Why should you invest in LEO CDP ?
Purpose: Big data and AI democracy for SMEs companies
Problem: Customer Analytics and Customer Personalization
Solutions: CDP + CX + Personalization Engine
Product demo: LEO CDP for Ecommerce and Fintech
Business model: Freemium → Ecosystem → Subscription
Market size: 20 billion USD in 2026 and CAGR 34.6%
Differentiation: cloud-native software
Go-to-market approach: Community → Free → Paid
Team: 1 full-stack dev, 1 data scientist and 12,000 fans of BigDataVietnam.org Community
Need 150,000 USD for scaling business (you get 20% share)
Webinar | Good Guys vs. Bad Data: How to Be a Data Quality HeroAngela Sun
Duplicate contact and account records. Missing field values. Inaccurate or outdated information. Unstandardized data. Typos upon typos. If you manage and operate sales and marketing tools, you’ve likely encountered these bad data scenarios (and others!).
We get it — data quality isn’t the sexiest topic. But the impact of poor data quality is undeniable, causing 21% of marketing budgets to be wasted, according to research from Forrester and Marketing Evolution. Furthermore, factors like increasing competition and evolving buyer needs continue to make data health even more important. Improving data quality can stretch your marketing dollars further, enable operational efficiency, and act as a strategic growth lever.
Tim Liu (Head of Product at Hull) and Brad Smith (Co-founder & CEO at Sonar) have spent their entire careers working in data integration and operations, so they’ve seen it all. In this webinar, they’ll share:
- Data quality nightmares they’ve personally dealt with
- Common scenarios where bad data can rear its ugly head
- Proactive strategies for getting ahead
From Data Analytics to Fast Data IntelligenceTrieu Nguyen
1) How to understand users with Data Analytics ?
2) How to build Real-time Music Recommender System from Data Stream ?
3) How to boost profit with Cross Sale in Real-time ?
Key Ideas to build Fast Data Intelligence Platform from Open Source Tools:
+ Apache Kafka
+ Apache Spark
+ RFX framework
Parallel and Iterative Processing for Machine Learning Recommendations with S...MapR Technologies
Recommendation systems help narrow your choices to those that best meet your particular needs. They are among the most popular applications of big data processing. In this Free Code Friday session, you’ll learn how to build a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.
Why should you invest in LEO CDP ?
Purpose: Big data and AI democracy for SMEs companies
Problem: Customer Analytics and Customer Personalization
Solutions: CDP + CX + Personalization Engine
Product demo: LEO CDP for Ecommerce and Fintech
Business model: Freemium → Ecosystem → Subscription
Market size: 20 billion USD in 2026 and CAGR 34.6%
Differentiation: cloud-native software
Go-to-market approach: Community → Free → Paid
Team: 1 full-stack dev, 1 data scientist and 12,000 fans of BigDataVietnam.org Community
Need 150,000 USD for scaling business (you get 20% share)
Webinar | Good Guys vs. Bad Data: How to Be a Data Quality HeroAngela Sun
Duplicate contact and account records. Missing field values. Inaccurate or outdated information. Unstandardized data. Typos upon typos. If you manage and operate sales and marketing tools, you’ve likely encountered these bad data scenarios (and others!).
We get it — data quality isn’t the sexiest topic. But the impact of poor data quality is undeniable, causing 21% of marketing budgets to be wasted, according to research from Forrester and Marketing Evolution. Furthermore, factors like increasing competition and evolving buyer needs continue to make data health even more important. Improving data quality can stretch your marketing dollars further, enable operational efficiency, and act as a strategic growth lever.
Tim Liu (Head of Product at Hull) and Brad Smith (Co-founder & CEO at Sonar) have spent their entire careers working in data integration and operations, so they’ve seen it all. In this webinar, they’ll share:
- Data quality nightmares they’ve personally dealt with
- Common scenarios where bad data can rear its ugly head
- Proactive strategies for getting ahead
From Data Analytics to Fast Data IntelligenceTrieu Nguyen
1) How to understand users with Data Analytics ?
