Graphs & Big Data - Philip Rathle and Andreas Kollegger @ Big Data Science Meetup, Fremont, CA

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.

Neo4j is a graph database that stores data in nodes and relationships. It allows for efficient querying of connected data through graph traversals. Key aspects include nodes that can contain properties, relationships that connect nodes and also contain properties, and the ability to navigate the graph through traversals. Neo4j provides APIs for common graph operations like creating and removing nodes/relationships, running traversals, and managing transactions. It is well suited for domains that involve connected, semi-structured data like social networks.

This document provides an overview of graph databases and Neo4j. It begins with an introduction to graph databases and their advantages over relational databases for modeling connected data. Examples of real-world use cases that are well-suited for graph databases are given. The document then describes the core components of the graph data model including nodes, relationships, properties, and labels. It provides examples of how to model data as a graph and query graphs using Cypher, the query language for Neo4j. The document concludes by discussing Neo4j as an example of a graph database and its key features and capabilities.

Graph Databases try to make it easy for developers to leverage huge amounts of connected information for everything from routing to recommendations. Doing that poses a number of challenges on the implementation side. In this talk we want to look at the different storage, query and consistency approaches that are used behind the scenes. We’ll check out current and future solutions used in Neo4j and other graph databases for addressing global consistency, query and storage optimization, indexing and more and see which papers and research database developers take inspirations from.

Before jumping straight in to development of such an graph based app, we asked the question that anyone would ask - "what makes it a case for Neo4J? and can you prove it?" Basically de-risking and making a case for management buy in. Further, its more about convincing ourselves as well and hence this comparison. So this is about that comparison and the white-paper that has resulted from it. It is not the actual project. Source code used to generate the comparison numbers is available on https://github.com/EqualExperts/Apiary-Neo4j-RDBMS-Comparison

Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni: Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.

The document discusses big data visualization and visual analysis, focusing on the challenges and opportunities. It begins with an overview of visualization and then discusses several challenges in big data visualization, including integrating heterogeneous data from different sources and scales, dealing with data and task complexity, limited interaction capabilities for large data, scalability for both data and users, and the need for domain and development libraries/tools. It then provides examples of visualizing taxi GPS data and traffic patterns in Beijing to identify traffic jams.

Graph databases are one of the leading drivers in the emerging, highly heterogeneous landscape of database management systems for non-relational data management and processing. The recent interest and success of graph databases arises mainly from the growing interest in social media analysis and the exploration and mining of relationships in social media data. However, with a graph-based model as a very flexible underlying data model, a graph database can serve a large variety of scenarios from different domains such as travel planning, supply chain management and package routing. During the past months, many vendors have designed and implemented solutions to satisfy the need to efficiently store, manage and query graph data. However, the solutions are very diverse in terms of the supported graph data model, supported query languages, and APIs. With a growing number of vendors offering graph processing and graph management functionality, there is also an increased need to compare the solutions on a functional level as well as on a performance level with the help of benchmarks. Graph database benchmarking is a challenging task. Already existing graph database benchmarks are limited in their functionality and portability to different graph-based data models and different application domains. Existing benchmarks and the supported workloads are typically based on a proprietary query language and on a specific graph-based data model derived from the mathematical notion of a graph. The variety and lack of standardization with respect to the logical representation of graph data and the retrieval of graph data make it hard to define a portable graph database benchmark. In this talk, we present a proposal and design guideline for a graph database benchmark. Typically, a database benchmark consists of a synthetically generated data set of varying size and varying characteristics and a workload driver. In order to generate graph data sets, we present parameters from graph theory, which influence the characteristics of the generated graph data set. Following, the workload driver issues a set of queries against a well-defined interface of the graph database and gathers relevant performance numbers. We propose a set of performance measures to determine the response time behavior on different workloads and also initial suggestions for typical workloads in graph data scenarios. Our main objective of this session is to open the discussion on graph database benchmarking. We believe that there is a need for a common understanding of different workloads for graph processing from different domains and the definition of a common subset of core graph functionality in order to provide a general-purpose graph database benchmark. We encourage vendors to participate and to contribute with their domain-dependent knowledge and to define a graph database benchmark proposal.

