The document discusses various ways to extend Neo4j's functionality through procedures, functions, and aggregation functions. It provides examples of loading data from external sources like databases and XML using procedures. User-defined procedures, functions, and aggregations allow custom logic to be added and invoked from Cypher. Over 400 procedures and functions are available through the APOC library for tasks like data integration, analytics, and more.
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The document discusses various ways to extend Neo4j using procedures, functions, and aggregation functions written in JVM languages. It provides examples of full text search, string joining, and finding the longest string aggregation procedures. The document also discusses how the Apoc procedures library can be used for data integration and loading data from relational databases and XML into Neo4j.
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This document summarizes the powerful extensions available for Neo4j including native language drivers, procedures, functions, and aggregation functions. It provides examples of loading data from external sources using procedures and transforming the data with Cypher. Key points covered include loading data from a relational database or XML source using procedures like apoc.load.jdbc and apoc.load.xml, unwinding arrays, filtering rows, and merging related nodes in the graph.
This document discusses using Python to connect to and interact with a PostgreSQL database. It covers:
- Popular Python database drivers for PostgreSQL, including Psycopg which is the most full-featured.
- The basics of connecting to a database, executing queries, and fetching results using the DB-API standard. This includes passing parameters, handling different data types, and error handling.
- Additional Psycopg features like server-side cursors, transaction handling, and custom connection factories to access columns by name rather than number.
In summary, it provides an overview of using Python with PostgreSQL for both basic and advanced database operations from the Python side.
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Psycopg2 - Connect to PostgreSQL using Python ScriptSurvey Department
It's the presentation slides I prepared for my college workshop. This demonstrates how you can talk with PostgreSql db using python scripting.For queries, mail at dipeshsuwal@gmail.com
Dpilot is a cloud based file transfer application that allows its user to upload data on cloud server and the receiver on the other hand can downlaod the data from the server. The Downlaod information is send to the receiver via mail service.
Other Features include:-
Secure Login system
Easy data Access
Lightening Fast Uploads and Downloads
Connect with your Facebook Or Gmail Account for easy access
This document contains code for a file sharing application that allows users to upload files to Amazon S3 storage and send download links to recipients via email. It includes code for connecting to MongoDB and S3 databases, handling file uploads with Multer, generating signed URLs with AWS, sending emails with Nodemailer, and building APIs with Express. The React code handles form inputs, file previews, and confirming upload status.
Neo4j GraphTour: Utilizing Powerful Extensions for Analytics and OperationsMark Needham
The document discusses various ways to extend Neo4j using procedures, functions, and aggregation functions written in JVM languages. It provides examples of full text search, string joining, and finding the longest string aggregation procedures. The document also discusses how the Apoc procedures library can be used for data integration and loading data from relational databases and XML into Neo4j.
Utilizing Powerful Extensions for Analytics and OperationsNeo4j
This document summarizes the powerful extensions available for Neo4j including native language drivers, procedures, functions, and aggregation functions. It provides examples of loading data from external sources using procedures and transforming the data with Cypher. Key points covered include loading data from a relational database or XML source using procedures like apoc.load.jdbc and apoc.load.xml, unwinding arrays, filtering rows, and merging related nodes in the graph.
This document discusses using Python to connect to and interact with a PostgreSQL database. It covers:
- Popular Python database drivers for PostgreSQL, including Psycopg which is the most full-featured.
- The basics of connecting to a database, executing queries, and fetching results using the DB-API standard. This includes passing parameters, handling different data types, and error handling.
- Additional Psycopg features like server-side cursors, transaction handling, and custom connection factories to access columns by name rather than number.
In summary, it provides an overview of using Python with PostgreSQL for both basic and advanced database operations from the Python side.
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This document discusses Neo4j, a native graph database. It highlights Neo4j's capabilities like being ACID-compliant, supporting powerful analytics on graphs through integration with tools like Cypher and APOC, and its evolution over time to support features like user-defined procedures and functions. The document also provides examples of using algorithms like PageRank and visualization libraries like NeoVis to analyze and explore graph data in Neo4j.
Psycopg2 - Connect to PostgreSQL using Python ScriptSurvey Department
It's the presentation slides I prepared for my college workshop. This demonstrates how you can talk with PostgreSql db using python scripting.For queries, mail at dipeshsuwal@gmail.com
Dpilot is a cloud based file transfer application that allows its user to upload data on cloud server and the receiver on the other hand can downlaod the data from the server. The Downlaod information is send to the receiver via mail service.
