Meetup is a valuable source of data for understanding trends around products or brands. Meetup does not support an analytics package to track group statistics overtime unless you are an administrator of a group. There are no third-party tools or websites that analyze Meetup trends to understand how communities grow.
In this talk I will present a graph-based analytics platform that uses the Meetup.com API to collect and analyze membership statistics over time.
This talk will cover:
How to poll and import periodic data from the Meetup.com API into Neo4j using Node.js.
How to track meetup group growth over time using a Neo4j graph database using Node.js.
How to apply tags to meetup groups and report combined growth of all groups over time.
How to build an interactive documented analytics API to support applications using Node.js and Neo4j.
How to build a business dashboard to visualize time-based statistics and reports using a Node.js based REST API that queries Neo4j.
Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Before jumping straight in to development of such an graph based app, we asked the question that anyone would ask - "what makes it a case for Neo4J? and can you prove it?" Basically de-risking and making a case for management buy in. Further, its more about convincing ourselves as well and hence this comparison.
So this is about that comparison and the white-paper that has resulted from it. It is not the actual project. Source code used to generate the comparison numbers is available on https://github.com/EqualExperts/Apiary-Neo4j-RDBMS-Comparison
The year of the graph: do you really need a graph database? How do you choose...George Anadiotis
Graph databases have been around for more than 15 years, but it was AWS and Microsoft getting in the domain that attracted widespread interest. If they are into this, there must be a reason.
Everyone wants to know more, few can really keep up and provide answers. And as this hitherto niche domain is in the mainstream now, the dynamics are changing dramatically. Besides new entries, existing players keep evolving.
I’ve done the hard work of evaluating solutions, so you don’t have to. An overview of the domain and selection methodology, as presented in Big Data Spain 2018
Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Before jumping straight in to development of such an graph based app, we asked the question that anyone would ask - "what makes it a case for Neo4J? and can you prove it?" Basically de-risking and making a case for management buy in. Further, its more about convincing ourselves as well and hence this comparison.
So this is about that comparison and the white-paper that has resulted from it. It is not the actual project. Source code used to generate the comparison numbers is available on https://github.com/EqualExperts/Apiary-Neo4j-RDBMS-Comparison
The year of the graph: do you really need a graph database? How do you choose...George Anadiotis
Graph databases have been around for more than 15 years, but it was AWS and Microsoft getting in the domain that attracted widespread interest. If they are into this, there must be a reason.
Everyone wants to know more, few can really keep up and provide answers. And as this hitherto niche domain is in the mainstream now, the dynamics are changing dramatically. Besides new entries, existing players keep evolving.
I’ve done the hard work of evaluating solutions, so you don’t have to. An overview of the domain and selection methodology, as presented in Big Data Spain 2018
Hadoop and Neo4j: A Winning Combination for Bioinformaticsosintegrators
This presentation includes an intro to bioinformatics with an emphasis on human genome re-sequencing and how Hadoop and Neo4j can be used together to open striking possibilities.
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.
Democratizing Data within your organization - Data DiscoveryMark Grover
n this talk, we talk about the challenges at scale in an organization like Lyft. We delve into data discovery as a challenge towards democratizing data within your organization. And, go in detail about the solution to solve the challenge of data discovery.
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.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Bigdata and ai in p2 p industry: Knowledge graph and inferencesfbiganalytics
Title: Knowledge graph and inference: use cases in online financial market
Abstract: While the knowledge graph is an active research field in machine learning community, this powerful tool is still less known to the people in the industry. In this talk, I will first introduce knowledge graph and inference techniques including the recent developments which combine with deep learning. Then I will talk about several use cases in online financial market: fraud/anomaly detection, lost contact discovery, intelligent search, name disambiguation and etc. I will also briefly mention how to build knowledge graph using neo4j from different data sources.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size.
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.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
All in one dashboard saves you from logging into all different Ad platforms (social, search, display) for updates on campaign performance.
rtb-media.me
Hadoop and Neo4j: A Winning Combination for Bioinformaticsosintegrators
This presentation includes an intro to bioinformatics with an emphasis on human genome re-sequencing and how Hadoop and Neo4j can be used together to open striking possibilities.
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.