2) How to build Real-time Music Recommender System from Data Stream ?
3) How to boost profit with Cross Sale in Real-time ?
Key Ideas to build Fast Data Intelligence Platform from Open Source Tools:
+ Apache Kafka
+ Apache Spark
+ RFX framework
Parallel and Iterative Processing for Machine Learning Recommendations with S...MapR Technologies
Recommendation systems help narrow your choices to those that best meet your particular needs. They are among the most popular applications of big data processing. In this Free Code Friday session, you’ll learn how to build a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.
Building Reactive Real-time Data PipelineTrieu Nguyen
Topic: Building reactive real-time data pipeline at FPT ?
1) What is “Data Pipeline” ?
2) Big Data Problems at FPT
+ VnExpress: pageview and heat-map
+ eClick: real-time reactive advertising
3) Solutions and Patterns
4) Fast Data Architecture at FPT
5) Wrap up
Before the Web...
Then came the Web...
Then happened Web2.0...
How Web2.0 Got its Name
Web2.0: An Overview
Web2.0: Web as a Platform
Web2.0: Harnessing Collective Intelligence
Web2.0: Rich User Experience
Web2.0: Visual Design?
Web2.0: Design Patterns
Web2.0: What is proprietary? What is the biz model?
Web2.0: Beyond the web, beyond the community: Web3?
Web2.0: Implications for Media
Are we going into a Bubble?
Some creative Web2.0 applications?
Nimish Vohra, Regalix
How I can a design website farm step by step.Website farm: On the Internet, a Web server farm, or simply Web farm, may refer to a Web site that uses two or more servers to handle user requests. Typically, serving user requests for the files (pages) of a Web site can be handled by a single server. However, larger Web sites may require multiple servers.
Web 2.0 has brought new life to the Internet, providing a more interactive user experience that is comparable to fat client desktop applications. Web 2.0 introduces new types of services that are designed to share presentation and multimedia objects (presentation services) and give the ability to develop a new application by combining services from different sites or applications (e.g. through Mashups). One of the popular components of today’s Web 2.0 is Rich Internet Applications (RIAs). They include new features to process information and interactive user interfaces based on Ajax, Flex, JavaFX, Silverlight, etc. The user no longer has to wait for a new page to load whenever he makes a request; some information retrieval now happens asynchronously in the background while the user is interacting with the GUI, while other requests can be managed by logic that runs on the client without requiring calls over the Internet to the server side. However, what users see is merely a facade. It is powered by server-side distributed Business services that process data according to client requests. The question then becomes how services should be designed to support the requirements of RIA clients. We will look at RIA support services that are included in a SOA infrastructure and are managed through SOA governance tools.
STUDY OF DEEP WEB AND A NEW FORM BASED CRAWLING TECHNIQUEIAEME Publication
The World Wide Web, abbreviated as WWW is global information medium interlinked with hypertext documents accessed via the internet. In a web browser a user can easily search the content by simply filling up a form. As the amount of information in the web is increasing drastically, the search result needs to be increased and it depends completely on the searching engine and the search engines are only as good as the web crawlers that serve up content for the result.
The paper gives an idea of a new hidden web crawling technique that is concerned with filling forms with meaningful values in order to get an appropriate search results
Mainstream Development is a growing international IT company headquartered in Minsk, Belarus (Eastern Europe). Now our team consists of 25+ developers.
We specialize primarily in mobile (iOS, Android, Windows Phone) and Web application development ( NET,SharePoint, LAMP, ROR) based on customer specifications.
Here you can see the presentation with selected projects.
More info you can get from our website http://mainstreamdevelopment.biz/
Feel free to ask questions :)
mail id: anna.vyrostak@mainstreamdevelopment.biz
skype id: anna.vyrostak
Building Reactive Real-time Data PipelineTrieu Nguyen
Topic: Building reactive real-time data pipeline at FPT ?