The Twitter data firehose delivers hundreds of millions of Tweets every day. This data flood comes with many ‘big data’ challenges in terms of both data volumes and velocities. This presentation will focus on tools that help you find your data ‘signal’ of interest, and will include several demos that focus on using Twitter for flood early-warning systems. These demos will highlight the real-time, public broadcast nature of Twitter, examples of real-time firehose filtering, as well as recent Internet of Things (IoT) Twitter integrations.

The document discusses how Puhui Finance, a Chinese P2P lending company, uses big data and AI techniques for risk control. It introduces their Feature Compute Engine, which converts unstructured user data into structured features, and their Knowledge Graph, which connects entities and analyzes relationships. Specific use cases discussed include anti-fraud detection using rules, contact recovery by building phone networks, and detecting high-risk individuals via search engines. Challenges around unstructured data, name disambiguation, reasoning and lack of training data are also covered.

The document is a presentation by Manash Ranjan Rautray on introducing graph databases and Neo4j. It discusses what a graph and graph database are, provides examples to illustrate graphs, and covers the basics of using Neo4j including its data model, query language Cypher, and real-world use cases for graph databases. The presentation aims to explain the concepts and capabilities of Neo4j for storing and querying connected data.

Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market. A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships. Ryan Boyd, Developer Relations at Neo4j Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.

Graph databases are well suited for complex, interconnected data. Neo4j is a graph database that represents data as nodes connected by relationships. It allows for complex queries and traversals of graph structures. Unlike relational databases, graph databases can directly model real world networks and relationships without needing to flatten the data.

In this webinar we discuss the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible. We cover the high-level steps of modeling, importing, and querying your data using Cypher and give an overview of the transition from RDBMS to Graph.

The document discusses building a data processing pipeline in Python to handle ingesting poorly formatted data dispersed across the web. It covers data ingestion using requests and futures, parsing with tools like BeautifulSoup, cleansing data with Celery job scheduling, and scaling out the pipeline with distributed task queues and SQL database sharding.

The document discusses using graph databases for insights into connected data. It provides an overview of graph databases, comparing them to relational databases and NoSQL stores. It discusses how graph databases are better suited than other models for richly connected data due to their native support of relationships. The document also covers graph data modeling, the Cypher query language, examples of graph databases in real world domains, and aspects of graph database internals like scalability.

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.

This is a presentation on Big Data graph Clustering with Laurence O'Toole, founder of Analytics SEO at the Digital Marketing Show, November 2015

The document announces a GraphTalks event in Hamburg in March 2017 hosted by Neo Technology. It includes an agenda with sessions on graph databases and Neo4j, semantic data management, and an open networking session.

1) Graph technology can be leveraged to fight financial fraud by detecting fraud rings and relationships between entities like individuals and locations that may indicate fraudulent activity. 2) A graph database like Neo4j is well suited for fraud detection because it can easily model and traverse complex relationships in connected data, identify patterns more quickly than SQL, and enable near real-time response. 3) A fraud detection demo using Neo4j showed how operational data from various sources could be integrated and analyzed to generate alerts when potential fraud cases are detected.

This document provides an agenda for the Neo4j GraphDay Stockholm event on February 21, 2017. The agenda includes speakers from Neo4j and partner companies, and sessions on use cases, hands-on demos, partner presentations, training, and Q&A clinics. The event will take place in Stockholm and cover topics like graphs in action, recommendations in retail, and the manufacturing value chain.

This document discusses how graph databases like Neo4j can help with analytics and information management. It provides examples of how graph queries in Neo4j are simpler than equivalent SQL queries for finding connections between nodes. Graph databases allow for impact analysis and easily reflecting new relationships. They also help with recommendations by incorporating events from the current user session.

