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.
Graph All the Things: An Introduction to Graph DatabasesNeo4j
The document discusses graph databases and their use cases. It provides an overview of Neo Technology, the creator of Neo4j, the world's leading graph database. It describes when graph databases are useful and how they model relationships between data differently than traditional databases. Examples are given of how graph databases can be used for recommendations, fraud detection, supply chain management, and powering the Internet of Things.
The document discusses a presentation about connecting data and Neo4j. It covers data ecosystems and where different technologies fit, how Neo4j works as a graph database, and building graph-native organizations. It also discusses Neo4j's long term vision of connecting enterprise data and the state of data in 2018. Key points include how data structures have evolved from hierarchies to dynamic knowledge graphs and how different technologies like relational databases and Neo4j are suited for different types of queries and connected data problems.
The year of the graph: do you really need a graph database? How do you choose...George Anadiotis
Graph databases have been around for more than 15 years, but it was AWS and Microsoft getting in the domain that attracted widespread interest. If they are into this, there must be a reason.
Everyone wants to know more, few can really keep up and provide answers. And as this hitherto niche domain is in the mainstream now, the dynamics are changing dramatically. Besides new entries, existing players keep evolving.
I’ve done the hard work of evaluating solutions, so you don’t have to. An overview of the domain and selection methodology, as presented in Big Data Spain 2018
Bigdata and ai in p2 p industry: Knowledge graph and inferencesfbiganalytics
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.
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
Challenges in the Design of a Graph Database Benchmark graphdevroom
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.
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.
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.
Family tree of data – provenance and neo4jM. David Allen
The document discusses using Neo4j, a graph database, to store and query provenance data. Some key points:
- Storing provenance in a relational database requires complex SQL and pushes graph operations into code, hurting performance on graph queries.
- Neo4j uses the Cypher query language which allows declarative graph queries without imperative code.
- Example Cypher queries are provided to demonstrate retrieving paths and relationships in a provenance graph.
- While graph databases provide better performance for graph queries, they have limitations for certain bulk scans compared to relational databases. Proper graph design is important.
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.
An Introduction to NOSQL, Graph Databases and Neo4jDebanjan Mahata
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.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
How Graph Databases efficiently store, manage and query connected data at s...jexp
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.
Airbnb aims to democratize data within the company by building a graph database of all internal data resources connected by relationships. This graph is queried through a search interface to help employees explore, discover, and build trust in company data. Challenges include modeling complex data dependencies and proxy nodes, merging graph updates from different sources, and designing a data-dense interface simply. Future goals are to gamify content production, deliver recommendations, certify trusted content, and analyze the information network.
The document introduces Neo4j, a graph database, and discusses its applications like social networks and fraud detection. It explains the labeled property graph model and shows examples of how it can represent relationships between people, locations, and objects. The document also lists several industries and use cases where Neo4j has helped companies connect disparate data and enable new insights through its graph capabilities.
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges.
این دیتابیس توسط Neo Technology در سال ۲۰۰۷ ایجاد شد و به صورت Opensource در اختیار کاربران قرار گرفت. آخرین نسخه Stable، ورژن ۳.۱ هست.
Getting started with Graph Databases & Neo4jSuroor Wijdan
The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.
The document provides an outline for a presentation on graph-based data models. It introduces some key concepts about graphs and how they are used to model real-world interconnected data. It discusses how early adopters of graph technologies grew by focusing on data relationships. The document also covers graph data structures, graph databases, and graph query languages like Cypher and Gremlin.
Relational databases power most applications, but new use-cases have requirements that they are not well suited for.
That's why new approaches like graph databases are used to handle join-heavy, highly-connected and realtime aspects of your applications.
This talk compares relational and graph databases, show similarities and important differences.
We do a hands-on, deep-dive into ease of data modeling and structural evolution, massive data import and high performance querying with Neo4j, the most popular graph database.
I demonstrate a useful tool which makes data import from existing relational databases with a non-denormalized ER-model a "one click"-experience.