Other Features include:-
Secure Login system
Easy data Access
Lightening Fast Uploads and Downloads
Connect with your Facebook Or Gmail Account for easy access
This document contains code for a file sharing application that allows users to upload files to Amazon S3 storage and send download links to recipients via email. It includes code for connecting to MongoDB and S3 databases, handling file uploads with Multer, generating signed URLs with AWS, sending emails with Nodemailer, and building APIs with Express. The React code handles form inputs, file previews, and confirming upload status.
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"PostgreSQL and Python" Lightning Talk @EuroPython2014Henning Jacobs
PL/Python allows users to write PostgreSQL functions and procedures using Python. It enables accessing PostgreSQL data and running Python code from within SQL queries. For example, a function could query a database table, process the results in Python by accessing modules, and return a value to the SQL query. This opens up possibilities to leverage Python's extensive libraries and expressiveness to expose data and perform complex validation from PostgreSQL.
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In this talk, I covered the basics of GPars, including what it's like to learn to use it. Although I've done a fair amount of concurrent programming, I've just started using GPars. As such, this talk should be suitable for Groovy beginners.
Here are the key points about GRASP patterns:
1. GRASP stands for General Responsibility Assignment Software Patterns. It is a set of 9 patterns used to assign responsibilities to classes in object-oriented design.
2. Properly assigning responsibilities and distributing them among classes is a core part of object-oriented design. Developers and designers should be familiar with these patterns.
3. The 9 GRASP patterns are: Information Expert, Creator, Controller, Low Coupling, High Cohesion, Indirection, Polymorphism, Pure Fabrication, and Protected Variations. Each pattern provides guidance on distributing responsibilities in a certain context.
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Includes
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- Functors
- Monads
ROracle is an R package that enables connectivity to Oracle Database, allowing users to execute SQL statements from R and interface with Oracle databases. It provides a high-performance Oracle driver based on OCI. ROracle reads and writes data between R and Oracle databases much faster than other R database connectors. It is open source and available on CRAN.
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KNN-GiST indexes were added in PostgreSQL 9.1 and greatly accelerate some common queries in the geospatial and textual search realms. This presentation will demonstrate the power of KNN-GiST indexes on geospatial and text searching queries, but also their present limitations through some of my experimentations. I will also discuss some of the theory behind KNN (k-nearest neighbor) as well as some of the applications this feature can be applied too.
To see a version of the talk given at PostgresOpen 2011, please visit http://www.youtube.com/watch?v=N-MD08QqGEM
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This document summarizes the evolution of Neo4j's developer surface over time, including the introduction of Bolt drivers, user defined procedures, functions, and aggregation functions. It provides examples of how to write and call user defined procedures and functions in Java to extend Neo4j's capabilities for analytics and operations.
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"PostgreSQL and Python" Lightning Talk @EuroPython2014Henning Jacobs
PL/Python allows users to write PostgreSQL functions and procedures using Python. It enables accessing PostgreSQL data and running Python code from within SQL queries. For example, a function could query a database table, process the results in Python by accessing modules, and return a value to the SQL query. This opens up possibilities to leverage Python's extensive libraries and expressiveness to expose data and perform complex validation from PostgreSQL.
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This document provides an overview of Kotlin coroutines through examples. It begins with examples of synchronous and asynchronous programming approaches and their drawbacks. Coroutines are then introduced as a better solution that allows writing asynchronous code in a synchronous-looking way using suspend functions and other features. Key benefits of coroutines include avoiding callback hell and enabling easy control of thread dispatch. Examples are provided of using coroutines for tasks like interleaving reading and processing of data from a file. Under the hood, coroutines implement asynchronous behavior using state machines and continuation passing.
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In this talk, I covered the basics of GPars, including what it's like to learn to use it. Although I've done a fair amount of concurrent programming, I've just started using GPars. As such, this talk should be suitable for Groovy beginners.
Here are the key points about GRASP patterns:
1. GRASP stands for General Responsibility Assignment Software Patterns. It is a set of 9 patterns used to assign responsibilities to classes in object-oriented design.
2. Properly assigning responsibilities and distributing them among classes is a core part of object-oriented design. Developers and designers should be familiar with these patterns.
