New Opportunities for Connected Data - Emil Eifrem @ GraphConnect Boston + Chicago 2013

The document summarizes a presentation given by Dr. Mikio Braun on architecting AI applications. It discusses the history and approaches of artificial intelligence, including classical, machine learning, and deep learning methods. It also provides examples of applying AI to autonomous driving, chatbots, recommendations, games and more. Finally, it outlines common elements of AI applications and design patterns for aspects like core machine learning, serving models, preprocessing data, automation, and integrating machine learning components.

The document discusses how Neo4j can be used to combat money laundering and financial fraud. It introduces the presenters and provides an agenda for the seminar. Additionally, it outlines Neo4j's capabilities for connecting disparate data sources and exposing related information to support enhanced decision making, fraud prevention, and compliance. Neo4j allows users to explore network and transactional data across multiple "anchor points" to discover relationships and patterns that may indicate money laundering or fraud.

View the slides from 'Graphs in Action' by William Lyon on the Neo4j Developer Relations team presented at GraphTalk Denver.

View the slides from 'The Connected Data Imperative' by Jeff Morris, Director of Product Marketing at Neo4j at GraphTalk Denver.

This document discusses top use cases for graph databases. It begins by outlining an agenda to discuss select case studies. Several case studies are then presented involving using graph databases for retail product recommendations, telecommunications network analysis, and managing real estate listings. Common drivers for adopting graph databases are also listed as creating new products/services, improving existing processes, and improving flexibility. Finally, core industries and use cases for graph technologies are charted.

This document provides an overview and agenda for a presentation on using graph databases like Neo4j for retail applications. The presentation covers introducing graph databases and Neo4j, discussing retail data types, and demonstrating use cases for customer 360 views, recommendations, supply chain management, and other areas. Case studies are presented on using Neo4j for real-time recommendations at a large retailer and real-time promotions at a top US retailer. The document concludes with an invitation for questions.

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.

Graphs are commonly used for (1) master data management to support complex non-hierarchical relationships between entities, (2) network and IT operations management to analyze dependencies in real-time across large connected systems, and (3) fraud detection by connecting related entities to uncover organized fraud rings. Example use cases include an insurer improving access to customer data, a social network powering recommendations by connecting users and interests, and a telecom enabling real-time authentication by modeling identity and access permissions as a graph.

The document discusses digital twins, which are virtual representations of physical objects or processes. It provides background on the origins of digital twins, noting the term was coined in 2002 but the concept owes to decades of modeling work. It then discusses the current state of digital twins, including applications in large software systems, power generation, and transportation. However, it notes implementation has been challenging to scale up beyond simple cases like jet turbines. The document proposes knowledge graphs as a better data structure for mastering complex, connected domains like digital twins due to their ability to represent relationships. It provides an example of using a knowledge graph as a digital twin for IT infrastructure. In conclusion, it discusses several vertical opportunities for digital twins in areas like asset tracking and

Noel Yuhanna, VP, Principal Analyst, Forrester Mary Barton, Consultant, Forrester Blaise James, Analyst Relations, Neo4j

This document discusses an index for tracking companies involved in 5G technology. It describes the index's semi-annual rebalancing process and criteria for selecting companies, including a minimum market capitalization and liquidity, membership or participation in 5G standards organizations, and scoring based on 5G patents, consortium involvement, and financial metrics. The index is aimed at maximizing returns from dividend payments by reinvesting dividends.

The document discusses how graph databases can help governments address challenges like fraud detection, cybersecurity, and intelligence analysis. It provides examples of how Neo4j has helped organizations like Lockheed Martin, the US Army, and NASA optimize processes and save time and money by integrating diverse data sources and analyzing relationships within the data. The document promotes Neo4j's graph data platform for its flexibility, performance, and ability to handle large, interconnected datasets in real-time.