Which leaves biggest challenge for people coming from a relational background is to adapt some of their existing database experience to new ways of thinking.
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.
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 announces a Neo4j GraphTalks event in May 2016. It provides an agenda including presentations on graph databases and Neo4j, a case study from ADAMA on data sharing and knowledge management, and a demonstration of ADAMA's implementation experience. Time is allotted after the presentations for networking and discussions with Neo4j and PRODYNA representatives.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
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.
Creating Open Data with Open Source (beta2)Sammy Fung
The document discusses creating open data using open source tools. It provides an overview of open data and Tim Berners-Lee's 5 star deployment scheme for open data. The author then describes using Python and the Scrapy framework to crawl websites and extract structured data to create open datasets. Specific examples discussed are the WeatherHK and TCTrack projects, which extract weather data from government websites. The author also proposes the hk0weather open source project to convert Hong Kong weather data into JSON format. The goal is to make more government data openly available in reusable, machine-readable formats.
This document summarizes an introductory webinar on building an enterprise knowledge graph from RDF data using TigerGraph. It introduces RDF and knowledge graphs, demonstrates loading DBpedia data into a TigerGraph graph database using a universal schema, and provides examples of queries to extract information from the graph such as related people, publishers by location, and related topics for a given predicate. The webinar encourages attendees to learn more about graph databases and TigerGraph through additional resources and future webinar episodes.
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.
Family tree of data – provenance and neo4jM. David Allen
The document discusses using Neo4j, a graph database, to store and query provenance data. Some key points:
- Storing provenance in a relational database requires complex SQL and pushes graph operations into code, hurting performance on graph queries.
- Neo4j uses the Cypher query language which allows declarative graph queries without imperative code.
- Example Cypher queries are provided to demonstrate retrieving paths and relationships in a provenance graph.
- While graph databases provide better performance for graph queries, they have limitations for certain bulk scans compared to relational databases. Proper graph design is important.
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.
An Introduction to NOSQL, Graph Databases and Neo4jDebanjan Mahata
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.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
How Graph Databases efficiently store, manage and query connected data at s...jexp
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.
Airbnb aims to democratize data within the company by building a graph database of all internal data resources connected by relationships. This graph is queried through a search interface to help employees explore, discover, and build trust in company data. Challenges include modeling complex data dependencies and proxy nodes, merging graph updates from different sources, and designing a data-dense interface simply. Future goals are to gamify content production, deliver recommendations, certify trusted content, and analyze the information network.
The document introduces Neo4j, a graph database, and discusses its applications like social networks and fraud detection. It explains the labeled property graph model and shows examples of how it can represent relationships between people, locations, and objects. The document also lists several industries and use cases where Neo4j has helped companies connect disparate data and enable new insights through its graph capabilities.
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges.
این دیتابیس توسط Neo Technology در سال ۲۰۰۷ ایجاد شد و به صورت Opensource در اختیار کاربران قرار گرفت. آخرین نسخه Stable، ورژن ۳.۱ هست.
Getting started with Graph Databases & Neo4jSuroor Wijdan
The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.
The document provides an outline for a presentation on graph-based data models. It introduces some key concepts about graphs and how they are used to model real-world interconnected data. It discusses how early adopters of graph technologies grew by focusing on data relationships. The document also covers graph data structures, graph databases, and graph query languages like Cypher and Gremlin.
Relational databases power most applications, but new use-cases have requirements that they are not well suited for.
That's why new approaches like graph databases are used to handle join-heavy, highly-connected and realtime aspects of your applications.
This talk compares relational and graph databases, show similarities and important differences.
We do a hands-on, deep-dive into ease of data modeling and structural evolution, massive data import and high performance querying with Neo4j, the most popular graph database.
I demonstrate a useful tool which makes data import from existing relational databases with a non-denormalized ER-model a "one click"-experience.
Which leaves biggest challenge for people coming from a relational background is to adapt some of their existing database experience to new ways of thinking.
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.