1) What is “Data Pipeline” ?
2) Big Data Problems at FPT
+ VnExpress: pageview and heat-map
+ eClick: real-time reactive advertising
3) Solutions and Patterns
4) Fast Data Architecture at FPT
5) Wrap up
Before the Web...
Then came the Web...
Then happened Web2.0...
How Web2.0 Got its Name
Web2.0: An Overview
Web2.0: Web as a Platform
Web2.0: Harnessing Collective Intelligence
Web2.0: Rich User Experience
Web2.0: Visual Design?
Web2.0: Design Patterns
Web2.0: What is proprietary? What is the biz model?
Web2.0: Beyond the web, beyond the community: Web3?
Web2.0: Implications for Media
Are we going into a Bubble?
Some creative Web2.0 applications?
Nimish Vohra, Regalix
How I can a design website farm step by step.Website farm: On the Internet, a Web server farm, or simply Web farm, may refer to a Web site that uses two or more servers to handle user requests. Typically, serving user requests for the files (pages) of a Web site can be handled by a single server. However, larger Web sites may require multiple servers.
Web 2.0 has brought new life to the Internet, providing a more interactive user experience that is comparable to fat client desktop applications. Web 2.0 introduces new types of services that are designed to share presentation and multimedia objects (presentation services) and give the ability to develop a new application by combining services from different sites or applications (e.g. through Mashups). One of the popular components of today’s Web 2.0 is Rich Internet Applications (RIAs). They include new features to process information and interactive user interfaces based on Ajax, Flex, JavaFX, Silverlight, etc. The user no longer has to wait for a new page to load whenever he makes a request; some information retrieval now happens asynchronously in the background while the user is interacting with the GUI, while other requests can be managed by logic that runs on the client without requiring calls over the Internet to the server side. However, what users see is merely a facade. It is powered by server-side distributed Business services that process data according to client requests. The question then becomes how services should be designed to support the requirements of RIA clients. We will look at RIA support services that are included in a SOA infrastructure and are managed through SOA governance tools.
STUDY OF DEEP WEB AND A NEW FORM BASED CRAWLING TECHNIQUEIAEME Publication
The World Wide Web, abbreviated as WWW is global information medium interlinked with hypertext documents accessed via the internet. In a web browser a user can easily search the content by simply filling up a form. As the amount of information in the web is increasing drastically, the search result needs to be increased and it depends completely on the searching engine and the search engines are only as good as the web crawlers that serve up content for the result.
The paper gives an idea of a new hidden web crawling technique that is concerned with filling forms with meaningful values in order to get an appropriate search results
Mainstream Development is a growing international IT company headquartered in Minsk, Belarus (Eastern Europe). Now our team consists of 25+ developers.
We specialize primarily in mobile (iOS, Android, Windows Phone) and Web application development ( NET,SharePoint, LAMP, ROR) based on customer specifications.
Here you can see the presentation with selected projects.
More info you can get from our website http://mainstreamdevelopment.biz/
Feel free to ask questions :)
mail id: anna.vyrostak@mainstreamdevelopment.biz
skype id: anna.vyrostak
How to track and improve Customer Experience with LEO CDPTrieu Nguyen
1) Why CX measurement is so important
2) Introduction to key metrics of CX
2.1 Customer Feedback Score (CFS)
2.2 Customer Effort Score (CES)
2.3 Customer Satisfaction Score (CSAT)
2.4 Net Promoter Score (NPS)
3) Using Journey Map to CX Data Management
4) Introduction to LEO CDP and demo
[Notes] Customer 360 Analytics with LEO CDPTrieu Nguyen
Part 1: Why should every business need to deploy a CDP ?