Data Con LA 2020 Description It’s no secret that the roots of Data Science date back to the 1960’s and were first mainstreamed in the 1990’s with the emergence of Data Mining. This occurred when commercially affordable computers started offering the horsepower and storage necessary to perform advanced statistics to scale. However, the words “to scale” have evolved over time. The leap to “Big Data” is only one serial aspect of growth. Beyond the typical 1-off studies that catalyzed the field of Data Mining, Data Science now fulfills enterprise and multi-enterprise use cases spanning much broader and deeper data sets and integrations. For example, AI and Machine Learning frameworks can interoperate with a variety of other systems to drive alerting, feedback loops, predictive frameworks, prescriptive engines, continual learning, and more. The deployment of AI/ML processes themselves often involves integration with contemporary DevOps tools. Now segue to SEAL – the Scalable Enterprise Analytic Lifecycle. In this presentation, you’ll learn how to cover the major bases of a modern Data Science projects – and Citizen Data Science as well – from conception, learning, and evaluation through integration, implementation, monitoring, and continual improvement. And as the name implies, your deployments will be performant and scale as expected in today’s environments. Speaker Jeff Bertman, CTO, Dfuse Technologies

Even if you have terabytes of business data, it might not be easy to apply AI-based analytics to it. The bottleneck is often Machine Learning (ML) expertise and scalable infrastructure. We'll first look at how you can access vast amounts of data from the data warehouse directly in a Google Sheet. Then, you'll see how it's possible to train custom ML models with that data, without ever leaving the spreadsheet. Speaker: Karl Weinmeister Google Cloud AI Advocacy Manager

Date: 16th November 2017 Location: Keynote Theatre Time: 15:10 - 15:40 Speaker: Adam Grzywaczewski Organisation: NVIDIA

Infopulse offers full-cycle mobile application development services since 2008. We develop any types of mobile apps for handheld devices and wearables regardless of the platform. Let's talk: http://bit.ly/2xKWtz1

This document provides an overview of the graph database Neo4j. It discusses that Neo4j is a graph database with nodes, relationships, and properties that is well-suited for complex, highly connected data. Examples are given demonstrating how Neo4j can be used for network management in telecommunications companies and content management, access control, and collaboration at Adobe. Cypher, the query language for Neo4j, is also introduced.

Watch here: https://bit.ly/3i2iJbu You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen. Join us for an exciting session that will cover: - The most interesting trends in data management. - Our predictions on how those trends will change the data management world. - How these trends are shaping the future of data virtualization and our own software.

Do you know how graph technology is used in today’s data-driven applications? We’ll get you up to speed and introduce you to the Neo4j product portfolio.

The document introduces Cubitic, a startup providing a predictive analytics platform for IoT applications. It summarizes the founders' backgrounds and experience. Jaco Els is the CEO with a degree in IT and experience at major companies. Ryan Topping is the Chief Scientist with degrees in mathematics and bioinformatics. Renjith Nair is the CTO with a master's degree in networking and experience developing scalable systems. The founders met working at King and saw an opportunity to build their own predictive analytics solution for IoT, launching initial prototypes in 2015.

Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet. Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva. Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”. Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt. På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query. Under eventet berördes följande punkter: - Big Data och Business Intelligence (BI) - “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång - Google Cloud Platform och hur man genomför en framgångsrik molnsatsning Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.

In this presentation we talked about how macnica.ai is preparing to provide AI as solutions to Japanese enterprises and business.

Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.

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.

The document discusses how telecommunications companies can leverage graph databases to derive value from five key "graphs": the network graph, customer graph, call graph, master data graph, and help desk graph. It provides examples of how companies are using graph databases to improve network management, customer analytics, and other use cases. Finally, it outlines the benefits that have driven telecommunications firms to adopt graph databases, including improved query performance, agile development, and an extensible data model.

The document discusses how telecommunications companies can leverage graph databases to derive value from five key "graphs": the network graph, customer graph, call graph, master data graph, and help desk graph. It provides examples of how companies are using graph databases to improve network management, customer analytics, and other tasks. Reasons for adopting graph databases include faster querying of connected data, better matching of the data model to business domains, and improved maintainability. The presentation encourages attendees to connect at upcoming GraphConnect conferences to learn more.