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 announces a Neo4j GraphTalks event in May 2016. It provides an agenda including presentations on graph databases and Neo4j, a case study from ADAMA on data sharing and knowledge management, and a demonstration of ADAMA's implementation experience. Time is allotted after the presentations for networking and discussions with Neo4j and PRODYNA representatives.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
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.
Creating Open Data with Open Source (beta2)Sammy Fung
The document discusses creating open data using open source tools. It provides an overview of open data and Tim Berners-Lee's 5 star deployment scheme for open data. The author then describes using Python and the Scrapy framework to crawl websites and extract structured data to create open datasets. Specific examples discussed are the WeatherHK and TCTrack projects, which extract weather data from government websites. The author also proposes the hk0weather open source project to convert Hong Kong weather data into JSON format. The goal is to make more government data openly available in reusable, machine-readable formats.
This document summarizes an introductory webinar on building an enterprise knowledge graph from RDF data using TigerGraph. It introduces RDF and knowledge graphs, demonstrates loading DBpedia data into a TigerGraph graph database using a universal schema, and provides examples of queries to extract information from the graph such as related people, publishers by location, and related topics for a given predicate. The webinar encourages attendees to learn more about graph databases and TigerGraph through additional resources and future webinar episodes.
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014Austin Ogilvie
The document outlines Greg Lamp's presentation at a Data Science MD Meetup in October 2014 about Applied Data Science with Yhat. The presentation covers the challenges of building analytical applications, a case study of a beer recommender system built in Python using beer review data, and a demonstration of deploying the model through Yhat's platform. It concludes with a question and answer section.
Building a Distributed Build System at Google ScaleAysylu Greenberg
It’s hard to imagine a modern developer workflow without a sufficiently advanced build system: Make, Gradle, Maven, Rake, and many others. In this talk, we’ll discuss the evolution of build systems that leads to distributed build systems, like Google's BuildRabbit. Then, we’ll dive into how we can build a scalable system that is fast and resilient, with examples from Google. We’ll conclude with the discussion of general challenges of migrating systems from one architecture to another.
JSON and Oracle Database: A Brave New WorldDaniel McGhan
A world of apps built in JavaScript, using JSON as their data exchange format, relying on APIs to get the job done - does Oracle Database have a place in this world? Can it offer UI developers what they need to get their job done as productively and successfully as possible? Absolutely! In this session, attendees will explore the new support for JSON in Oracle Database SQL and PL/SQL and learn how to help front-end developers build secure, high-performance applications.
Comprehensive Container Based Service Monitoring with Kubernetes and IstioFred Moyer
This document summarizes Fred Moyer's talk on comprehensive container-based service monitoring with Kubernetes and Istio. The talk covered Istio architecture and deployment, using the Istio sample bookinfo application, and monitoring the application with Istio metrics and Grafana dashboards. It also discussed Istio Mixer metrics adapters, math and statistics concepts like histograms and quantiles, and monitoring concepts like service level objectives, indicators, and agreements. The talk provided exercises for attendees to deploy sample applications and create custom metrics adapters.
The document describes the Neo4j graph database and platform vision. It discusses key components like index-free adjacency, ACID transactions, clustering, and hardware optimizations. It outlines use cases for graph analytics, transactions, AI, and data integration. It also covers drivers, APIs, visualization, and administration tools. Finally, it previews upcoming innovations in Neo4j 3.4 like geospatial support, native string indexes, and rolling upgrades.
Creando microservicios con Java, Microprofile y TomEE - Baranquilla JUGCésar Hernández
En esta sesión los asistentes presenciaron la base teórica y práctica para la creación de micro servicios con Java, JakartaEE, MicroProfile utilizando TomEE como servidor de aplicaciones.
Drill can query JSON data stored in various data sources like HDFS, HBase, and Hive. It allows running SQL queries over JSON data without requiring a fixed schema. The document describes how Drill enables ad-hoc querying of JSON-formatted Yelp business review data using SQL, providing insights faster than traditional approaches.