The document discusses how Telia scaled its Neo4j graph database to support millions of homes using Kubernetes. It describes the zone API architecture built on Kubernetes, including microservices for the zone API, TheZone agent, API management, and databases like Neo4j, Redis, and Cloud SQL. It also discusses how Kubernetes features like namespaces, auto-scaling, node selectors, and stateful sets were used to scale the Neo4j graph database using causal clustering to support millions of users and billions of requests per day.

The document announces a GraphTalks event in Cologne in February 2017 hosted by Neo Technology. The agenda includes an introduction to graph databases and Neo4j, a presentation on semantic data management, and an open networking session. Complex topics like the internet of things, domain modeling, and traditional vs graph approaches to data modeling will also be discussed.

The document discusses how graph databases like Neo4j can help drive digital transformation, especially in retail. It provides examples of large retailers like Adidas, eBay, and Walmart using Neo4j to power personalized customer experiences, optimize delivery routes, and make relevant product recommendations. The document also discusses how graphs are well-suited for modeling interconnected fraud patterns and can help detect fraud in real-time. It highlights the benefits of Neo4j for augmented connected analysis over legacy technologies.

In this session from Neo4j Government Graphday, Philip Rathle discusses how federal agencies and contractors can utilize graphs to power their applications.

Dirk Möller discusses selecting the right database technology, with a focus on graph databases like Neo4j. He outlines the benefits of graph databases over relational and NoSQL databases for connected data, including high performance, easy maintenance, and seamless evolution. Möller also provides examples of common use cases where graph databases have business benefits in areas like recommendations, fraud detection, and network operations.

This document introduces Neo4j, a graph database developed by Neo Technology. It discusses how graph databases can model and query data relationships more easily than relational or NoSQL databases. The document provides an overview of Neo4j's history and growth, key features, examples of use cases, and how it helps customers like Adidas, Die Bayerische insurance, and SFR communications manage data relationships.

This document summarizes a presentation about using graph databases for identity and access management (IAM). It discusses how IAM systems traditionally assume rigid hierarchies that do not reflect modern complex organizations. Graph databases provide a flexible model for IAM by representing relationships between users, roles, devices, and other entities as nodes connected by relationships. This allows querying complex access scenarios and augmenting existing IAM systems. The presentation provides examples of building full IAM systems or augmenting existing ones using a graph database to better model complex real-world relationships.

This document summarizes Cerved Group's use of Neo4j and graph databases. Cerved processes large amounts of data on companies and individuals to provide credit risk management, marketing, and other services. Neo4j allows Cerved to more efficiently analyze relationships between entities, such as beneficial owners of companies. Cerved's Graph4You platform makes some of this graph data accessible to customers and data scientists to explore use cases. Cerved sees graph databases and extracting additional insights from relationships in data as important to its future.

This document discusses using knowledge architecture to transform data into actionable knowledge. It outlines challenges such as wasted research spending and inadequate information for decisions. The summary defines knowledge architecture as designing intellectual infrastructure combining knowledge management, informatics, and data science. It then shows how the presenter applied these fields by graphing a NASA lesson learned database to find patterns, topics, and correlations to enable more informed decisions.

This webinar will cover how to work with a real world dataset in Neo4j, with a focus on how to build a graph from an existing dataset (in this case a series of JSON files). We will explore how to performantly import the data into Neo4j - both in the case of an initial import and scaling writes for your graph application. We will demonstrate different approaches for data import (neo4j-import, LOAD CSV, and using the official Neo4j drivers), and discuss when it makes to use each import technique. If you've ever asked these questions, then this webinar is for you! - How do I design a property graph model for my domain? - How do I use the official Neo4j drivers? - How can I deal with concurrent writes to Neo4j? - How can I import JSON into Neo4j?

This document contains the agenda for the Neo4j Partner Day event in Amsterdam on March 16th, 2017. The agenda includes sessions on the business potential for graph database partners, real-world Neo4j applications, an overview of the Neo4j partner program, and networking sessions.

