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.
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.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
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.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
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.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Review the latest features released in Neo4j version 4.1 including Cypher, database drivers, clustering, security, and extension libraries like APOC and Spring Data Neo4j!
Complex hierarchical relationships between entities can only be mapped with difficulty in a relational database and demanding queries are usually quite slow.
Graph databases are optimized for exactly these kinds of relationships and can provide high-performance results even with huge amounts of data. Moreover, not only the entities that are stored in the database, have attributes, but also their relationships. Queries can look at entities as well as their relationships.
Get to know the basics of graph databases, using Neo4j as an example, and see how it is used C# projects.
This talk argues that the future of data query/analytic languages will be all about embedding the language into the native programming language of the developer. As an example of this style, the Gremlin graph traversal language is presented. Gremlin can be represented in any programming language that supports function composition and function nesting. The language representation is then compiled to Gremlin bytecode to ultimately be executed by the/a Gremlin graph traversal machine. This enables both the Gremlin language and machine to be agnostic to the execution language.
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.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
This presentation introduces the graph model as obvious choice for rich and connected data. Graph Databases are a category of open-source NoSQL datastores which are specialized in storing, handling and querying graph structures efficiently.
Use cases represent the applicability of the graph model across many domains.
Neo4j as the most widely used graph database supports the property graph model, which is explained in detail.
To query a graph database a powerful and expressive but also friendly and easily understandable query language that is tailored for graph patterns is key. Neo4j's Cypher is such a query language developed from the ground up to support expressing challenging use-cases in a comprehensive way.
A series of examples rounds up the presentation to apply the lessons learned.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
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.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Review the latest features released in Neo4j version 4.1 including Cypher, database drivers, clustering, security, and extension libraries like APOC and Spring Data Neo4j!
Complex hierarchical relationships between entities can only be mapped with difficulty in a relational database and demanding queries are usually quite slow.
Graph databases are optimized for exactly these kinds of relationships and can provide high-performance results even with huge amounts of data. Moreover, not only the entities that are stored in the database, have attributes, but also their relationships. Queries can look at entities as well as their relationships.
Get to know the basics of graph databases, using Neo4j as an example, and see how it is used C# projects.
This talk argues that the future of data query/analytic languages will be all about embedding the language into the native programming language of the developer. As an example of this style, the Gremlin graph traversal language is presented. Gremlin can be represented in any programming language that supports function composition and function nesting. The language representation is then compiled to Gremlin bytecode to ultimately be executed by the/a Gremlin graph traversal machine. This enables both the Gremlin language and machine to be agnostic to the execution language.
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.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
This presentation introduces the graph model as obvious choice for rich and connected data. Graph Databases are a category of open-source NoSQL datastores which are specialized in storing, handling and querying graph structures efficiently.
Use cases represent the applicability of the graph model across many domains.
Neo4j as the most widely used graph database supports the property graph model, which is explained in detail.
To query a graph database a powerful and expressive but also friendly and easily understandable query language that is tailored for graph patterns is key. Neo4j's Cypher is such a query language developed from the ground up to support expressing challenging use-cases in a comprehensive way.
A series of examples rounds up the presentation to apply the lessons learned.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
Neo4j: JDBC Connection Case Using LibreOfficeEric Lee
Show how to connect Neo4j dataase using JDBC driver, software client is LibreOffice Base. The slide including setting, query execution and trouble shooting.
A simple CRUD (no D for the blockchain) cases, you can understand how to use R3 Corda to build a simple "database", record the state by data flow and smart contract.
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.
Combine Spring Data Neo4j and Spring Boot to quicklNeo4j
Speakers: Michael Hunger (Neo Technology) and Josh Long (Pivotal)
Spring Data Neo4j 3.0 is here and it supports Neo4j 2.0. Neo4j is a tiny graph database with a big punch. Graph databases are imminently suited to asking interesting questions, and doing analysis. Want to load the Facebook friend graph? Build a recommendation engine? Neo4j's just the ticket. Join Spring Data Neo4j lead Michael Hunger (@mesirii) and Spring Developer Advocate Josh Long (@starbuxman) for a look at how to build smart, graph-driven applications with Spring Data Neo4j and Spring Boot.
Slides from a talk at a meetup organized by SF Scala at Spotify's San Francisco office. The slides present details of playlist recommendations at Spotify and how Spotify uses Scalding to develop robust and reliable pipelines to generate these recommendations.
Meetup details: http://www.meetup.com/SF-Scala/events/224430674/
Analyze one year of radio station songs aired with Spark SQL, Spotify, and Da...Paul Leclercq
Paris Spark Meetup - May 2017
Video : https://www.youtube.com/watch?v=w5Zd-1wIJrU
AdHoc analysis of radio stations broadcasts stored in a parquet files with plain SQL, the dataframe API.
The aim was to notice radio stations habits, differences and if radio stations brainwashing is a thing
This talk's Databricks notebook can be found here : https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6937750999095841/3645330882010081/6197123402747553/latest.html
A Drupal case study on developing the Australian Broadcasting Corporation's Dig Music website. I gave this talk at Drupal Downunder #ddu2011 in Brisbane, Australia (Jan 23, 2011).
I discuss how the Semantic Web was used to create a real time snapshot of a musical artist that is pulled live from the digital radio broadcast.
I also talk about performance issues we encountered and ways that they were overcome.