Developing in R - the contextual Multi-Armed Bandit editionRobin van Emden
The document discusses R package development. It covers that R is dominant in statistics research and is an interpreted language. It also supports multiple programming paradigms like imperative, functional and object oriented programming. It discusses different class systems in R like S3, S4 and the newer R6 class. It emphasizes that R6 class provides a better approach. The document also highlights the importance of skills like semantic development skills, syntactic development skills and domain knowledge for R development.
HEPData is a repository for data from high energy physics (HEP) experiments dating back to the 1950s. It provides physicists with access to the underlying data and tables from published papers. The new HEPData system offers simplified submission processes, standard data formats, versioning, and assigning DOIs to help data providers share their work. It also improves access and search capabilities for data consumers through features like publication-driven and data-driven searching, semantic publishing, data conversion tools, and access through analysis environments like ROOT and Mathematica.
The document describes Krist Wongsuphasawat's background and work in data visualization. It notes that he has a PhD in Computer Science from the University of Maryland, where he studied information visualization. He currently works as a data visualization scientist at Twitter, where he builds internal tools to analyze log data and monitor changes over time. Some of his projects include Scribe Radar, which allows users to search through and visualize client event data in order to find patterns and monitor effects of product changes. The document provides details on his approaches for dealing with large log datasets and visualizing user activity sequences.
Data Science Amsterdam - Massively Parallel Processing with Procedural LanguagesIan Huston
The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. With Procedural Languages such as PL/Python and PL/R data parallel queries can be run across terabytes of data using not only pure SQL but also familiar Python and R packages. The Pivotal Data Science team have used this technique to create fraud behaviour models for each individual user in a large corporate network, to understand interception rates at customs checkpoints by accelerating natural language processing of package descriptions and to reduce customer churn by building a sentiment model using customer call centre records.
http://www.meetup.com/Data-Science-Amsterdam/events/178974942/
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...Codemotion
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system using tools like: Web Services,Spark,Cassandra,MongoDB,AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...Demi Ben-Ari
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Fully Tested: From Design to MVP In 3 WeeksSmartBear
In this presentation Daniel Giordano, Product Marketing Manager at SmartBear, will cover how to speed up your development with a design first mind set, virtualizing services and dependencies to enhance collaboration between developers & testers, & end-to-End testing strategies for an immature product.
The document discusses SYSTAP and their graph database product Blazegraph. It provides an overview of SYSTAP and Blazegraph, highlighting that Blazegraph can scale to handle large graph datasets with billions or trillions of edges through various deployment options including embedded, high availability, scale-out, and GPU acceleration configurations. The document also discusses how Blazegraph is being used by organizations for applications like knowledge graphs, genomics, and defense/intelligence.
Massively Parallel Processing with Procedural Python by Ronert Obst PyData Be...PyData
The Python data ecosystem has grown beyond the confines of single machines to embrace scalability. Here we describe one of our approaches to scaling, which is already being used in production systems. The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. Using PL/Python we can run parallel queries across terabytes of data using not only pure SQL but also familiar PyData packages such as scikit-learn and nltk. This approach can also be used with PL/R to make use of a wide variety of R packages. We look at examples on Postgres compatible systems such as the Greenplum Database and on Hadoop through Pivotal HAWQ. We will also introduce MADlib, Pivotal’s open source library for scalable in-database machine learning, which uses Python to glue SQL queries to low level C++ functions and is also usable through the PyMADlib package.
Why and How to integrate Hadoop and NoSQL?Tugdual Grall
This document contains a presentation on integrating Hadoop with NoSQL databases. It discusses using Sqoop to transfer data between Hadoop and NoSQL databases like Couchbase and MongoDB. It provides examples of using Sqoop to import and export data between these systems. The presentation also highlights some key uses cases and benefits of using Hadoop and NoSQL databases together for applications involving large datasets.
