This document provides an overview of graph databases. It discusses how graph data is naturally represented as nodes connected by edges, unlike relational databases which require joins. Graph databases allow for fast traversal of connected data and enable querying connected subgraphs. Popular graph database models include property graphs and RDF triple stores. Neo4j is introduced as a widely used graph database management system that uses labels, properties, relationships, and Cypher query language.
Graph databases are well-suited for storing and querying multi-relational data. They provide better performance, flexibility, and agility than relational databases for such data. Tests showed graph databases like Neo4j outperforming relational databases by returning results faster and for more records as depth and complexity of queries increased. Cypher is the query language for Neo4j that allows starting queries, matching patterns, returning and filtering results through clauses like START, MATCH, RETURN, and WHERE. Graph databases are used successfully by many large companies needing to handle complex relationships in data.
Graph databases are a type of NoSQL database that is optimized for storing and querying connected data and relationships. A graph database represents data in graphs consisting of nodes and edges, where the nodes represent entities and the edges represent relationships between the entities. Graph databases are well-suited for applications that involve complex relationships and connected data, such as social networks, knowledge graphs, and recommendation systems. They allow for flexible querying of relationships and connections via graph traversal operations.
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 native 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.
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.
Graph databases use graph structures to represent and store data, with nodes connected by edges. They are well-suited for interconnected data. Unlike relational databases, graph databases allow for flexible schemas and querying of relationships. Common uses of graph databases include social networks, knowledge graphs, and recommender systems.
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.
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.
Graph databases are well-suited for storing and querying multi-relational data. They provide better performance, flexibility, and agility than relational databases for such data. Tests showed graph databases like Neo4j outperforming relational databases by returning results faster and for more records as depth and complexity of queries increased. Cypher is the query language for Neo4j that allows starting queries, matching patterns, returning and filtering results through clauses like START, MATCH, RETURN, and WHERE. Graph databases are used successfully by many large companies needing to handle complex relationships in data.
Graph databases are a type of NoSQL database that is optimized for storing and querying connected data and relationships. A graph database represents data in graphs consisting of nodes and edges, where the nodes represent entities and the edges represent relationships between the entities. Graph databases are well-suited for applications that involve complex relationships and connected data, such as social networks, knowledge graphs, and recommendation systems. They allow for flexible querying of relationships and connections via graph traversal operations.
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 native 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.
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.
Graph databases use graph structures to represent and store data, with nodes connected by edges. They are well-suited for interconnected data. Unlike relational databases, graph databases allow for flexible schemas and querying of relationships. Common uses of graph databases include social networks, knowledge graphs, and recommender systems.
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.
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.
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
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.
This document discusses graph databases and introduces DataStax Enterprise Graph. It defines a graph database as one that prioritizes relationships between entities over the entities themselves. It provides examples of problems well-suited for graph databases, such as customer 360 views, recommendations, and fraud detection. The document contrasts graph and relational databases, noting graphs are better for highly connected data. It then introduces DataStax Enterprise Graph as a native graph implementation built on Apache TinkerPop and integrated with Cassandra for scale-out performance and DSE's enterprise features.
This document provides an overview of NoSQL databases and compares them to relational databases. It discusses the different types of NoSQL databases including key-value stores, document databases, wide column stores, and graph databases. It also covers some common concepts like eventual consistency, CAP theorem, and MapReduce. While NoSQL databases provide better scalability for massive datasets, relational databases offer more mature tools and strong consistency models.
Graph databases store data in graph structures with nodes, edges, and properties. Neo4j is a popular open-source graph database that uses a property graph model. It has a core API for programmatic access, indexes for fast lookups, and Cypher for graph querying. Neo4j provides high availability through master-slave replication and scales horizontally by sharding graphs across instances through techniques like cache sharding and domain-specific sharding.
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.
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.
Introduction to Graph database, using K-pop as a database modelling case. From the idea of graph database, Neo4j installation, modelling, Cypher to business application.
Introduction to Neo4j for the Emirates & BahrainNeo4j
This document provides an agenda and overview of a Neo4j presentation. It discusses Neo4j as the leading native graph database, its graph data science capabilities, and deployment options like Neo4j Aura and Cloud Managed Services. Success stories are highlighted like Minka using Neo4j Aura to power Colombia's new real-time ACH payments system. The presentation aims to demonstrate Neo4j's technology, use cases, and how it can drive business value through connecting data.