Democratizing Data within your organization - Data DiscoveryMark Grover
n this talk, we talk about the challenges at scale in an organization like Lyft. We delve into data discovery as a challenge towards democratizing data within your organization. And, go in detail about the solution to solve the challenge of data discovery.
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.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Bigdata and ai in p2 p industry: Knowledge graph and inferencesfbiganalytics
Title: Knowledge graph and inference: use cases in online financial market
Abstract: While the knowledge graph is an active research field in machine learning community, this powerful tool is still less known to the people in the industry. In this talk, I will first introduce knowledge graph and inference techniques including the recent developments which combine with deep learning. Then I will talk about several use cases in online financial market: fraud/anomaly detection, lost contact discovery, intelligent search, name disambiguation and etc. I will also briefly mention how to build knowledge graph using neo4j from different data sources.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size.
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.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
All in one dashboard saves you from logging into all different Ad platforms (social, search, display) for updates on campaign performance.
rtb-media.me
MCN2017 Workshop: Web Analytics and SEOBrian Alpert
"Web Analytics and SEO: Learn the Ropes, Work a Plan, Measure the Right Stuff... Declare Victory!" - MCN2017 workshop conducted by Brian Alpert and Elena Villaespesa Cantalapiedra. A workshop designed to make Web Analytics and SEO both understandable, manageable and actionable. Recognizing that most practitioners don't have much time yet need to show measurable results, a common sense, multi-stepped web analytics process is introduced. Tool-specific highlights are largely oriented toward the world's most popular web analytics tool, Google Analytics. Automation tools to lighten the load are presented as well as Google’s new powerful dashboard tool Data Studio. The conversation then shifts to today's SEO landscape, and safe, effective steps practitioners may take to improve findability. The metrics focus continues with a discussion of search-specific free tools and specialized metrics that are effective in demonstrating whether or not website findability is improving.
"Growth Analytics: Evolution, Community and Tools" with emphasis on Google Analytics (and its API), including examples of how web analysts and data scientists can use this rich source of data for analysis and applications.
Customer analytics meetup in Dublin May '18
https://www.meetup.com/Customer-Analytics-Dublin-Meetup/events/250809233/
Growth, Engagement & Search Metrics: Snake Oil or North StarsJune Andrews
Talk at Social Media & Web Analytics
LinkedIn's homepage contains content from over 40 product areas and has evolved over hundreds of experiments. For modern websites this is not an unusual phenomena. To parallelize website development and work in harmony, product teams rely on two guidance systems, organizational cohesion and analytical feedback. Our focus is analytics and in particular, metrics. Unfortunately, not all metrics are created equal. Common metrics such as mean average precision and engagement stickiness have massive downsides if used incorrectly. Here we explore criteria to align optimizing metrics with improving user experience and reaching company goals.
[Webinar Deck] Google Data Studio for Mastering the Art of Data VisualizationsTatvic Analytics
In this webinar, we will take you through the basics of Google Data Studio and enable you to unlock the true value of data when converted into insights. With Google Data Studio, you can turn your data into informative dashboards and monthly reports that are easy to read, easy to share and fully customizable.
Successful data visualization designs are built by smart teams with varied skill sets using advanced visualization tools. This is not an easy combination to find, but we’ll help you get started.
You'd like to be more rational and data-driven in your decision making. You know that your museum has been collecting web traffic metrics using Google Analytics, but you've never fully understood what those reports mean for you or your department. How can you use this popular software to find actionable data that helps you do your job better? In this session you will get a practical tutorial, led by two Google Analytics veterans at the Smithsonian, who will provide an overview of the current Google Analytics, including some of its newest, most powerful features. The presenters will also discuss the step-by-step process for moving beyond measurement just for measurement's sake, using real-life museum case studies as examples.
Presenters: Sara Snyder, Smithsonian American Art Museum; Brian Alpert, Smithsonian Institution.
Presented at the American Alliance of Museums 2016 Annual Meeting & MuseumExpo, 5/27/2016.
Bound Tech is the Top Institute For Tableau training. Tableau Hands On Training and Tableau Job Oriented Training is taught by Our Real Time Trainer with real time scenario’s and examples. We teach our students from the fundamental concepts to the highly developed concepts.