The document discusses Pivotal's platform and strategy. It notes that Pivotal's platform allows for agile application development, access to big data solutions, and infrastructure flexibility. Examples are given of how companies like GE have used Pivotal's technologies to innovate faster using data and applications. The document promotes Pivotal's platform as uniquely positioned to help enterprises modernize their use of applications, data, and analytics.

The document discusses how graph databases like Neo4j can enable real-time analytics at massive scale by leveraging relationships in data. It notes that data is growing exponentially but traditional databases can't efficiently analyze relationships. Neo4j natively stores and queries relationships to allow analytics 1000x faster. The document advocates that graphs will form the foundation of modern data and analytics by enhancing machine learning models and enabling outcomes like building intelligent applications faster, gaining deeper insights, and scaling limitlessly without compromising data.

The Briefing Room with Dean Abbott and Tableau Software Live Webcast July 23, 2013 http://www.insideanalysis.com Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets. Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.

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

The idea was to predict the customer experience, and their perception of the O2 network at both the user and area levels to drive the network and marketing investments. Here is why and how we got there. In order to measure and predict customer network experience, O2 needed a streaming big data solution which would consume billions of events coming in from the network, in real-time, to measure the performance of the network as experienced by the customer. It was important to build a platform to gather all the relevant data; to co-relate that with the customer satisfaction index (CSI) surveys to understand the relationship of metrics to score. We applied machine learning methods to predict the CSI for all users on the network. Customer insights from the network helped us to build customer segmentations which are shaping various marketing and digital propositions at O2. - The overall solution was based on a hybrid architecture, where Open Source technologies were brought together with Tableau visualization which enabled O2 to keep the maintenance cost down to a minimum. - In order to have quick ROI, the solution was built as the prototype which continued to evolve and now currently handles 30 billion transactions a day, continuously streaming into the platform, and predicting customer experience for 35m+ users. The O2 solution continued to expand every year to accommodate multi-fold growth in traffic, and to accommodate additional features. The decision to move from a community edition Hadoop to the Hortonworks-based platform enabled us to have a supported, faster, and more reliable service. The migration to Hortonworks was completed in October 2018 which has given us the reliable platform to expand the analytics use cases across the wider O2 businesses.

This document discusses the key factors that contributed to the recent boom in deep learning. It identifies better neural network algorithms/techniques, large datasets, massive parallelization using GPUs, and industry investment as major enabling factors. In particular, it highlights how the availability of large, labeled datasets like ImageNet; developments in CNNs, autoencoders, and other neural network architectures; the use of GPUs to enable efficient parallel training; and large-scale research at tech companies like Google were central to recent advances in deep learning.

This document contains an agenda and presentation slides for a Neo4j Graphs in Government event. The presentation introduces graph databases and Neo4j, discusses how graphs can help solve network-oriented problems, provides examples of graph use cases in various industries, and highlights new features in Neo4j 4.0 like easy management, unlimited scaling, and granular security. Case studies demonstrate how Neo4j has helped organizations like the US Army, MITRE, Adobe, and the German Center for Diabetes Research tackle complex data challenges.

A whitepaper about how the evolving data engineering profession helps data-driven companies work smarter and lower cloud costs with Qubole. https://www.qubole.com/resources/white-papers/the-evolving-role-of-the-data-engineer

Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility. In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover: • DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns; • Where Data Fabric fits into your architecture; • How different patterns can work together to maximize agility; and • How a DataOps platform serves as the foundational superstructure for your agile architecture.

Watch full webinar here: https://bit.ly/3H4vrlD Data as a strategic imperative for any company to compete, New common self-service data experience required for all things intelligent, Modern data platform focused on producing data products, Data platform, product, people, process key solution ingredients and Denodo is the future and time is now to get started.

This document provides instructions for using a presentation deck on Cloud Pak for Data. It instructs the user to: 1. Delete the first slide before using the deck. 2. Customize the presentation for the intended audience as the deck covers various topics and using all slides may not fit a single meeting. 3. The deck contains 6 embedded video records for a demo that takes 15-25 minutes to present. Guidance on pitching the demo is available. The appendix contains slides on Cloud Pak for Data licensing and IBM's overall strategy.

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