1. Big data is the reality of business today
2. What are technologies to manage customer data ?
3. The rise of first-party data and new technologies for Digital Marketing
4. How to apply USPA mindset to build your CDP for data-driven business
Part 2: How to use LEO CDP for your business
1. Core functions of LEO CDP for marketers and IT managers
2. Data Unification for Customer 360 Analytics
3. Data Segmentation
4. Customer Personalization
5. Customer Data Activation
Part 3: Case study in O2O Retail and Ecommerce
1. How to build customer journey map for ecommerce and retail
2. How to do customer analytics to find ideal customer profiles
The ideal customer profile in a B2B context
The ideal customer profile in a B2C context
3. Manage product catalog for customer personalization
4. Monitoring Data of Customer Experience (CX Analytics)
CX Data Flow
CX Rating plugin is embedded in the website, to collect feedback data
An overview of CX Report
A CX Report in a customer profile
5. Monitoring data with real-time event tracking reports
Event Data Flow
Summary Event Data Report
Event Data Report in a Customer Profile
Part 4: How to setup an instance of LEO CDP for free
1. Technical architecture
2. Server infrastructure
3. Setup middlewares: Nginx, ArangoDB, Redis, Java and Python
Network requirements
Software requirements for new server
ArangoDB
Nginx Proxy
SSL for Nginx Server
Java 8 JVM
Redis
Install Notes for Linux Server
Clone binary code for new server
Set DNS hosts for LEO CDP workers
4. Setup data for testing and system verification
Part 5: Summary all key ideas
Lộ trình triển khai LEO CDP cho ngành bất động sảnTrieu Nguyen
1) Hiểu bài toán số hoá trải nghiệm khách hàng
2) Nghiên cứu giải pháp LEO CDP
3) Lộ trình triển khai
Phát triển / số hoá điểm chạm khách hàng
Xây dựng bản đồ hành trình khách hàng
Định nghĩa các metrics và KPI quan trọng
Xây dựng web portal và mobile data hub
Xây dựng kế hoạch Digital Marketing
Triển khai CDP và Marketing Automation
Xây dựng đội Analytics để phân tích dữ liệu
From Dataism to Customer Data PlatformTrieu Nguyen
1) How to think in the age of Dataism with LEO CDP ?
2) Why is Dataism for human, business and society ?
3) How should LEO Customer Data Platform (LEO CDP) work ?
4) How to use LEO CDP for your business ?
Data collection, processing & organization with USPA frameworkTrieu Nguyen
1) How to think in the age of Dataism with USPA framework ?
2) How to collect customer data
3) Data Segmentation Processing for flexibility and scalability
4) Data Organization for personalization and business activation
Part 1: Introduction to digital marketing technologyTrieu Nguyen
Outline of this course
1. Digital Media Models in the age of marketing 4.0
2. Strategic Thought as It Relates to Digital Marketing
3. Web: The Center of Digital Marketing Delivery Mix
4. Content Management System (CMS) and headless CMS
5. Search Engine Marketing
6. Email Marketing
7. Social Media and Mobile Marketing
8. Introduction to Advertising Technology (Ad Tech)
9. Introduction to Customer Database and Customer Data Platform (CDP)
10. Legal Issues: Data privacy, Security, and Intellectual Property
11. Case study: IKEA - from business strategy to digital marketing strategy
12. Recommended books for self-study
Transform your marketing and sales capabilities with Big Data and A.I
1) Why is Customer Data Platform (CDP) ?
Case study: Enhancing the revenue of your restaurant with CDP and mobile app marketing
Question: Why can CDP disrupt business model for restaurant industry (B2C) ?
2) How would CDP work in practice ?
Introducing USPA.tech as logical framework for implementing CDP in practice
How Can a Customer Data Platform Enhance Your Account-Based Marketing Strategy (B2B) ?
3) How can we implement CDP for business?