3. The 9 GRASP patterns are: Information Expert, Creator, Controller, Low Coupling, High Cohesion, Indirection, Polymorphism, Pure Fabrication, and Protected Variations. Each pattern provides guidance on distributing responsibilities in a certain context.
4. When designing a system, analyzing which classes should have which responsibilities based
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This is a presentation which was presented on Innovecs Tech Hangout in Kiev June 2017.
Includes
- Pure Functions
- Currying
- Function Composition
- Functors
- Monads
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The document provides documentation on Ring programming language functions and features. It summarizes the use of various functions in the stdlib.ring library including puts(), print(), print2Str(), getstring(), getnumber(), apppath(), justfilepath(), justfilename(), value(), times(), map(), filter(), split(), splitmany(), newlist(), capitalized(), isspecial(), isvowel(), linecount(), factorial(), fibonacci(), isprime(), and sign(). Examples are given to demonstrate the usage of each function.
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KNN-GiST indexes were added in PostgreSQL 9.1 and greatly accelerate some common queries in the geospatial and textual search realms. This presentation will demonstrate the power of KNN-GiST indexes on geospatial and text searching queries, but also their present limitations through some of my experimentations. I will also discuss some of the theory behind KNN (k-nearest neighbor) as well as some of the applications this feature can be applied too.
To see a version of the talk given at PostgresOpen 2011, please visit http://www.youtube.com/watch?v=N-MD08QqGEM
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This document provides an overview of functional programming using Underscore.js and Lo-Dash. It discusses key concepts of functional programming like pure functions, first class functions, and higher order functions. It then explains how Underscore.js and Lo-Dash support functional programming in JavaScript with functions like compose, map, reduce, filter, and utilities to work with functions as first class citizens. The document compares Underscore.js and Lo-Dash, noting Lo-Dash's better performance, larger feature set, and utilities like partial and result. It emphasizes applying functional concepts judiciously for benefits while avoiding rewriting all code to be purely functional.
Spring Boot is a framework for building stand-alone, production-grade Spring based Applications that can be "just run". It takes an opinionated view of the Spring platform and third-party libraries so developers can get started with minimum fuss. Some key features include an embedded HTTP server and automatic configuration support. Spring Boot applications can be started using Java -jar or executed using Gradle/Maven and can be packaged as an executable jar or war file for deployment. The document also discusses using Spring Boot with databases, profiles, Spring Data, GORM, views and some miscellaneous topics.
You may all know that JSON is a subset of JavaScript, but… Did you know that HTML5 implements NoSQL databases? Did you know that JavaScript was recommended for REST by HTTP co-creator Roy T. Fielding himself? Did you know that map & reduce are part of the native JavaScript API? Did you know that most NoSQL solutions integrate a JavaScript engine? CouchDB, MongoDB, WakandaDB, ArangoDB, OrientDB, Riak…. And when they don’t, they have a shell client which does. The story of NoSQL and JavaScript goes beyond your expectations and opens more opportunities than you might imagine… What better match could you find than a flexible and dynamic language for schemaless databases? Isn’t an event-driven language what you’ve been waiting for to manage consistency? When NoSQL doesn’t come to JavaScript, JavaScript comes to NoSQL. And does it very well.
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Next-generation API Development with GraphQL and PrismaNikolas Burk
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2) It describes the key features of their framework, such as MVC structure, middleware support, asset packaging, and command line tools.
3) The author explains that they rolled their own framework to learn more about how frameworks work, have more control over the technology stack, and because node.js performance is better than Ruby on Rails. They emphasize that building the framework was a fun learning experience.
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Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
The Ultimate Guide to Top 36 DevOps Testing Tools for 2024.pdfkalichargn70th171
Testing is pivotal in the DevOps framework, serving as a linchpin for early bug detection and the seamless transition from code creation to deployment.
DevOps teams frequently adopt a Continuous Integration/Continuous Deployment (CI/CD) methodology to automate processes. A robust testing strategy empowers them to confidently deploy new code, backed by assurance that it has passed rigorous unit and performance tests.
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...kalichargn70th171
Visual testing plays a vital role in ensuring that software products meet the aesthetic requirements specified by clients in functional and non-functional specifications. In today's highly competitive digital landscape, users expect a seamless and visually appealing online experience. Visual testing, also known as automated UI testing or visual regression testing, verifies the accuracy of the visual elements that users interact with.
