a clearly defined data product owner, akin to that of any other product. In addition, we focus on building out a self-service platform with integrated governance, letting consumers safely access and use the data they need to solve their business problems. Data mesh is prescribed as a solution for _analytical data_, so that conventionally analytical results (think weekly sales or monthly revenue reports) can be more accurately and predictably computed. But what about non-analytical business operations? Would they not also benefit from data products backed by self-service capabilities and dedicated owners? If you've ever provided a customer with an analytical report that differed from their operational conclusions, then this talk is for you. Adam discusses the resounding successes he has seen from applying data mesh _off-label_ to both analytical and operational domains. The key? Event streams. Well-defined, incrementally updating data products that can power both real-time and batch-based applications, providing a single source of data for a wide variety of application and analytical use cases. Adam digs into the common areas of success seen across numerous clients and customers and provides you with a set of practical guidelines for implementing your own minimally viable data mesh. Finally, Adam covers the main social and technical hurdles that you'll encounter as you implement your own data mesh. Learn about important data use cases, data domain modeling techniques, self-service platforms, and building an iteratively successful data mesh."

Off-Label Data Mesh: A Prescription for Healthier Data

HostedbyConfluent

Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...

Cloudera, Inc.

Data Lake, Business Intelligence, Enterprise Data Warehouse, Big Data Pipeline, Online Machine Learning, Lambda Architecture, Streaming, Spark, Kafka, Storm, Flink, Hadoop, Mesos and SMACK stack are some of the things you hear about when you want to dive into building a data pipeline. The Big Data Landscape cannot fit on a single screen as seen in the presentation. This is in addition to all the Big Data & Machine Learning offerings AWS has been introducing over the past few years which address many pain points highlighted by the various communities and help you get up and running faster. The objective of this talk is to provide the audience with a framework which helps them define their pipeline problems, isolate components and pick the right tools for the right job. We will talk about: 1. A consistent definition of BIG in big data 2. The lineage of fundamental tools in the ecosystem 3. First principles of a big data pipeline based on the lambda (not lambda functions) and kappa architectures 4. Distinguishing between big data and online machine learning pipelines 5. Technology choices based on first principles, open source solutions and AWS offerings 6. Demo: Serverless, Managed Big Data Pipeline and real-time dashboard on AWS (orchestrated via Terraform) Presented at: https://www.meetup.com/Vancouver-Amazon-Web-Services-User-Group/events/245946651/

Big Data & Machine Learning Pipelines: A Tale of Lambdas, Kappas and Pancakes

Osama Khan

Presented by Susanna Mornati at the 2019 DSpace North American User Group Meeting September 23 & 24, 2019 at the University of Minnesota in Minneapolis. Abstract: DSpace-CRIS is a free open-source platform based on DSpace for Research Data and Information Management, adopted by a wide international community of universities and research centers: DSpace-CRIS Home. It complies with recommendations, open standards and technologies such as the OAI-PMH, SignPosting, and ResourceSync (recommended by the COAR Next Generation Repositories WG), it features complete ORCID integration, compliance with the CERIF model, the IIIF framework, and with the OpenAIRE Guidelines for Literature Repositories, Data Archives, CRIS Managers, to improve findability, accessibility, interoperability, and reuse of digital assets for research and cultural heritage. DSpace-CRIS collects and disseminates information about researchers' profiles, organizations, publications, patents, grants, awards, and all entities that populate the research domain and their relationships, besides storing and exposing full-text publications, datasets, and other relevant digital objects, providing persistent identifiers and long-term preservation capabilities. DSpace-RDM exposes datasets to visual exploration and M2M streaming for analysis thanks to the integration with CKAN. DSpace-GLAM enhances the fruition of the cultural heritage through the (crowd-funded) IIIF image viewer, providing remote fruition of cultural heritage and offering a great user experience. These flavors of DSpace allow to expose and share open data, open information, and open digital objects in a collaborative, interoperable, and sustainable way. The use cases of a variety of institutions in different countries and continents will be shared to show the use of this powerful technology.

How to enhance your DSpace repository: use cases for DSpace-CRIS, DSpace-RDM,...