Tableau is one of the fastest evolving Business Intelligence (BI) and data visualization tool. It is very quick to deploy, easy to learn and very spontaneous to use for a customer. It has evolved into one of the fastest and easiest way to share analytics in the cloud.
ChatGPT and Beyond - Elevating DevOps ProductivityVictorSzoltysek
In the dynamic field of DevOps, the quest for efficiency and productivity is endless. This talk introduces a revolutionary toolkit: Large Language Models (LLMs), including ChatGPT, Gemini, and Claude, extending far beyond traditional coding assistance. We'll explore how LLMs can automate not just code generation, but also transform day-to-day operations such as crafting compelling cover letters for TPS reports, streamlining client communications, and architecting innovative DevOps solutions. Attendees will learn effective prompting strategies and examine real-life use cases, demonstrating LLMs' potential to redefine productivity in the DevOps landscape. Join us to discover how to harness the power of LLMs for a comprehensive productivity boost across your DevOps activities.
"Planning Your Analytics Implementation" by Bachtiar Rifai (Kofera Technology)Tech in Asia ID
Bachtiar is a tech startup & science enthusiast with more than 7 years experience in digital marketing, ecommerce, analytics and product development. Bachtiar has spend his career life as marketing leader at top ecommerce such as Lazada & Blanja.com. Currently Bachtiar develop a startup called Kofera, a technology company who provides Software as a Service (SaaS) marketing automation platform powered by Artificial Intelligence (AI) and machine learning. Established in 2016, Kofera helps companies build & optimize PPC campaign using machine learning algorithm to maximize business ROI. Kofera has helped many clients from various industries. Recently, Kofera received pra-series A funding lead by MDI Ventures and followed by Indosterling, DNC & Gunung Sewu.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
In the Eventual Consistency of Succeeding at MicroservicesKenny Bastani
The transition to microservices can be an exciting change of pace for developers. But for organizations, the path to success with microservices is not without embracing a major cultural shift in the process of how teams build and deliver software.
In this session, Kenny will introduce you to the leading practices and patterns for building and scaling event-driven microservice architectures.
Building Cloud Native Architectures with SpringKenny Bastani
Cloud-native architectures are an emerging practice of software development and delivery. This deck was presented at the Pivotal Cloud Native roadshow and teaches developers how to build modern cloud-native applications using the popular JVM-based application framework: Spring Boot. You'll be provided with a walk through from the monolith application architecture into the more modern microservices architecture. Two open source reference architectures are introduced for building cloud-native microservices. Learn the basics of cloud native platforms and also the approaches for integrating and strangling legacy systems.
https://pivotal.io/event/pivotal-cloud-native-roadshow
Extending the Platform with Spring Boot and Cloud FoundryKenny Bastani
When developing cloud native applications that are deployed and operated using a cloud platform, such as Cloud Foundry, there becomes a need to provision middleware services using the platform. The result of building platform services are that developers using the platform are able to take advantage of service offerings as bindings for their application deployments.
Back your app with MySQL and Redis on Cloud FoundryKenny Bastani
In this session, we will build a minimum viable Spring Data web service with REST API, add a MySQL backing service as the primary data store, and a Redis Labs backing service for caching. We will demonstrate performance metrics without Redis caching enabled and then with Redis caching enabled. I will also provide an intro-level explanation of the platform capabilities within Pivotal Web Services
Using Docker, Neo4j, and Spring Cloud for Developing MicroservicesKenny Bastani
In this talk we will explore a sample microservice architecture that uses Spring Boot, Docker, and Neo4j to discover similar users on Twitter. We will dive into the architecture and talk about how the application uses Spring Cloud to add service discovery and an API gateway to help services communicate. Finally we will take a look at how to use Docker Compose to run the multi-container application, using Docker Hub distributions of Neo4j and Apache Spark for graph processing and ranking of Twitter users.
In this talk, Kenny Bastani will introduce you to Spring Cloud, a set of tools for building cloud-native JVM applications. We will take a look at some of the common patterns for microservice architectures and how to use Cloud Foundry to deploy multiple microservices to the cloud. We will also dive into a microservices example project of a cloud-native application built using Spring Boot and Spring Cloud. Using this example project, I'll show you how to use Cloud Foundry to spin up a microservice cluster. We will then explore what a cloud-native application looks like when using self-describing REST APIs that link multiple microservices together.