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.
Elasticsearch Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!
( ELK Stack Training - https://www.edureka.co/elk-stack-trai... )
This Edureka Elasticsearch Tutorial will help you in understanding the fundamentals of Elasticsearch along with its practical usage and help you in building a strong foundation in ELK Stack. This video helps you to learn following topics:
1. What Is Elasticsearch?
2. Why Elasticsearch?
3. Elasticsearch Advantages
4. Elasticsearch Installation
5. API Conventions
6. Elasticsearch Query DSL
7. Mapping
8. Analysis
9 Modules
Se você quer conhecer mais sobre a tecnologia de grafos, este webinar de introdução é perfeito para começar a explorar o potencial dos relacionamentos entre os dados para o seu negócio.
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
A column-oriented database stores data tables as columns rather than rows. This improves the speed of queries that aggregate data over large numbers of records by only reading the necessary columns from disk. Column databases compress data well and avoid reading unnecessary columns. However, they have slower insert speeds and incremental loads compared to row-oriented databases, which store each row together and are faster for queries needing entire rows.
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
This document provides an overview of graph databases and Neo4j. It defines what a graph is mathematically and in the context of databases. It describes the key components of Neo4j including nodes, relationships, properties, labels, paths, traversals, and indexes. It also discusses the Cypher query language, performance advantages of Neo4j over SQL databases, and basic requirements and licensing options.
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
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.
This document discusses graph databases and introduces DataStax Enterprise Graph. It defines a graph database as one that prioritizes relationships between entities over the entities themselves. It provides examples of problems well-suited for graph databases, such as customer 360 views, recommendations, and fraud detection. The document contrasts graph and relational databases, noting graphs are better for highly connected data. It then introduces DataStax Enterprise Graph as a native graph implementation built on Apache TinkerPop and integrated with Cassandra for scale-out performance and DSE's enterprise features.
This document provides an overview of NoSQL databases and compares them to relational databases. It discusses the different types of NoSQL databases including key-value stores, document databases, wide column stores, and graph databases. It also covers some common concepts like eventual consistency, CAP theorem, and MapReduce. While NoSQL databases provide better scalability for massive datasets, relational databases offer more mature tools and strong consistency models.
Graph databases store data in graph structures with nodes, edges, and properties. Neo4j is a popular open-source graph database that uses a property graph model. It has a core API for programmatic access, indexes for fast lookups, and Cypher for graph querying. Neo4j provides high availability through master-slave replication and scales horizontally by sharding graphs across instances through techniques like cache sharding and domain-specific sharding.
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.
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.
Introduction to Graph database, using K-pop as a database modelling case. From the idea of graph database, Neo4j installation, modelling, Cypher to business application.
Introduction to Neo4j for the Emirates & BahrainNeo4j
This document provides an agenda and overview of a Neo4j presentation. It discusses Neo4j as the leading native graph database, its graph data science capabilities, and deployment options like Neo4j Aura and Cloud Managed Services. Success stories are highlighted like Minka using Neo4j Aura to power Colombia's new real-time ACH payments system. The presentation aims to demonstrate Neo4j's technology, use cases, and how it can drive business value through connecting data.
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.
Elasticsearch Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!
( ELK Stack Training - https://www.edureka.co/elk-stack-trai... )
This Edureka Elasticsearch Tutorial will help you in understanding the fundamentals of Elasticsearch along with its practical usage and help you in building a strong foundation in ELK Stack. This video helps you to learn following topics:
1. What Is Elasticsearch?
2. Why Elasticsearch?
3. Elasticsearch Advantages
4. Elasticsearch Installation
5. API Conventions
6. Elasticsearch Query DSL
7. Mapping
8. Analysis
9 Modules
Se você quer conhecer mais sobre a tecnologia de grafos, este webinar de introdução é perfeito para começar a explorar o potencial dos relacionamentos entre os dados para o seu negócio.
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
A column-oriented database stores data tables as columns rather than rows. This improves the speed of queries that aggregate data over large numbers of records by only reading the necessary columns from disk. Column databases compress data well and avoid reading unnecessary columns. However, they have slower insert speeds and incremental loads compared to row-oriented databases, which store each row together and are faster for queries needing entire rows.