Recommendations are at the core of digital transformation in retail today. Whether you’re building features such as product recommendations, promotion recommendations, personalized customer experience, or re-imagining your supply chain to meet customer demands for same day delivery — you’re facing challenges that require the ability to leverage connections from many different data sources, in real-time. There’s no better technology to meet these challenges than a native graphDB technology such as Neo4j.

Dr. Andreas Weber - semantic pdm

The document discusses using Neo4j and graph databases to analyze documents from the Panama Papers investigation. It describes the steps taken by journalists to acquire, classify, and analyze over 3 million documents. Key aspects included developing a data model to represent entities and relationships, extracting metadata and parsing documents to populate the graph. Neo4j was highlighted as a tool to efficiently store and query the connected relational data in the documents. An example graph showing relationships in documents related to Azerbaijan's president was also presented.

This document summarizes a Neo4j partner event. It includes an agenda with sessions on the business potential of Neo4j for system integrators and consultants, the Neo4j partner program, and a case study on using Neo4j to analyze the Panama Papers. There are also sessions on quickly gaining value from Neo4j and on modeling logistics processes with Neo4j.

Presented by Max De Marzi at StampedeCon 2015: Graphs are eating the world – but in what form? Starting off with a primer on Graph Databases, this talk will focus on practical examples of graph applications. We’ll look at multiple use cases like job boards, dating sites, recommendation engines of all kinds, network management, scheduling engines, etc. We'll also see some examples of graph search in action.

The Briefing Room with Radiant Advisors and IBM Live Webcast on February 25, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=53c9b7fa2000f98f5b236747e3602511 The power of Big Data depends heavily upon the context in which it's used, and most organizations are just beginning to figure out where, how and when to leverage it. One key to success is integration with existing information systems, many of which still rely on relational database technologies. Finding ways to blend these two worlds can help companies generate measurable business value in fairly short order. Register for this episode of The Briefing Room to hear Analysts Lindy Ryan and John O'Brien as they explain how the combination of traditional Business Intelligence with Big Data Analytics can provide game-changing results in today's information economy. They'll be briefed by Eric Poulin and Paul Flach of Stream Integration who will share best practices for designing and implementing Big Data solutions. They'll discuss the components of IBM BigInsights, and explain how BigSheets can empower non-technical users who need to explore self-structured data. Visit InsideAnlaysis.com for more information.

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.

A short intro to Web Data Extraction techniques. The talk is part of the HiPEDS CDT Seminar Series at Imperial College

Philip Rathle, VP of Product at Neo4j, presents on the Connected Data Imperative at Neo4j GraphDay NYC

"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."

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

This document outlines an agenda for the Neo4j GraphTalk event in Atlanta on May 3rd 2017. The event will include an introduction to Neo4j and its capabilities for connected data, a presentation on real-world uses of Neo4j in production, and a reception. Neo4j is a native graph database created by Neo Technology to leverage connections in data in real-time to create value for organizations. It is well-suited for applications involving connected data, such as recommendations, fraud detection, and customer analytics.

This document discusses how organizations can leverage big data and artificial intelligence (AI) to drive insights and add intelligence to their solutions. It covers common big data challenges, AWS big data solutions like Amazon S3, Glue, Athena, Redshift, Kinesis, and SageMaker, and how big data can power machine learning. Some key tenets for building big data architectures are using the right tools, leveraging managed services, adopting event-driven design patterns, and enabling ML applications.

The document outlines an agenda for a Neo4j Graph Day event including sessions on connected data, graphs and artificial intelligence, a lunch break, Neo4j training, and a reception. Key topics include Neo4j in production environments, its role in boosting artificial intelligence, and training opportunities.

The document discusses how graph databases and graph technologies can be used for business intelligence, analytics, and decision making. It provides examples of how companies in various industries like communications, logistics, online recruiting, and consumer web have used graph databases from Neo4j to power applications, gain insights, and improve user experiences. Specific use cases discussed include network management, parcel routing, social job search, recommendations, and interactive television programming. The benefits of the graph model over relational databases for complex connected data are also highlighted.