Building REST APIs with Spring Boot and Spring CloudKenny Bastani
In this talk I will introduce you to Spring Cloud, a set of tools for building cloud-native JVM applications. We will take a look at some of the common patterns for microservice architectures and how to use Cloud Foundry to deploy multiple microservices to the cloud.
We will also dive into a microservices example project of a cloud-native application built using Spring Boot and Spring Cloud. Using this example project, I'll show you how to use Lattice to spin up a microservice cluster on AWS. We will then explore what a cloud-native application looks like when using self-describing REST APIs that link multiple microservices together.
Open Source Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You’ll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Graphs are a perfect solution to organize information and to determine the relatedness of content. Neo4j Developer Evangelist Kenny Bastani will discuss using Neo4j to perform document classification and text classification using a graph database. Kenny will demonstrate how to build a scalable architecture for classifying natural language text using a graph-based algorithm called Hierarchical Pattern Recognition. This approach encompasses a set of techniques familiar to Deep Learning practitioners.
Kenny demonstrates how to build a flexible and expressive graph model and related queries that map closely to your domain needs, and which can be evolved as your application evolves.
As companies like Facebook and Google have introduced us to Graph Search and the Knowledge Graph, developers are learning the benefits of graph database architectures. Graph databases, like Neo4j, have increased in popularity by nearly 250% from last year - the highest among all other DBMS categories, according to db-engines.com. Join Kenny Bastani as we look at the benefits of using a graph database, explore various use cases and walkthrough creating a movie recommendation app on Neo4j 2.0.
Recent natural language processing advancements have propelled search engine and information retrieval innovations into the public spotlight. People want to be able to interact with their devices in a natural way. In this talk I will be introducing you to natural language search using a Neo4j graph database. I will show you how to interact with an abstract graph data structure using natural language and how this approach is key to future innovations in the way we interact with our devices.
Recent natural language processing advancements have propelled search engine and information retrieval innovations into the public spotlight. People want to be able to interact with their devices in a natural way. In this talk I will be introducing you to natural language search using a Neo4j graph database. I will show you how to interact with an abstract graph data structure using natural language and how this approach is key to future innovations in the way we interact with our devices.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
8. Using Meetup as an example use case
Meetup.com is a valuable source of data for
understanding trends around products or brands.
Understanding demand is key for delivering compelling
content at meetups.
It sounded like a great use case for Neo4j.
9. The Problem
Track meetup group growth over time.
Apply tags to meetup groups and report combined
growth of all groups over time.
11. Question #1
Given a start date and an end date, what is the time
series that plots the membership growth of a given
meetup group?
12. Question #2
Given a start date, an end date, and a combination of
tags, what is the time series that plots the combined
membership growth of all meetup groups with those
tags?
13. Question #3
How do you generate the JSON data of a time series
for a basic JS line chart plugin?
15. The GraphGist Project
The GraphGist project is a way to quickly build a
graph-based proof of concept on Neo4j.
I started with a GraphGist.
Neo4j for Graph Analytics: Meetup.com Example
25. Tackling Time in Neo4j
How do you implement a time series in Neo4j?
For any node that represents a unit of time, use a
timestamp. Traversals can be costly for selecting time
series. Expose a REST API that takes a normal date format
and then convert it to an integer that allows you to select a
range of dates in your Neo4j Cypher query.
For any node that represents a unit of time, use a timestamp. Traversals can be costly for selecting time series. Expose a REST API that takes a normal date format and then convert it to a Int32 that allows you to select a range of
dates.
26. Scale it up!
It started with a GraphGist and then I said “Why not?”
let’s build something cool using Neo4j.
27. Challenges
I decided to take my GraphGist and make a full
platform.
There were some challenges.
28. Challenge #1
How do I get historical Meetup group statistics for all
groups?
33. What do I want to know?
Assuming I had as much historical Meetup data as I
pleased, what kind of questions would I want to ask
about that data?
How would I want to present it?
34. What’s the combined growth percent of Meetup
groups having a certain topic?
This chart plots a line chart of the time series for a meetup group topic on Meetup.com. Each group on Meetup.com has a set of topics associated with it. This chart is meant to show the percent growth month over month.