Introducing the CDP as customer-first marketing platform for all industries (my key idea in this slide)
Video Ecosystem and some ideas about video big dataTrieu Nguyen
Introduction to Video Ecosystem Mind Map
Video Streaming Platform
Video Ad Tech Platform
Video Player Platform
Video Content Distribution Platform
Video Analytics Platform
Summary of key ideas
Q & A
Concepts, use cases and principles to build big data systems (1)Trieu Nguyen
1) Introduction to the key Big Data concepts
1.1 The Origins of Big Data
1.2 What is Big Data ?
1.3 Why is Big Data So Important ?
1.4 How Is Big Data Used In Practice ?
2) Introduction to the key principles of Big Data Systems
2.1 How to design Data Pipeline in 6 steps
2.2 Using Lambda Architecture for big data processing
3) Practical case study : Chat bot with Video Recommendation Engine
4) FAQ for student
Apache Hadoop and Spark: Introduction and Use Cases for Data AnalysisTrieu Nguyen
Growth of big datasets
Introduction to Apache Hadoop and Spark for developing applications
Components of Hadoop, HDFS, MapReduce and HBase
Capabilities of Spark and the differences from a typical MapReduce solution
Some Spark use cases for data analysis
Introduction to Recommendation Systems (Vietnam Web Submit)Trieu Nguyen
1) Why do we need recommendation systems ?
2) How can we think with recommendation systems ?
3) How can we implement a recommendation system with open source technologies ?
RFX framework https://github.com/rfxlab
Apache Kafka: https://kafka.apache.org
Apache Spark: https://spark.apache.org
Giới thiệu cơ bản về Big Data và các ứng dụng thực tiễnTrieu Nguyen
1. Các ứng dụng Big Data thực tiễn trên thế giới
2. Các lĩnh vực đang ứng dụng Big Data ở Việt
Nam
3. Các bài toán Big Data tiêu biểu ở Vietnam
a. Quản lý chăm sóc khách hàng (CRM)
b. Tối ưu hoá trải nghiệm truyền hình Internet
c. Quảng cáo trực tuyến AdsPlay.net
4. Giới thiệu về công việc và thị trường việc làm
Big Data ở Việt Nam
5. Kiến thức nền tảng cho các bạn sinh viên
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. Introduction to RFX for
Backend Developer
Reactive Function (X)
the Open Source Framework for solving Fast Data Problem
and reacting to the World with Deep Learning
By Triều Nguyễn, the creator of RFX
http://mc2ads.com (Reactive Big Data Lab)
λ(x)
2. 2008: Java Developer, develop Social Trading Network for a
startup (Yopco)
2011: joined FPT Online, software engineer, worked in banbe.
net social network and VnExpress Mobile Restful API
2012: backend engineer at Greengar Studios
12/2012 to now - back to FPT Online, lead engineer,
developed new version Data Analytics Platform (ad-network
eclick.vn and VnExpress News)
Introduction about myself
3. 1. What is Rfx ?
2. Inception and Ideas
3. How Rfx was born
4. Why is Rfx ?
a. from big picture view (business)
b. from business view
c. from specific problems
5. Concepts and architecture: The BIG picture
6. Coding and tutorials from practical problems
7. Resources for self-studying
Contents of this talk
4. ● A framework for reactive real-time big fast data
● A collection of Open Source Tools
● The mission of RFX
→ “BUILD digital data-driven brain for every company in the
World”
What is RFX or Reactive Function X ?