Building API data products on top of your real-time data infrastructureconfluent
This talk and live demonstration will examine how Confluent and Gravitee.io integrate to unlock value from streaming data through API products.
You will learn how data owners and API providers can document, secure data products on top of Confluent brokers, including schema validation, topic routing and message filtering.
You will also see how data and API consumers can discover and subscribe to products in a developer portal, as well as how they can integrate with Confluent topics through protocols like REST, Websockets, Server-sent Events and Webhooks.
Whether you want to monetize your real-time data, enable new integrations with partners, or provide self-service access to topics through various protocols, this webinar is for you!
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Folding Cheat Sheet #6 - sixth in a seriesPhilip Schwarz
Left and right folds and tail recursion.
Errata: there are some errors on slide 4. See here for a corrected versionsof the deck:
https://speakerdeck.com/philipschwarz/folding-cheat-sheet-number-6
https://fpilluminated.com/deck/227
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
Implementing Odoo, a robust and all-inclusive business management software, can significantly improve your organisation. To get the most out of it and ensure a smooth implementation, it is important to have a strategic plan. This blog shares some essential tips to help you with successful Odoo ERP implementation. From planning and customisation to training and support, this blog outlines some expert advice that will guide you through the process confidently. It is true that adopting a new software can be challenging, and hence, this post has tailored these tips to help you avoid common mistakes and achieve the best results. Whether you run a small business or a large enterprise, these tips will help you streamline operations, boost productivity, and drive growth. Whether you are new to Odoo or looking to improve your current setup, it is essential to learn the key strategies for a successful Odoo implementation. Implementing Odoo doesn’t have to be difficult. With the right approach and guidance, you can use this software to elevate your business. Read on to discover the secrets of a successful Odoo implementation.
Why is successful Odoo implementation crucial?
Implementing Odoo effectively can transform your business by making processes smoother, increasing efficiency, and providing useful insights. It helps align your operations with best practices, boosting productivity and aiding better decision-making. A well-executed implementation ensures you get the most out of your investment, while a poor one can cause disruptions, higher costs, and frustration among employees.
What is Continuous Testing in DevOps - A Definitive Guide.pdfkalichargn70th171
Once an overlooked aspect, continuous testing has become indispensable for enterprises striving to accelerate application delivery and reduce business impacts. According to a Statista report, 31.3% of global enterprises have embraced continuous integration and deployment within their DevOps, signaling a pervasive trend toward hastening release cycles.
Photoshop Tutorial for Beginners (2024 Edition)alowpalsadig
Photoshop Tutorial for Beginners (2024 Edition)
Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."
Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
Photoshop Tutorial for Beginners (2024 Edition)Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
The importance of developing and designing programming in 2024
Programming design and development represents a vital step in keeping pace with technological advancements and meeting ever-changing market needs. This course is intended for anyone who wants to understand the fundamental importance of software development and design, whether you are a beginner or a professional seeking to update your knowledge.
Course objectives:
1. **Learn about the basics of software development:
- Understanding software development processes and tools.
- Identify the role of programmers and designers in software projects.
2. Understanding the software design process:
- Learn about the principles of good software design.
- Discussing common design patterns such as Object-Oriented Design.
3. The importance of user experience (UX) in modern software:
- Explore how user experience can improve software acceptance and usability.
- Tools and techniques to analyze and improve user experience.
4. Increase efficiency and productivity through modern development tools:
- Access to the latest programming tools and languages used in the industry.
- Study live examples of applications
Boost Your Savings with These Money Management AppsJhone kinadey
A money management app can transform your financial life by tracking expenses, creating budgets, and setting financial goals. These apps offer features like real-time expense tracking, bill reminders, and personalized insights to help you save and manage money effectively. With a user-friendly interface, they simplify financial planning, making it easier to stay on top of your finances and achieve long-term financial stability.
The Comprehensive Guide to Validating Audio-Visual Performances.pdfkalichargn70th171
Ensuring the optimal performance of your audio-visual (AV) equipment is crucial for delivering exceptional experiences. AV performance validation is a critical process that verifies the quality and functionality of your AV setup. Whether you're a content creator, a business conducting webinars, or a homeowner creating a home theater, validating your AV performance is essential.