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
This document provides an overview of graph databases and Neo4j. It defines what a graph is mathematically and in the context of databases. It describes the key components of Neo4j including nodes, relationships, properties, labels, paths, traversals, and indexes. It also discusses the Cypher query language, performance advantages of Neo4j over SQL databases, and basic requirements and licensing options.
This document discusses graph databases and the graph database Neo4j. It provides an introduction to graph databases, explaining that they are well-suited for storing relationships and sparse data. It then discusses Neo4j and its Cypher query language. Examples using GraphGists are provided and use cases and resources for getting started with Neo4j are listed.
Graph databases are designed to store and query connected data efficiently. They represent data as nodes connected by edges, allowing for fast traversal and retrieval of related information. OrientDB is an open source graph database written in Java that uses a novel indexing algorithm to provide fast insertion and lookups. It supports ACID transactions, models data natively as a graph, includes an SQL interface, and is lightweight, distributed, and free to use.
Neo4j Morpheus: Interweaving Table and Graph Data with SQL and Cypher in Apac...Databricks
This document discusses property graphs and how they are represented and queried using Morpheus, a graph query engine for Apache Spark.
Morpheus allows querying property graphs using Cypher and represents property graphs using DataFrames, with node and relationship data stored in tables. It integrates with various data sources and supports federated queries across multiple property graphs. The document provides examples of loading property graph data from sources like JSON, SQL databases and Neo4j, creating graph projections, running analytical queries, and recommending businesses based on graph algorithms.
This document discusses building a graph-based RDF store on Apache Cassandra. It first introduces RDF data and triple stores, then discusses challenges in building a scalable triple store on Cassandra. It reviews existing approaches like relational and graph-based models. The methodology builds a prototype RDF store on Cassandra using a graph model. Evaluation benchmarks it against other stores on DBPedia data, showing it outperforms them on more complex queries. Future work could improve scalability with a distributed implementation.
This document discusses different graph query languages such as SQL, SPARQL, and Gremlin and provides examples of querying graph data models that were created from relational databases. It begins by introducing the authors and providing an overview of querying entity relations with different languages. Several examples are then given that demonstrate how to express common graph queries like finding connections between nodes in each language using sample data from GitHub and Northwind databases modeled as graphs.
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
RDF4U: RDF Graph Visualization by Interpreting Linked Data as KnowledgeRathachai Chawuthai
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
The document discusses NoSQL databases and big data frameworks. It defines NoSQL databases as next generation databases that are non-relational, distributed, open-source and horizontally scalable. It describes four main categories of NoSQL databases - document databases, key-value stores, column-oriented databases and graph databases. It also discusses properties of NoSQL databases and provides examples of popular NoSQL databases. The document then discusses big data frameworks like Hadoop and its ecosystem including HDFS, MapReduce, YARN and Hadoop Common. It provides details on how these components work together to process large datasets in a distributed manner.
- Neo4j is a graph database that is well-suited for modeling complex, interconnected domain models that are difficult to represent in a relational database. It uses a graph structure of nodes and relationships rather than tables and rows.
- The Neo4j.rb library provides an object-oriented mapping for representing Neo4j graph data as Ruby objects and relationships. It can act as a "drop in" replacement for ActiveRecord in Rails applications.
- Graph databases like Neo4j are particularly useful for problems involving recommendations, social networks, and other domains that benefit from deep relationship traversals and modeling flexible, evolving schemas.
Mubashar Iqbal presented on PostgreSQL, an open-source object-relational database system. PostgreSQL prioritizes reliability, security, and standards compliance. It supports Linux, Unix, Windows and is programmed through interfaces like C/C++, Java, .NET, PHP and Python. Common uses include ERP, data warehousing, and network tools. Prominent users include Yahoo, Sony, Reddit, and Skype. Key features include ACID compliance, online backup, point-in-time recovery, and SSL encryption.
Neo4j is an open source graph database that uses nodes, relationships, and properties to store and query data. It supports ACID transactions and is high performance. Cypher is Neo4j's query language that allows matching patterns of nodes and relationships. A graph database model uses nodes connected by relationships, unlike a relational database that uses tables and rows.
This document discusses NoSQL databases for .NET developers. It begins with an introduction to NoSQL and why it is gaining popularity. It then covers the main types of NoSQL databases - document stores, key-value stores, graph databases, and object databases - and examples of databases for each type. It also discusses how .NET developers can interface with different NoSQL databases either through native .NET clients or REST APIs. The document concludes by noting that NoSQL is well-suited for cloud databases and provides an example of using AWS SimpleDB from .NET.