4Science

BigData

Shankar R

Experiments With Knowledge Graphs in Fisheries & Oceans Canada

Neo4j

Making sense of your data jug

Gerald Muecke

We started as an ad network. The challenge was to recommend the best product (out of millions) to the right person in a given moment (thousands of users within a second). We have delivered 5 billion ad views since 24 months. To put it in the scale context: If we would serve 1 ad per second it will take 160 years to serve 5 billion ads. So we needed a solution. SQL databases did not work. Popular NoSQL databases did not work. Standard data warehouse approaches (pre-aggregations, creating schemas) - did not work too. Re-thinking all the problems with huge data streams flowing to us every second we have built a complete solution based on open-source technologies and fresh, smart ideas from our engineering team. It is called deep.bi and now we make it available to other companies. deep.bi lets high-growth companies solve fast data problems by providing scalable, flexible and real-time data collection, enrichment and analytics. It was built using: - Node.js - API - Kafka - collecting and distributing data - Spark Streaming - ETL, data enrichments - Druid - real-time analytics - Cassandra - user events store - Hadoop + Parquet + Spark - raw data store + ad-hoc queries

Real-time big data analytics based on product recommendations case study

deep.bi

Cassandra & puppet, scaling data at $15 per month

daveconnors

The hidden engineering behind machine learning products at Helixa

Alluxio, Inc.

Similar to Graph-Powered Digital Asset Management with Neo4j (20)

Big Data at DYNO

RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica

RISELab:Enabling Intelligent Real-Time Decisions

Data Infrastructure for a World of Music

Building search and discovery services for Schibsted (LSRS '17)

Yale University: Digital Asset Collections and Course Materials

Data Warehouse to Data Science

Introduction to Azure DocumentDB

Scaling up with Cisco Big Data: Data + Science = Data Science

Multiplatform Spark solution for Graph datasources by Javier Dominguez

Off-Label Data Mesh: A Prescription for Healthier Data

Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...

Big Data & Machine Learning Pipelines: A Tale of Lambdas, Kappas and Pancakes

How to enhance your DSpace repository: use cases for DSpace-CRIS, DSpace-RDM,...

BigData

Experiments With Knowledge Graphs in Fisheries & Oceans Canada

Making sense of your data jug

Real-time big data analytics based on product recommendations case study

Cassandra & puppet, scaling data at $15 per month

The hidden engineering behind machine learning products at Helixa

GraphAware - Transforming policing with graph-based intelligence analysis

Neo4j

GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates

Neo4j

KLARNA - Language Models and Knowledge Graphs: A Systems Approach

Neo4j

INGKA DIGITAL: Linked Metadata by Design

Neo4j

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.

Your enemies use GenAI too - staying ahead of fraud with Neo4j

Neo4j

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.

BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx

Neo4j

Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan

Neo4j

Workshop 1. Architecting Innovative Graph Applications Join this hands-on workshop for beginners led by Neo4j experts guiding you to systematically uncover contextual intelligence. Using a real-life dataset we will build step-by-step a graph solution; from building the graph data model to running queries and data visualization. The approach will be applicable across multiple use cases and industries.

Workshop - Architecting Innovative Graph Applications- GraphSummit Milan

Neo4j

Roberto Sannino, Larus Business Automation Nel panorama sempre più complesso dei progetti basati su grafi, LARUS ha consolidato una solida esperienza pluriennale, costruendo un rapporto di fiducia e collaborazione con Neo4j. Attraverso il LARUS Labs, ha sviluppato componenti e connettori che arricchiscono l’ecosistema Neo4j, contribuendo alla sua continua evoluzione. Tutto questo know-how è stato incanalato nell’innovativa soluzione Galileo.XAI di LARUS, un prodotto all’avanguardia che, integrato con la Generative AI, offre una nuova prospettiva nel mondo dell’Intelligenza Artificiale Spiegabile applicata ai grafi. In questo speech, si esplorerà il percorso di crescita di LARUS in questo settore, mettendo in luce le potenzialità della soluzione Galileo.XAI nel guidare l’innovazione e la trasformazione digitale.

LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...

Neo4j

GraphSummit Milan - Visione e roadmap del prodotto Neo4j

Neo4j

Dr Jesús Barrasa, Head of Solutions Architecture for EMEA, 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.