6. Neo4j Developer Surface
2000-2010 0.x Embedded Java API
2010-2014 1.x REST
2014-2015 2.x Cypher over HTTP
2016 3.0.x Bolt, Official Language Drivers, User Defined Procedures
2016 3.1.x User Defined Functions
2017 3.2.x User Defined Aggregation Functions
7. Neo4j Developer Surface
2000-2010 0.x Embedded Java API
2010-2014 1.x REST
2014-2015 2.x Cypher over HTTP
2016 3.0.x Bolt, Official Language Drivers, User Defined Procedures
2016 3.1.x User Defined Functions
2017 3.2.x User Defined Aggregation Functions
11. Neo4j Developer Surface
2000-2010 0.x Embedded Java API
2010-2014 1.x REST
2014-2015 2.x Cypher over HTTP
2016 3.0.x Bolt, Official Language Drivers, User Defined Procedures
2016 3.1.x User Defined Functions
2017 3.2.x User Defined Aggregation Functions
53. UNWIND the array of elements
CALL apoc.load.xml('http://overpass.osm.rambler.ru/cgi/xapi_meta?*[bbox=11.54,48.14,11.543,48.145]')
YIELD value
UNWIND value["_children"] AS child
WITH child WHERE child["_type"] = "node"
WITH child.id AS id,
child.lat AS latitude,
child.lon AS longitude,
child["user"] AS userName
MERGE (point:Point {id: id})
SET point.latitude = latitude,
point.longitude = longitude
MERGE (user:User {name: userName})
MERGE (user)-[:EDITED]->(point)
57. apoc.load.json
WITH "https://api.stackexchange.com/2.2/questions?pagesize=100&order=desc&sort=creation&tagged=neo4j&site=stackoverflow&filter=!5-i6Zw8Y)4W7vpy91PMYsKM-k9yzEsSC1_Uxlf" AS url
CALL apoc.load.json(url) YIELD value
UNWIND value.items AS q
MERGE (question:Question {id:q.question_id})
ON CREATE SET question.title = q.title,
question.share_link = q.share_link,
question.favorite_count = q.favorite_count
MERGE (owner:User {id:q.owner.user_id})
ON CREATE SET owner.display_name = q.owner.display_name
MERGE (owner)-[:ASKED]->(question)
FOREACH (tagName IN q.tags |
MERGE (tag:Tag {name:tagName}) MERGE (question)-[:TAGGED]->(tag))
FOREACH (a IN q.answers |
MERGE (question)<-[:ANSWERS]-(answer:Answer {id:a.answer_id})
MERGE (answerer:User {id:a.owner.user_id})
ON CREATE SET answerer.display_name = a.owner.display_name
MERGE (answer)<-[:PROVIDED]-(answerer)
)
58. WITH "https://api.stackexchange.com/2.2/questions?pagesize=100&order=desc&sort=creation&tagged=neo4j&site=stackoverflow&filter=!5-i6Zw8Y)4W7vpy91PMYsKM-k9yzEsSC1_Uxlf" AS url
CALL apoc.load.json(url) YIELD value
UNWIND value.items AS q
MERGE (question:Question {id:q.question_id})
ON CREATE SET question.title = q.title,
question.share_link = q.share_link,
question.favorite_count = q.favorite_count
MERGE (owner:User {id:q.owner.user_id})
ON CREATE SET owner.display_name = q.owner.display_name
MERGE (owner)-[:ASKED]->(question)
FOREACH (tagName IN q.tags |
MERGE (tag:Tag {name:tagName}) MERGE (question)-[:TAGGED]->(tag))
FOREACH (a IN q.answers |
MERGE (question)<-[:ANSWERS]-(answer:Answer {id:a.answer_id})
MERGE (answerer:User {id:a.owner.user_id})
ON CREATE SET answerer.display_name = a.owner.display_name
MERGE (answer)<-[:PROVIDED]-(answerer)
)
Use FOREACH for arrays within a row
FOREACH (tagName IN q.tags |
MERGE (tag:Tag {name:tagName}) MERGE (question)-[:TAGGED]->(tag))
FOREACH (a IN q.answers |
MERGE (question)<-[:ANSWERS]-(answer:Answer {id:a.answer_id})
MERGE (answerer:User {id:a.owner.user_id})
ON CREATE SET answerer.display_name = a.owner.display_name
MERGE (answer)<-[:PROVIDED]-(answerer)
)
99. MATCH (d:District)
WITH collect(d) AS districts
CALL spatial.addNodes('geom', districts) YIELD node
RETURN count(*)
Add nodes to spatial index