Graphs & GraphRAG - Essential Ingredients for GenAINeo4j
Knowledge graphs are emerging as useful and often necessary for bringing Enterprise GenAI projects from PoC into production. They make GenAI more dependable, transparent and secure across a wide variety of use cases. They are also helpful in GenAI application development: providing a human-navigable view of relevant knowledge that can be queried and visualised.
This talk will share up-to-date learnings from the evolving field of knowledge graphs; why more & more organisations are using knowledge graphs to achieve GenAI successes; and practical definitions, tools, and tips for getting started.
Discover how Neo4j-based GraphRAG and Generative AI empower organisations to deliver hyper-personalised customer experiences. Explore how graph-based knowledge empowers deep context understanding, AI-driven insights, and tailored recommendations to transform customer journeys.
Learn actionable strategies for leveraging Neo4j and Generative AI to revolutionise customer engagement and build lasting relationships.
GraphTalk New Zealand - The Art of The Possible.pptxNeo4j
Discover firsthand how organisations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimising supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In this presentation, ANZ will be sharing their journey towards AI-enabled data management at scale. The session will explore how they are modernising their data architecture to support advanced analytics and decision-making. By leveraging a knowledge graph approach, they are enhancing data integration, governance, and discovery, breaking down silos to create a unified view across diverse data sources. This enables AI applications to access and contextualise information efficiently, and drive smarter, data-driven outcomes for the bank. They will also share lessons they are learning and key steps for successfully implementing a scalable, AI-ready data framework.
Google Cloud Presentation GraphSummit Melbourne 2024: Building Generative AI ...Neo4j
GenerativeAI is taking the world by storm while traditional ML maturity and successes continue to accelerate across AuNZ . Learn how Google is working with Neo4J to build a ML foundation for trusted, sustainable, and innovative use cases.
Telstra Presentation GraphSummit Melbourne: Optimising Business Outcomes with...Neo4j
This session will highlight how knowledge graphs can significantly enhance business outcomes by supporting the Data Mesh approach. We’ll discuss how knowledge graphs empower organisations to create and manage data products more effectively, enabling a more agile and adaptive data strategy. By leveraging knowledge graphs, businesses can better organise and connect their data assets, driving innovation and maximising the value derived from their data, ultimately leading to more informed decision-making and improved business performance.
Building Smarter GenAI Apps with Knowledge Graphs
While GenAI offers great potential, it faces challenges with hallucination and limited domain knowledge. Graph-powered retrieval augmented generation (GraphRAG) helps overcome these challenges by integrating vector search with knowledge graphs and data science techniques. This approach improves context, enhances semantic understanding, enables personalisation, and facilitates real-time updates.
In this workshop, you’ll explore detailed code examples to kickstart your journey with GenAI and graphs. You’ll leave with practical skills you can immediately apply to your own projects.
How Siemens bolstered supply chain resilience with graph-powered AI insights ...Neo4j
In this captivating session, Siemens will reveal how Neo4j’s powerful graph database technology uncovers hidden data relationships, helping businesses reach new heights in IT excellence. Just as organizations often face unseen barriers, your business may be missing critical insights buried in your data. Discover how Siemens leverages Neo4j to enhance supply chain resilience, boost sustainability, and unlock the potential of AI-driven insights. This session will demonstrate how to navigate complexity, optimize decision-making, and stay ahead in a constantly evolving market.
Knowledge Graphs for AI-Ready Data and Enterprise Deployment - Gartner IT Sym...Neo4j
Knowledge graphs are emerging as useful and often necessary for bringing Enterprise GenAI projects from PoC into production. They make GenAI more dependable, transparent and secure across a wide variety of use cases. They are also helpful in GenAI application development: providing a human-navigable view of relevant knowledge that can be queried and visualised. This talk will share up-to-date learnings from the evolving field of knowledge graphs; why more & more organisations are using knowledge graphs to achieve GenAI successes; and practical definitions, tools, and tips for getting started.
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc
Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency.