GraphSummit Milan & Stockholm - Neo4j: The Art of the Possible with Graph

Neo4j

Roberto Sannino, Product Owner, Larus Business Automation Nel panorama sempre più complesso dei progetti basati su grafi, LARUS ha consolidato una solida esperienza pluriennale, costruendo un rapporto di fiducia e collaborazione con Neo4j. Attraverso il LARUS Labs, ha sviluppato componenti e connettori che arricchiscono l’ecosistema Neo4j, contribuendo alla sua continua evoluzione. Tutto questo know-how è stato incanalato nell’innovativa soluzione Galileo.XAI di LARUS, un prodotto all’avanguardia che, integrato con la Generative AI, offre una nuova prospettiva nel mondo dell’Intelligenza Artificiale Spiegabile applicata ai grafi. In questo speech, si esplorerà il percorso di crescita di LARUS in questo settore, mettendo in luce le potenzialità della soluzione Galileo.XAI nel guidare l’innovazione e la trasformazione digitale.

LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...

Neo4j

UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida

Neo4j

CERVED e Neo4j su una nuvola, migrazione ed evoluzione di un grafo mission cr...

Neo4j

From Knowledge Graphs via Lego Bricks to scientific conversations.pptx

Neo4j

Novo Nordisk: When Knowledge Graphs meet LLMs

Neo4j

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Neo4j

Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...

Neo4j

QIAGEN: Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Neo4j

EY_Graph Database Powered Sustainability

Neo4j

More from Neo4j (20)

GraphAware - Transforming policing with graph-based intelligence analysis

GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates

KLARNA - Language Models and Knowledge Graphs: A Systems Approach

INGKA DIGITAL: Linked Metadata by Design

Your enemies use GenAI too - staying ahead of fraud with Neo4j

BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx

Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan

Workshop - Architecting Innovative Graph Applications- GraphSummit Milan

LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...

GraphSummit Milan - Visione e roadmap del prodotto Neo4j

GraphSummit Milan & Stockholm - Neo4j: The Art of the Possible with Graph

LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...

UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida

CERVED e Neo4j su una nuvola, migrazione ed evoluzione di un grafo mission cr...

From Knowledge Graphs via Lego Bricks to scientific conversations.pptx

Novo Nordisk: When Knowledge Graphs meet LLMs

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...

QIAGEN: Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

EY_Graph Database Powered Sustainability

Recently uploaded

Agnieszka Andrzejewska - BIM School Course in Kraków

bim.edu.pl

AI/ML Infra Meetup | Perspective on Deep Learning Framework

Alluxio, Inc.

AI/ML Infra Meetup May. 23, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Junchen Jiang (Assistant Professor of Computer Science, @University of Chicago) Prefill in LLM inference is known to be resource-intensive, especially for long LLM inputs. While better scheduling can mitigate prefill’s impact, it would be fundamentally better to avoid (most of) prefill. This talk introduces our preliminary effort towards drastically minimizing prefill delay for LLM inputs that naturally reuse text chunks, such as in retrieval-augmented generation. While keeping the KV cache of all text chunks in memory is difficult, we show that it is possible to store them on cheaper yet slower storage. By improving the loading process of the reused KV caches, we can still significantly speed up prefill delay while maintaining the same generation quality.

AI/ML Infra Meetup | Reducing Prefill for LLM Serving in RAG

Alluxio, Inc.

Key takeaways: Challenges of building platforms and the benefits of platformless. Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience. How Choreo enables the platformless experience. How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo. Demo of an end-to-end app built and deployed on Choreo.

Accelerate Enterprise Software Engineering with Platformless

WSO2

In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey. Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience. Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system. Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.

Beyond Event Sourcing - Embracing CRUD for Wix Platform - Java.IL

Natan Silnitsky

Top Mobile App Development Companies 2024

XongoLab Technologies LLP

Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...

informapgpstrackings

Unlocking Business Potential: Tailored Technology Solutions by Prosigns Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support. Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth. Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices. AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making. Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency. DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration. Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly. Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business. Join us on a journey of innovation and growth. Let's partner for success with Prosigns.

Prosigns: Transforming Business with Tailored Technology Solutions

Prosigns

Facemoji Keyboard released its 2023 State of Emoji report, outlining the most...

rajkumar669520

CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.

Cyaniclab : Software Development Agency Portfolio.pdf

Cyanic lab

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.

top nidhi software solution freedownload

vrstrong314

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

Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...