100. CALL spatial.withinDistance('geom',
{latitude: 37.563440, longitude: -122.322265}, 1)
YIELD node AS d
WITH d, d.wkt AS wkt, d.state AS state, d.district AS district
LIMIT 1
MATCH (d)<-[:REPRESENTS]-(l:Legislator)
MATCH (l)-[:SERVES_ON]->(c:Committee)
MATCH (c)<-[:REFERRED_TO]-(b:Bill)
MATCH (b)-[:DEALS_WITH]->(s:Subject)
RETURN *
Query nodes by location
101. CALL spatial.withinDistance('geom',
{latitude: 37.563440, longitude: -122.322265}, 1)
YIELD node AS d
WITH d, d.wkt AS wkt, d.state AS state, d.district AS district
LIMIT 1
MATCH (d)<-[:REPRESENTS]-(l:Legislator)
MATCH (l)-[:SERVES_ON]->(c:Committee)
MATCH (c)<-[:REFERRED_TO]-(b:Bill)
MATCH (b)-[:DEALS_WITH]->(s:Subject)
RETURN *
Finds nodes within 1km
102. CALL spatial.withinDistance('geom',
{latitude: 37.563440, longitude: -122.322265}, 1)
YIELD node AS d
WITH d, d.wkt AS wkt, d.state AS state, d.district AS district
LIMIT 1
MATCH (d)<-[:REPRESENTS]-(l:Legislator)
MATCH (l)-[:SERVES_ON]->(c:Committee)
MATCH (c)<-[:REFERRED_TO]-(b:Bill)
MATCH (b)-[:DEALS_WITH]->(s:Subject)
RETURN *
Continue with the rest of the query
107. CREATE (n:News)
SET n.text = "Scores of people were already lying dead or
injured inside a crowded Orlando nightclub,
and the police had spent hours trying to connect with the
gunman and end the situation without further violence.
But when Omar Mateen threatened to set off explosives, the
police decided to act, and pushed their way through a
wall to end the bloody standoff."
Annotating text
117. Finds the optimal
path or evaluates
route availability and
quality
Evaluates how a
group is clustered
or partitioned
Determines the
importance of distinct
nodes in the network
118. Single Source Short Path
All-Nodes SSP
Parallel BFS / DFS
Strongly Connected
Components
Union Find / WCC
Label Propagation
Louvain
Triangle-Count /
Clustering Coefficent
PageRank (baseline)
Betweeness
Closeness
Degree
119. 1.Call as Cypher procedure
2.Pass in specification (Label, Prop, Query) and configuration
3.~.stream variant returns (a lot) of results
CALL algo.<name>.stream('Label','TYPE', {conf})
YIELD nodeId, score
4.non-stream variant writes results to graph
and returns statistics
CALL algo.<name>('Label','TYPE', {conf})
Usage
120. Pass in Cypher statement for node and relationship lists.
CALL algo.<name>(
'MATCH ... RETURN id(n)',
'MATCH (n)-->(m)
RETURN id(n) as source, id(m) as target',
{graph:'cypher'})
Cypher Projection
126. CALL algo.labelPropagation()
MATCH (n:Page)
WITH n ORDER BY n.pagerank DESC
LIMIT 1000000
WITH n.partition AS partition,
count(*) AS clusterSize,
collect(n.title) AS pages
RETURN pages[0] AS mainPage,
pages[1..10] AS otherPages
ORDER BY clusterSize DESC
LIMIT 20
Largest Clusters
137. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
138. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
139. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
140.
141. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
type Planet {
name: String
climate: String
}
type Character {
name: String
friends: [Character]
homeWorld: Planet
species: Species
}
type Species {
name: String
lifespan: Int
origin: Planet
}
148. CALL graphql.idl('
type Movie {
title: String!
released: Int
actors: [Person] @relation(name:"ACTED_IN",direction:IN)
}
type Person {
name: String!
born: Int
movies: [Movie] @relation(name:"ACTED_IN")
}'
)
149. WITH '{ Person(born: 1961) { name, born } }' as query, {} as params
CALL graphql.execute(query,params)
YIELD result
UNWIND result.Person as p
RETURN p.name, p.born