35. What’s the cumulative growth of Meetup groups with
a specific topic?
This chart plots a bar chart of the cumulative growth of a meetup group topic on Meetup.com. Using the time series data of monthly growth from the Meetup Tag Growth % chart, the growth percents over the period are
aggregated into a sum for each topic. This chart shows total growth percentage over the period.
36. What’s the relative growth of Meetup groups with a
topic for a date range?
This chart plots an Donut Chart of the relative cumulative growth of a meetup group topic on Meetup.com. Using the data from Cumulative Meetup Growth, the percentage growth of each topic over the period is compared relative
to one another as a ratio of 100.
37. How many groups does a topic have relative to
others?
This chart plots an Donut Chart of the number of groups in the region during the period for each topic. Each group is compared relative to one another as a ratio of 100.
38. What’s the growth percent of all groups for a topic in
a location for a date range?
This report is a simple table that shows the growth percent of all groups for a topic broke down by location. What do these high percentages tell us about Meetup? Within the last year there has been massive growth for meetup
groups that are focused on NoSQL database technology. If I imported a different topic, not related to technology, what would the data show?
39. How do I give users a clean set of controls to filter
and search?
41. Architecture
Front-end web-based dashboard in Node.js and
bootstrap
REST API via Neo4j Swagger in Node.js
Data import services in Node.js
Data storage in Neo4j graph database
47. REST API
The REST API is a fork of Neo4j Swagger. Swagger is
a specification and complete framework
implementation for describing, producing, consuming,
and visualizing RESTful web services.
50. The Neo4j Swagger Project
The Swagger project was modified to use Neo4j as its
data source. The REST API module of this project is
extended from the Neo4j swagger project.
51. REST API Methods
Get Weekly Growth
Get Monthly Growth
Get Monthly Growth By Tag
Get Monthly Growth By Location
Get Cities
Get Countries
Get Group Count By Tag
52. Get Weekly Growth
Gets the weekly growth percent of meetup groups as
a time series. Returns a set of data points containing
the week of the year, the meetup group name, and
membership count.
53. Get Monthly Growth
Gets the monthly growth percent of meetup groups as
a time series. Returns a set of data points containing
the month of the year, the meetup group name, and
membership count.
54. Get Monthly Growth By Tag
Gets the monthly growth percent of meetup group
tags as a time series. Returns a set of data points
containing the month of the year, the meetup group
tag name, and membership count.
55. Get Monthly Growth By Location
Gets the monthly growth percent of meetup group
locations and tags as a time series. Returns a set of data
points containing the month of the year, the meetup
group tag name, the city, and membership count.
56. Get Cities
Gets a list of cities that meetup groups reside in.
Returns a distinct list of cities for typeahead.
57. Get Countries
Gets a list of countries that meetup groups reside in.
Returns a distinct list of countries for typeahead.
58. Get Group Count By Tag
Gets a count of groups by tag. Returns a list of tags
and the number of groups per tag.
59. Analytics Dashboard
The dashboard is a web application that uses client-
side JavaScript to communicate with the Neo4j
Swagger REST API to populate a series of interactive
chart controls with data. This web application uses
bootstrap for the front-end styles and highcharts.js for
the charting controls.
61. Reports
Meetup Tag Growth %
Cumulative Meetup Growth
Category Growth %
Groups By Tag
Meetup Tag Growth By Location
62. Meetup Tag Growth %
https://github.com/kbastani/meetup-analytics/blob/master/docs/DOCS.md#meetup-tag-growth-
This chart plots a line chart of the time series for a meetup group topic on Meetup.com. Each group on Meetup.com has a set of topics associated with it. This chart is meant to show the percent growth month over month.
66. Meetup Tag Growth By Location
This report is a simple table that shows the growth percent of all groups for a topic broke down by location. What do these high percentages tell us about Meetup? Within the last year there has been massive growth for meetup
groups that are focused on NoSQL database technology. If I imported a different topic, not related to technology, what would the data show?
75. touch
Twitter:
@kennybastani
LinkedIn:
/in/kennybastani
Email:
kenny.bastani@neotechnology.com
“Get in touch with me about meetups and Neo4j community events happening around the world.”
!
“I’ll now open up the floor to questions.”