Anthony Dahanne

"Introduction to Windows 7" serves as the foundational chapter in our guide, setting the stage for understanding the key features and functionalities of this operating system. Windows 7, released by Microsoft in 2009, quickly became one of the most popular and widely used versions of Windows due to its user-friendly interface, stability, and performance improvements over its predecessor, Windows Vista. This chapter begins by providing an overview of the Windows 7 operating system, highlighting its key attributes and improvements compared to earlier versions of Windows. It introduces users to the visual enhancements such as Aero Glass, the revamped taskbar (also known as the Superbar), and the redesigned Start menu, which all contribute to a more intuitive and streamlined user experience. Furthermore, "Introduction to Windows 7" delves into the architecture and system requirements of the operating system, helping users understand what hardware specifications are necessary for optimal performance. It covers topics such as processor requirements, RAM, disk space, and graphics capabilities, ensuring that readers have a clear 3 understanding of the hardware prerequisites for running Windows 7 smoothly. Additionally, this chapter explores the various editions of Windows 7, including Home Premium, Professional, Ultimate, and Enterprise, outlining the differences between them and helping users choose the edition that best suits their needs and requirements. Moreover, "Introduction to Windows 7" provides an overview of the installation process, guiding users through the steps required to install or upgrade to Windows 7 on their computers. It covers topics such as preparing for installation, choosing the installation type (upgrade or custom), partitioning disks, and configuring initial settings. In summary, "Introduction to Windows 7" serves as a comprehensive primer for users who are new to the operating system or seeking to refresh their understanding. By familiarizing themselves with the core concepts and features of Windows 7, readers can lay a solid foundation for exploring more advanced topics covered in subsequent chapters of this guide.

Mastering Windows 7 A Comprehensive Guide for Power Users .pdf

mbmh111980

Advanced Flow Concepts Every Developer Should Know

Peter Caitens

Into the Box 2024 - Keynote Day 2 Slides.pdf

Ortus Solutions, Corp

AI/ML Infra Meetup May. 23, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Eric Wang (Software Engineer, @Uber) Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes. In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.

AI/ML Infra Meetup | ML explainability in Michelangelo

Alluxio, Inc.

Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload. Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.

Designing for Privacy in Amazon Web Services

KrzysztofKkol1

Crafting the Perfect Measurement Sheet with PLM Integration

Wave PLM

Breaking the Code : A Guide to WhatsApp Business API.pdf

Meon Technology

WSO2Con2024 - WSO2's IAM Vision: Identity-Led Digital Transformation

WSO2

Recently uploaded (20)

Agnieszka Andrzejewska - BIM School Course in Kraków

AI/ML Infra Meetup | Perspective on Deep Learning Framework

AI/ML Infra Meetup | Reducing Prefill for LLM Serving in RAG

Accelerate Enterprise Software Engineering with Platformless

Beyond Event Sourcing - Embracing CRUD for Wix Platform - Java.IL

Top Mobile App Development Companies 2024

Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...

Prosigns: Transforming Business with Tailored Technology Solutions

Facemoji Keyboard released its 2023 State of Emoji report, outlining the most...

Cyaniclab : Software Development Agency Portfolio.pdf

top nidhi software solution freedownload

Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...

Mastering Windows 7 A Comprehensive Guide for Power Users .pdf

Advanced Flow Concepts Every Developer Should Know

Into the Box 2024 - Keynote Day 2 Slides.pdf

AI/ML Infra Meetup | ML explainability in Michelangelo

Designing for Privacy in Amazon Web Services

Crafting the Perfect Measurement Sheet with PLM Integration

Breaking the Code : A Guide to WhatsApp Business API.pdf

WSO2Con2024 - WSO2's IAM Vision: Identity-Led Digital Transformation

Graph-Powered Digital Asset Management with Neo4j

1. Graph-Powered Digital Asset Management with Neo4j 7th March 2019

2. Jesús Barrasa Director Telco&Media Solutions at Neo4j jesus.barrasa@neo4j.com @barrasaDV 2

3. ➔ What is DAM + Areas where graphs add value ➔ Two DAM success stories ➔ Q&A

4. What is DAM? 4 Digital asset management (DAM) solves the problem of storing, organising, finding, retrieving, sharing and distributing rich-media assets (text, graphics, images, video, audio...)