This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB
Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxAnoop Ashok
In today's fast-paced retail environment, efficiency is key. Every minute counts, and every penny matters. One tool that can significantly boost your store's efficiency is a well-executed planogram. These visual merchandising blueprints not only enhance store layouts but also save time and money in the process.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungenpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-und-verwaltung-von-multiuser-umgebungen/
HCL Nomad Web wird als die nächste Generation des HCL Notes-Clients gefeiert und bietet zahlreiche Vorteile, wie die Beseitigung des Bedarfs an Paketierung, Verteilung und Installation. Nomad Web-Client-Updates werden “automatisch” im Hintergrund installiert, was den administrativen Aufwand im Vergleich zu traditionellen HCL Notes-Clients erheblich reduziert. Allerdings stellt die Fehlerbehebung in Nomad Web im Vergleich zum Notes-Client einzigartige Herausforderungen dar.
Begleiten Sie Christoph und Marc, während sie demonstrieren, wie der Fehlerbehebungsprozess in HCL Nomad Web vereinfacht werden kann, um eine reibungslose und effiziente Benutzererfahrung zu gewährleisten.
In diesem Webinar werden wir effektive Strategien zur Diagnose und Lösung häufiger Probleme in HCL Nomad Web untersuchen, einschließlich
- Zugriff auf die Konsole
- Auffinden und Interpretieren von Protokolldateien
- Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS)
- Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien
- Nutzung der Client Clocking-Funktion
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
Hadoop and Graph Databases (Neo4j): Winning Combination for Bioanalytics - Jonathan Freeman @ GraphConnect NY 2013
1. {GraphConnect NYC}
Hadoop and Graph Databases
(Neo4j): Winning Combination for
Bioinformatics
Jonathan Freeman
@freethejazz
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
2. Hadoop + Neo4j = Bioanalytics Win
Open Software Integrators
●
Jonathan Freeman
@freethejazz
Founded January 2008 by Andrew C. Oliver
○ Durham, NC
Revenue and staff has at least doubled every year since
2009.
●
New office (2012) in Chicago, IL
○ We're hiring associate to senior level as well as UI Developers
(JQuery, Javascript, HTML, CSS)
○ Up to 50% travel (probably less), salary + bonus, 401k, health,
etc etc
○ Preferred: Java, Tomcat, JBoss, Hibernate, Spring, RDBMS,
JQuery
○ Nice to have: Hadoop, Neo4j, MongoDB, Ruby a/o at least one
Cloud platform
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
3. Hadoop + Neo4j = Bioinformatics Win
Questions to answer
●
●
●
●
uhh, bioinformatics?
What is Hadoop? Why is it a good fit?
And Neo4j? Why the combination?
I want this now! How do I do it?!?!
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
Jonathan Freeman
@freethejazz
5. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
“
dynamic
information processing
system
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
6. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Life
http://www.labtimes.org/labtimes/issues/lt2011/lt07/lt_2011_07_26_29.pdf
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
7. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
● Storing/Retrieving Biological Data
● Organizing Biological Data
● Analyzing Biological Data
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
8. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Biological Data
● amino acid sequences
● nucleotide sequences
● protein structures
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
9. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
●
●
●
●
●
Genetic sequence analysis
Tracing biological evolution
Analysis of gene expression
Studying mutations in cancer
Predicting protein structure and
function
● Molecular Interaction
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
10. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
●
●
●
●
●
Genetic sequence analysis
Tracing biological evolution
Analysis of gene expression
Studying mutations in cancer
Predicting protein structure and
function
● Molecular Interaction
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
11. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Full Human Genome Sequencing Then
13 Years
$2,700,000,000
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
12. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Full Human Genome Sequencing Then
1 Day
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
$5,000
14. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
So what are we
waiting for?
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
25. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Infrastructure for distributed computing
HDFS
MapReduce
A distributed file system.
An implementation of a
programming model for
processing very large data sets.
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
29. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Infrastructure for distributed computing
HDFS
MapReduce
A distributed file system.
An implementation of a
programming model for
processing very large data sets.
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}