This document discusses Elasticsearch and how it can be used to search, analyze, and make sense of large amounts of data. It provides examples of how Elasticsearch is being used by large companies to handle petabytes of data and gain insights. Implementations in France are highlighted. The document concludes by demonstrating how easily Elasticsearch can be deployed and used to ingest and search sample data.

The document discusses new features and capabilities in Neo4j 4.0, including unlimited scalability through sharding and federation, a fully reactive architecture, and new security and data privacy controls. It also introduces Neo4j Desktop for graph development workflows, Neo4j Aura cloud database service, and visualization and analytics tools for working with graph data.

Clusterpoint is a privately held database software company founded in 2006 with 32 employees. Their product is a hybrid operational database, analytics, and search platform that provides secure, high-performance distributed data management at scale. It reduces total cost of ownership by 80% over traditional relational databases by providing blazing fast performance, unlimited scalability, and bulletproof transactions with instant text search and security. Clusterpoint also offers their database software as a cloud database as a service to instantly scale databases on demand.

This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.

(1) Amundsen is a data discovery platform developed by Lyft to help users find, understand, and use data. (2) The platform addresses challenges around data discovery such as lack of understanding about what data exists and where to find it. (3) Amundsen provides searchable metadata about data resources, previews of data, and usage statistics to help data scientists and others explore and understand data.

The document discusses graph databases and Neo4j. It provides examples of industries using graph databases and discusses Neo4j's performance advantages over MySQL for graph-oriented queries on social network data. Upcoming versions of Neo4j aim to improve ease of use and support larger datasets. The remainder of the document advertises an upcoming Neo4j user conference.

Slides from the Big Data presentation by Bradley Holt, Laura Thomson, Julie Steele, Eli White, Dennis Yang, and David Zülke

Lyft developed Amundsen, an internal metadata and data discovery platform, to help their data scientists and engineers find data more efficiently. Amundsen provides search-based and lineage-based discovery of Lyft's data resources. It uses a graph database and Elasticsearch to index metadata from various sources. While initially built using a pull model with crawlers, Amundsen is moving toward a push model where systems publish metadata to a message queue. The tool has increased data team productivity by over 30% and will soon be open sourced for other organizations to use.

As organizations modernize their data and analytics platforms, the data lake concept has gained momentum as a shared enterprise resource for supporting insights across multiple lines of business. The perception is that data lakes are vast, slow-moving bodies of data, but innovations like Apache Kafka for streaming-first architectures put real-time data flows at the forefront. Combining real-time alerts and fast-moving data with rich historical analysis lets you respond quickly to changing business conditions with powerful data lake analytics to make smarter decisions. Join this complimentary webinar with industry experts from 451 Research and Arcadia Data who will discuss: - Business requirements for combining real-time streaming and ad hoc visual analytics. - Innovations in real-time analytics using tools like Confluent’s KSQL. - Machine-assisted visualization to guide business analysts to faster insights. - Elevating user concurrency and analytic performance on data lakes. - Applications in cybersecurity, regulatory compliance, and predictive maintenance on manufacturing equipment all benefit from streaming visualizations.

Presented at Gartner Data & Analytics, London Maty 2024. 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. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.

Gursev Pirge, PhD Senior Data Scientist - JohnSnowLabs

Tomaz Bratanic Graph ML and GenAI Expert - Neo4j

Katja Glaß OpenStudyBuilder Community Manager - Katja Glaß Consulting Marius Conjeaud Principal Consultant - Neo4j

Dmitrii Kamaev, PhD Senior Product Owner - QIAGEN

Atelier - Architecture d’applications de Graphes Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.

Atelier - Innover avec l’IA Générative et les graphes de connaissances Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement. Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.

Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.

Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.

Thibault Gossé, Chief Data & AI Officer, FLOA Martin Schubenel, Fraud Project Manager, FLOA

Romain CAMPOURCY – Architecte Solution, Sopra Steria Patrick MEYER – Architecte IA Groupe, Sopra Steria La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.

Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin) Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.

Sudhir Hasbe, Chief Product Officer, Neo4j Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.

Dr. Sean Tan, Head of Data Science, Changi Airport Group Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.

Leonard Jayamohan, Partner & Generative AI Lead, Deloitte This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.

Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.

Neha Bajwa, Vice President of Product Marketing, Neo4j Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.

Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.

Petr Matuska, Sales & Sales Engineering Lead, GraphAware Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.

David Pond, Lead Product Manager, Neo4j Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.

Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk. What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year? Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year. This webinar will review: - Key changes to privacy regulations in 2024 - Key themes in privacy and data governance in 2024 - How to maximize your privacy program in the second half of 2024

Stream processing is a crucial component of modern data infrastructure, but constructing an efficient and scalable stream processing system can be challenging. Decoupling compute and storage architecture has emerged as an effective solution to these challenges, but it can introduce high latency issues, especially when dealing with complex continuous queries that necessitate managing extra-large internal states. In this talk, we focus on addressing the high latency issues associated with S3 storage in stream processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of S3 latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing. Throughout the talk, we will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of S3 latency on stream processing. By the end of the talk, attendees will have gained insights into how to optimize their stream processing systems for reduced latency and improved cost-efficiency.

Recent advancements in the NIST-JARVIS infrastructure: JARVIS-Overview, JARVIS-DFT, AtomGPT, ALIGNN, JARVIS-Leaderboard

This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization. Key Takeaways: * Understand why connection pooling is essential for high-traffic applications * Explore various connection poolers available for PostgreSQL, including pgbouncer * Learn the configuration options and functionalities of pgbouncer * Discover best practices for monitoring and troubleshooting connection pooling setups * Gain insights into real-world use cases and considerations for production environments This presentation is ideal for: * Database administrators (DBAs) * Developers working with PostgreSQL * DevOps engineers * Anyone interested in optimizing PostgreSQL performance Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services

MuleSoft Meetup on APM and IDP

Solar Storms (Geo Magnetic Storms) are the motion of accelerated charged particles in the solar environment with high velocities due to the coronal mass ejection (CME).

Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.

Not so much to say

accommodate the strengths, weaknesses, threats and opportunities of autonomous vehicles

Java Servlet programs

Invited Remote Lecture to SC21 The International Conference for High Performance Computing, Networking, Storage, and Analysis St. Louis, Missouri November 18, 2021

The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries: 1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes. 2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions. 3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines. 4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors. 5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering. 6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands. 7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems. 8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering. 9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively. Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.

An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)

YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well. Some facts about WPRiders and why we are one of the best firms around: More than 700 five-star reviews! You can check them here. 1500 WordPress projects delivered. We respond 80% faster than other firms! Data provided by Freshdesk. We’ve been in business since 2015. We are located in 7 countries and have 22 team members. With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce. Our team members are: - highly experienced developers (employees & contractors with 5 -10+ years of experience), - great designers with an eye for UX/UI with 10+ years of experience - project managers with development background who speak both tech and non-tech - QA specialists - Conversion Rate Optimisation - CRO experts They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals. At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.

How do we build an IoT product, and make it profitable? Talk from the IoT meetup in March 2024. https://www.meetup.com/iot-sweden/events/299487375/

The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.

This is a slide deck that showcases the updates in Microsoft Copilot for May 2024

Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality. Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality. Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality. Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank? ** Episode Overview ** In this first episode of our quality series, Kristen Hansen and the panel discuss: ⦿ What do we mean when we say patent quality? ⦿ Why is patent quality important? ⦿ How to balance quality and budget ⦿ The importance of searching, continuations, and draftsperson domain expertise ⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications https://www.aurorapatents.com/patently-strategic-podcast.html

Password Rotation in 2024 is still Relevant

This is a powerpoint that features Microsoft Teams Devices and everything that is new including updates to its software and devices for May 2024