5. 5 Key Capabilities in DAM solutions Repository and library services Metadata and taxonomy management Search and navigation Lifecycle and rights management capabilities Identity and Access Management Production analytics Repository and library services Metadata and taxonomy management Search and navigation Lifecycle and rights management capabilities Identity and Access Management Production analytics

6. Two success stories ➔ Scripps Networks Asset Registry ➔ Adobe Behance Activity Feed

7. “All organisations are now media organisations, based on the amount of content they create, the audiences they deliver it to, and the stories they tell to connect with customers...” (*) (*) https://www.forrester.com/go?objectid=RES138067

8. Neo4j’s Property Graph 8 Capture Complexity Flexibility High Performance Scale Rich, Dynamic Graph Model on a Native Graph Platform DAM Needs... Neo4j - The worlds leading Graph Platform

9. Scripps Networks Asset Registry Model Flexibility / Expressivity

10.

11.

12. Scripps Asset Registry Single point of access to all asset data from multiple DAMs → Registry style MDM

13. The Model: Multiple DAMS in one graph Broadcast Video Related Assets (recipes) Production Deliverables

14. The Model: Flexibility is key MATCH (a:Asset)-[:PART*]-(nlv:Asset) WHERE a.scrid = “1134” AND nlv.type = “NLVideo” RETURN nlv.scrid

15. Scripps Networks Asset Registry Model Flexibility requirements New asset types, attributes and relationships need to be easy to create/add Need to be able to query related data https://www.youtube.com/watch?v=4jlK88WXm8I Graph Characteristics 10M to 47M nodes in 3 years (x5) Read/Write ~ 400/1 Peak 8K requests/min (AVG of 3K) 35Bill – 55Bill weekly requests

16. Adobe Behance Activity Feed Performance and Efficiency

17.

18. Pain Point: Fan out Model

19. Solution based on Cassandra Cluster of 48 nodes Dataset ~50tb Action broadcast taking 15 min (!) Activity Feed Old Approach

20. MATCH (u:User { id: “11”})-[:FOLLOWS]->(f:User)-[p:PUBLISH]->(pr:Project) RETURN f.name, p.date, pr ORDER BY p.date DESC FOLLOWS APPRECIATE PUBLISH FOLLOWS Activity Feed New Graph Data Model

21. Techniques for boosting read perf on dense nodes : Tiered Relationships MATCH (u:User { id: “11”})-[:FOLLOWS]->(f:User)-[p:LAST_200]->(pr:Project) RETURN f.name, p.date, p.type, pr ORDER BY p.date DESC

22. Techniques for boosting read perf on dense nodes : Qualified Relationships MATCH (u:User)-[:FOLLOWS]->(f:User)-[p:APPRECIATE_32]->(pr:Project) RETURN f.name, pr ORDER BY p.date DESC https://maxdemarzi.com/

23. #cats selected Column Store Neo4j 1 .55 .21 4 1.15 .29 13 4.07 .65 Avg processing time in seconds Improved onboarding experience

24. ● Human maintenance hours down well over 300% ● 1/1000 storage required ● Lower Cost ● Improved user-facing functionality ● Easy extensibility Behance Activity Feed https://www.youtube.com/watch?v=bPM9hVorPSM

25. Takeaways ➔ Graph → efficient / flexible / expressive representation of asset data ➔ Neo4j platform gives you a head start ➔ Are you ready to build/enhance your DAM solution with Neo4j?

26. What next? 1. Go to neo4j.com and download our graph platform. 2. Watch/Read the resources mentioned in this webinar. Don’t take my word for it. 3. Join our online community site to learn from others and to share your experience https://community.neo4j.com/ 4. Reach out to us for licensing info and best practices on building DAM solutions with Neo4j

27. Thanks! https://neo4j.com/contact-us/ https://community.neo4j.com/

Graph-Powered Digital Asset Management with Neo4j

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Graph-Powered Digital Asset Management with Neo4j

Similar to Graph-Powered Digital Asset Management with Neo4j (20)

More from Neo4j

More from Neo4j (20)

Recently uploaded

Recently uploaded (20)

Graph-Powered Digital Asset Management with Neo4j