MongoDB is a horizontally scalable, schema-free, document-oriented NoSQL database. It stores data in flexible, JSON-like documents, allowing for easy storage and retrieval of data without rigid schemas. MongoDB provides high performance, high availability, and easy scalability. Some key features include embedded documents and arrays to reduce joins, dynamic schemas, replication and failover for availability, and auto-sharding for horizontal scalability.
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
Spark is an Apache cluster computing framework designed for big data processing. It uses RDDs (Resilient Distributed Datasets), which are immutable distributed collections of objects that can be operated on in parallel. RDDs support transformations, which create new RDDs, and actions, which return final results. RDDs are lazily evaluated, meaning operations are not performed until an action requires a result. Caching RDDs in memory improves performance for iterative algorithms. MLlib is Spark's machine learning library, which implements parallel machine learning algorithms like clustering and forests that can operate directly on RDDs.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
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.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
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3. 3/25
Storing Connected Data in a Relational Database
● Relationships do exist in the relational databases, but only as a means of joins and joining tables
● Logically, join crates a Cartesian product of tables
● Operations of relational databases are index-intensive. Retrieval based on an index is fast, but not
with a constant time (most often O(log 2 n))
● Traversal queries require hierarchical joins, which are costly. Deep traversal queries are
infeasible. Execution time increases exponentially with a depth of a join.
● For a given SQL query, RDBMS creates an in-memory graph data structure.
● Often relational database are normalized in order to efficiently organize data in a database.
● Normalization increases number of joins needed to query the database. Denormalization can be a
partial solution.
4. 4/25
Database normalization
● Database normalization is the process of organizing the fields and tables of a relational database to
minimize redundancy.
– Normalization usually involves dividing large tables into smaller (and less redundant) tables and defining
relationships between them.
● Normal forms
– The first normal form (each attribute contains only atomic values)
– The second normal form (each non primary key attribute is dependent on the whole primary key)
– The third normal form (each non primary key attribute is dependent on nothing but the primary key)
● A relational database table is often described as "normalized" if it is in the 3NF
● When a database is intended for OLAP rather than OLTP, it is topically denormalized.
● Denormalization is the process of attempting to optimize the read performance of a database by
adding redundant data or by grouping data
● Examples of denormalization techniques:
– Materialised views
– Star schemas
– OLAP cubes
5. 5/25
Graph Database Highlights
● Graph data stores provide index-free adjacency resulting in a much better performance, if
compared to traditional RDBMS
● Designed predominantly for traversal performance and executing graph algorithms
● Graph database is more natural, direct representation of a domain than RDBMS (no need for
junction tables)
● There is no need for joining tables because the data structure is already “joined” by the edges
that are defined.
● In graph databases denormalization is not needed!
● The interesting thing about graph diagrams is that they tend to contain specific instances of
nodes and relationships, rather than classes or archetypes.
● The main purpose of Graph Databases is analysis and visualization of graphical data.
6. 6/25
Graph Database Models
● The Property Graph Model
– Model is built of nodes and relationships
– Nodes contain key-value properties. Sometimes relationships as well.
– Relationships are named and directed, and always have a start and end node
● Hypergraphs
– Generalization of a graph model.
– A relationship can have any number of nodes at either end of a relationship (many-to-
many relationships)
● Triple stores
– A triple expresses a relationship between two resources.
– The triple is a subject-predicate-object data structure, e.g. Fred likes ice cream
7. 7/25
Triple stores
● The Resource Description Framework (RDF) is a framework for expressing
information about resources.
● Resources can be anything, including documents, people, physical objects, and
abstract concepts.
● RDF is intended for situations in which information on the Web needs to be processed
by applications, rather than being only displayed to people.
● RDF is a building block of the Semantic Web movement.
● RDF is a set of W3C specifications
– SPARQL - SPARQL Protocol and RDF Query Language
● Disadvantages
– Lack of index-free adjacencies. Data is stored in form of triplets which are independent
artifacts. In order to traverse the graph one need to join multiple triplets.