One of the challenges that comes with moving to MongoDB is figuring how to best model your data. While most developers have internalized the rules of thumb for designing schemas for RDBMSs, these rules don't always apply to MongoDB. The simple fact that documents can represent rich, schema-free data structures means that we have a lot of viable alternatives to the standard, normalized, relational model. Not only that, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense. Understandably, this begets good questions: Are foreign keys permissible, or is it better to represent one-to-many relations withing a single document? Are join tables necessary, or is there another technique for building out many-to-many relationships? What level of denormalization is appropriate? How do my data modeling decisions affect the efficiency of updates and queries? In this session, we'll answer these questions and more, provide a number of data modeling rules of thumb, and discuss the tradeoffs of various data modeling strategies.
SVC101 Building Search into Your App - AWS re: Invent 2012Amazon Web Services
Amazon CloudSearch is a fully-managed search service in the cloud that allows customers to easily integrate fast and highly scalable search functionality into their applications. In this session, we cover the basics of search and search engines. We take an introductory look at CloudSearch along with a deep dive showing how to build a CloudSearch-based web application.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
1. Introduction to
Graph Database
!
Eric C.Y. LEE
eric@nus.edu.sg
eric@eric.lv
National University of Singapore
February, 2014
2. Outline
•
Relational Database v.s. Graph Database
•
Design the Database in Graph Architecture
•
Basic Usage of Neo4j
•
Cypher Query Language
•
Business application
!2
3. Relational Database
•
Based on table schema.
•
Field -> Record -> Table -> Database
•
Query by SQL syntax.
•
Open source and commercial products available.
•
MySQL, Oracle, PostgreSQL, MS-SQL Server.
•
LAMP: Linux+Apache+MySQL+PHP
!3
4. Graph Database
•
Schema-less, based on graph theory.
•
Only two types of data inside the graph database.
•
Node and relationship (edge) .
•
One of the NoSQL database management systems, query by
several query languages, depend on database products.
•
Widely use in social network system and large scale website
architecture.
!4
Figure Credit: Wikipedia
5. Modelling a K-pop Database
All materials for database construction,
we can find in these wikipedia pages.
!5
6. The information I want to
provide.
•
Team member profiles
•
•
Group, Name, Birth Place,Birth Year, Birth Month
Released albums
•
Title, Released Year, Number of Sales
!6
7. K-Pop Profiles in Table View
Group
SNSD
SNSD
SNSD
SNSD
SNSD
SNSD
SNSD
SNSD
SNSD
KARA
KARA
KARA
KARA
Name
Taeyeon
Jessica
Sunny
Tiffany
Hyoyeon
Yuri
Sooyoung
Yoona
Seohyun
Gyuri
Seungyeon
Hara
Jiyoung
Birth Place
Korea
U.S.A.
U.S.A.
U.S.A.
Korea
Korea
Korea
Korea
Korea
Korea
Korea
Korea
Korea
!7
Birth Year
1989
1989
1989
1989
1989
1989
1990
1990
1991
1988
1988
1991
1994
Birth Month
March
April
May
August
September
December
February
May
June
May
July
January
January
Any finding?
8. K-Pop Albums in Table View
Group
Title
Released Year
Number of sales
SNSD
Girls’ Generation
2007
284994
SNSD
Oh
2010
406662
SNSD
The Boys
2011
449616
SNSD
I GOT A BOY
2013
293302
KARA
BLOOMING
2007
50000
KARA
REVOLUTION
2009
80000
KARA
STEP
2011
100662
KARA
FULL BLOOM
2013
46199
!8
Any finding?
10. Group
Title
Released Year
Number of sales
SNSD
Girls’ Generation
2007
284994
SNSD
Oh
2010
406662
SNSD
The Boys
2011
449616
SNSD
I GOT A BOY
2013
293302
KARA
BLOOMING
2007
50000
KARA
REVOLUTION
2009
80000
KARA
STEP
2011
100662
KARA
FULL BLOOM
2013
46199
2007
2009
2010
2011
2013
SNSD
KARA
!10
27. Query Cases
Who is the member of Girl’s Generation(SNSD)?
•
MATCH (snsd{name:”SNSD”})-[:HAS_MEMBER]->(member)
RETURN member;
Who is the youngest member of KARA?
•
MATCH (kara{name:”KARA”})-[:HAS_MEMBER]->(member),
(member)-[:BIRTH_YEAR]->(year)
RETURN member, ORDER BY (year.year) LIMIT 1 ;
!27
28. Query Cases
Who is not born in Korea? Who and where.
•
MATCH (singer)-[:BIRTH_PLACE]->(country)
WHERE NOT country.name="Korea"
RETURN singer, country;
Which album is the top selling of SNSD? Show the
album name and number.
•
MATCH (Group{name:”SNSD”})-[:HAS_ALBUM]->(albums)
WITH albums ORDER BY albums.sales DESC
RETURN albums LIMIT 1;
!28
30. Potential Orders?
Customer:B bought SNSD and KARA’s album, we can promote albums of
T-ARA to him?
•
Both SNSD and KARA are female K-pop groups, T-ARA is female K-pop
group too.
Give Customer:A price discount, push him buy the album “I GOT A BOY”.
•
SNSD has 4 albums. According to the graph, Customer:A bought the 3
albums from us. He didn’t buy “I GOT A BOY”.
Ask male customer buy female K-pop groups’ album is much easier.
•
The database shows most of female K-pop group album buyers are male.
!30
31. [:HAS_STUDENT]
Person
Name:”TAN TIN WEE”
Occupation: “Professor”
[:HAS_MODULE]
Person
Name:”Eric Lee”
Occupation: “Student”
[:HAS_TA]
[:HAS_TA]
[:SAY]
Person
Name:”Christine Eng”
Occupation: “Student”
[:HAS_STUDENT]
Module
ID:”LSM3241”
Name:”Bioinformatics and
Biocomputing”
[:HAS_TA]
Person
Name:”Hu Yongli”
Occupation: “Student”
Sentence
Sentence:”Thank you!”
!31