5. INCEPTION and Ideas
Ideas when I was student, internship at DRD,
non-profit Organization
More info at http://activefunctor.blogspot.com
11. Why Rfx ?
● Ideas since 2007 (from Haskell and Actor model theory)
● R&D and Deployed in Production since 2013
● Open Source: Apache License, Version 2.0
● Full Stack: Front-end and Back-end
● Apply Agile for Analytics and Data Science
● Apply Reactive Lambda Architecture
● Really fast and near-real-time processing
● Tested with 1.000.000 logs / second (1 million in 1 second)
● Simple development model for big data developer
13. Domain (in business) where Rfx can be used
● real-time data analytics for digital marketing, advertising
● hospital systems
● personal banking system
● financial institution to detect frauds
● manufacturing plant
● airline systems
● online trading system
● emergency control system
● manufacturing plant management system
● road tolling system detects
● social networking site
15. Problem: How to monitor Mobile Web Performance and react
to slow response time
http://sixrevisions.com/mobile/pay-attention-to-mobile-web-performance
16. Luggage management system, events are produced by the check-in process
and by the various radio-frequency identification (RFID) readers, which emit
events about the movement of the luggage in the system. The events
generated by the event processing system are consumed by the luggage
control system itself, by airport staff, or even by the passengers themselves.
Problem: Monitoring sensor data and real-time security checking
17. Actor
User, Mobile,
Browser, ...
Reactive Lambda Architecture
System Rfx-
Topology
data + context + metadata
useful (data + relationship)
Database
NoSQL
1. Actor → System
2. System → Database
3. Database → System
4. System → Actor
18. Concepts
● Each user, who uses the services and creates data, is the
actor in system
● Actor is the source of all events (aka: logs), (click, reading
news, sending message to friends, playing games, ...)
● Functor (aka: neuron) is a computing object, used for
storing, processing data and emitting results to subscribed
functors
● Topology is the directed graph, define how functors that
are connected with stream data and process data
21. There are 3 demos, from simple to advanced user story
User story 1: Counting Real-time URL Pageview
User story 2: Monitoring Social Media Statistics
User story 3: Social Ranking for Recommendation Engine
22. User Story
Domain problem: Reactive Real-time Marketing
User story’s details:
1. User does read news from a website
→ tracking user activities (pageview, time on site)
2. User does login with Facebook Account
3. User clicks on like Facebook button
→ tracking what user liked, commented
4. The marketer/data analyst should see the trending most
read article in real-time
● → Personalized articles for the reader
● → Native advertising in real-time
23.
24. Demo user story 1: Counting Real-time URL Pageview
Input:
1. The pageview logs from HTTP
Output:
1. The total number of page-view
2. The total number of page-view per hour
3. The total number of page-view per minute
4. The total number of page-view per second
5. The total number of page-view for URL
25.
26. Demo user story 2: Monitoring Social Media Statistics
Input:
1. The pageview logs from HTTP
Output:
The social media statistics from:
1. Facebook: Like, Share, Comment
2. Twitter: Tweet Count
3. LinkedIn: Share Count
4. Geolocation heat-map report
27.
28. Demo user story 3:
Social Ranking for Recommendation Engine
Input:
● Data: the URL of article
● Context: where (User's Location), when (time visit), from
where (referer url)
● Metadata: keywords, category of article
Output:
Real-time Statistics about pageview, social media statistics
(Share, Like, Comment), recommended articles
The list of articles are ranked by:
● most liked and same category
● most viewed and same category
● most liked, same category and near user's location
29.
30. Reference Resources
Main website for Rfx: http://www.mc2ads.com
Ideas:
● http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00112/full
● http://singularityhub.com/2014/04/20/new-imaging-method-shows-young-neurons-making-
connections-exchanging-information
● http://en.wikipedia.org/wiki/Actor_model_theory
● http://java.dzone.com/articles/introduction-event-processing
● http://www.technologyreview.com/featuredstory/526501/brain-mapping
● http://www.technologyreview.com/featuredstory/513696/deep-learning
Apache Storm: http://storm.incubator.apache.org
Apache Kafka: http://kafka.apache.org
In-memory NoSQL: http://redis.io
Deep Learning for Java: http://deeplearning4j.org
Distributed processing with Actor Model: http://akka.io
Papers:
● Real-Time Visualization of Streaming Text Data
● Hypernetworks for the Science of. Complex Systems
Main Blogs: http://www.mc2ads.org
31. The end and thank you
https://github.com/mc2ads/rfx
http://www.mc2ads.org
λ(x)