8. 8/25
RDF example
[G. Schreiber, Y. Raimond, RDF 1.1 Primer, W3C, 2014]
In RDF, resources are
described by IRI - International
Resource Identifier
RDF define logical
relationships. A number of
different serialization formats
exist for writing down RDF
graphs:
● Turtle
● JSON-LD
● RDFa
● RDF/XML
Popular RDF datasets:
● Wikidata
● Dbpedia
● WordNet
● Europeana
● VIAF
9. 9/25
Hypergraphs
[I. Robinson, J. Webber, E. Eifrem, Graph Databases, O’Reilly Media, 2013]
HyperGraphDB
http://www.hypergraphdb.org
Using hypergraphs we lose the ability to add
properties to the individual relationships.
10. 10/25
The Property Graph Model
● The most popular variant of graph model
● Only one-to-one relationships
● The Property Graph Model databases are typically schema-less. There is
no notion of database schema.
● Querying is often done in specification by example way, i.e. by finding
data (nodes and relationships) matching the specified pattern.
● Optimization for traversal
● Popular solutions:
– Neo4j (pure graph DBMS)
– OrientDB (hybrid document and graph DBMS)
11. 11/25
Neo4j
● Written in Java but uses some high-performance features of JVM
● Concepts:
– Nodes (can have zero or more properties)
– Relationships (always have direction and a type; can have zero or more properties)
– Labels for grouping nodes together (a node can have zero or more labels; labels have colors assigned)
● Neo4j is a schema-optional graph database (since 2.0 version). There are two schema elements:
– Indexes - you can create index on a set of properties of nodes with a specific label (Apache Lucene)
– Constraints - constraint (currently only unique) on a property of nodes of a given label (index will be added automatically)
● Two versions/modes:
– Web server with pure RESTful API and rich web GUI
– Embedded Java library
● RESTful API was designed with discoverability in mind. Just start with a GET on the service root (e.g.
http://localhost:7474/db/data) and you will a list of hyperlinks to available resources.
12. 12/25
Cypher Query Language basics
● Cypher is declarative query language based on pattern matching
● Basic SQL syntax structure:
SELECT columns FROM table WHERE conditions
● Basic Cypher syntax structure:
MATCH pattern WHERE conditions RETURN nodes
● Patterns are defined in ASCII art graphs, e.g.:
MATCH x-->y RETURN x
● It is possible to crate data with Cypher as well:
CREATE ({key:"value"})
13. 13/25
Cypher basic examples
●
Create a simple node
create ({name:"Anna"})
● Retrieve all the nodes
match x return x
● Create a labeled node with some properties
create (x:Person {name:"Jan", from: "Poland"})
● Retrieve all the nodes labeled as Person having parameter from: “Poland”
match (y:Person) where y.from = "Poland" return y
● Create a relationship
match x where x.name="Anna"
match (y:Person)
create x-[:knows]->y
14. 14/25
Traversal queries
● Find Jan's friends. Return him and his friends.
MATCH (x:Person)-[:knows]-(friends)
WHERE x.name = "Jan"
RETURN x, friends
● Find friends of Jan's friends who likes surfing
MATCH (x:Person)-[:knows]-()-[:knows]-(surfer)
WHERE x.name = "Jan"
AND surfer.hobby = "surfing"
RETURN DISTINCT surfer
15. 15/25
Starting points
● Patterns often have starting points, i.e. nodes or relationships that are
explicitly given.
● It is possible to specify the starting point using WHERE clause (as in the
previous slide), but it can be inefficient (when there are no indices).
● More proper way of specifying the starting point (node or relationship) is by
using the START keyword.
● These starting points are obtained via index lookups or, more rarely,
accessed directly based on node or relationship IDs
– START n=node:index-name(key = "value")
– START n=node(id)
16. 16/25
START clause example
Find the mutual friends of user named “Michael”
[I. Robinson, J. Webber, E. Eifrem, Graph Databases, O’Reilly Media, 2013]
START a=node:user(name='Michael')
MATCH (c)-[:KNOWS]->(b)-[:KNOWS]->(a), (c)-[:KNOWS]->(a)
RETURN b, c
18. 18/25
Transaction management
● Neo4j provide full ACID support
● All relationships must have a valid start node and end node. In
effect this means that trying to delete a node that still has
relationships attached to, it will throw an exception upon commit.
● When updating or inserting massive amounts of data then periodic
commit query hint (USING PERIODIC COMMIT) can be helpful.
● Currently only one isolation level (READ_COMMITTED) is supported.
● In order to execute a query inside a transaction, POST the query to
http://localhost:7474/db/data/transaction/{id}
19. 19/25
Native Graph Storage
There are separate stores for nodes, relationships and properties. In order to be able to compute a
record’s location at cost O(1), all stores are fixed-size record stores.
Nodes (9 bytes)
Relationships are stored in doubly linked lists, so firstPrevRelId, firstNextRelId, secondPrevRelId and
secondNextRelId are pointers for the next and previous relationship records for the start and end nodes
[I. Robinson, J. Webber, E. Eifrem, Graph Databases, O’Reilly Media, 2013]
20. 20/25
Scalability
●
On a single server, Neo4j is capable of managing 34*109
nodes
●
Currently, only full DB replication for read-only purposes, is available
– Master-slave architecture to support fault-tolerancy
– Horizontally scaling for read-mostly purposes
● Open transactions are not shared among members of an HA cluster. Therefore, if you use this
endpoint in an HA cluster, you must ensure that all requests for a given transaction are sent to the
same Neo4j instance.
● As was stated, in the graph database data are already “joined”, so it is hard to partition (to shard) a
graph into multiple machine.
● Neo4j team is working on this, but it is not ready yet. It would be desired to keep nodes tightly
connected (or belonging to a common domain) together on the same machine and loosely
connected (or belonging to different domains) on separate machines.
● The problem is that the connection that is currently loose, can one day in the future, become tight,
and vice-versa.
21. 21/25
Graph algorithms
● Both graph theory and graph algorithms are mature and well-understood fields of
computing science and both can can be used to mine sophisticated information
from graph databases.
● Neo4j supports both depth- and breadth-first search
– Search type can be specified using BranchSelector and BranchOrderingPolicy
● Graph Algorithms available in neo4j
– all paths (find all paths between two nodes)
– all simple paths (find paths with no repeated nodes)
– shortest paths (find paths with the fewest relationship)
● Can find all shortest paths (if there are more than one) or just the first one.
– Dijkstra (find paths with the lowest cost)
– A* (improved version of Dijkstra algorithm)
22. 22/25
Example of finding the shortest path using REST API
Example request
POST http://localhost:7474/db/data/node/35/path
Accept: application/json; charset=UTF-8
Content-Type: application/json
{
"to" : "http://localhost:7474/db/data/node/30",
"max_depth" : 3,
"relationships" : {
"type" : "to",
"direction" : "out"
},
"algorithm" : "shortestPath"
}
Example response
200: OK
Content-Type: application/json; charset=UTF-8
{
"start" : "http://localhost:7474/db/data/node/35",
"nodes" : [ "http://localhost:7474/db/data/node/35",
"http://localhost:7474/db/data/node/31","http://localhost:7474/db/data/node/30" ],
"length" : 2,
"relationships" : [ "http://localhost:7474/db/data/relationship/26", "http://localhost:7474/db/data/relationship/32" ],
"end" : "http://localhost:7474/db/data/node/30"
}
23. 23/25
Spring Data Neo4J
Spring Data is an umbrella project that makes it easy to use new data access technologies,
such as non-relational databases, map-reduce frameworks, and cloud based data services.
Spring Data Neo4j is an integration library for Neo4j and it was the first Spring Data project
@NodeEntity
public class Movie {
@GraphId Long id;
@Indexed(type = FULLTEXT, indexName = "search")
String title;
Person director;
@RelatedTo(type="ACTS_IN", direction = INCOMING)
Set<Person> actors;
@Query("start movie=node({self})
match movie-->genre<--similar
return similar")
Iterable<Movie> similarMovies;
}
24. 24/25
Bibliography
● I. Robinson, J. Webber, E. Eifrem, Graph Databases, O’Reilly Media, 2013
● R. Angles, C. Gutierrez, Survey of graph database models, ACM Computing
Surveys (CSUR), 2008
● M. A. Rodriguez, P. Neubauer, The Graph Traversal Pattern, Graph Data
Management: Techniques and Applications, 2011
● Jonas Partner, Aleksa Vukotic, and Nicki Watt, Neo4j in Action, Manning,
2014
● Eric Redmond. Jim R. Wilson, Seven Databases in Seven Weeks, The
Pragmatic Bookshelf, 2012
● G. Schreiber, Y. Raimond, RDF 1.1 Primer, W3C, 2014