The 12 characteristics of modern application development

Here are 12 attributes that characterize the new mode of application development beginning to make inroads in enterprises:

1. Mobile: Supporting touch interaction and adaptability .
2. Cloud-Backed: for back ends in the form of PaaS.
3. Agile: Incremental, frequent releases. Rapid response to change. 
4. Continuously Integrated and Delivered: Applications must be integrated to run daily. Heavy reliance on automated testing.
5. DevOps-Enabled: Developers own deployment or work seamlessly with Ops staff to release, test, refine and rerelease applications to users. 
6. App Store Delivered and Extended: 
7. Reactive Analytics Infused: Developers get rich intelligence on application usage. With automation and quick turnaround (e.g. A/B testing of planned features), reactive analytics could display optimized UX and personalized information for user
8. User Experience-Centric: Focusing on the design and appearance of the application 
9. Socially Oriented: Integrating of user-interface patterns borrowed from social networks, such as timelines, event streams, social graphs and other social metadata. Data is updated using event-based push-oriented patterns. Integrated search functionality. Seamless support for content elements such as images and video. Integration with consumer or enterprise social networks.
10. API Factored and Surfaced: use standardized, loosely coupled and lightweight REST call formats to reduce complexity and maximize accessibility.
11. Lightweight: Less complex software that is less time-consuming to install, learn and use. 
12. Model-Driven: Rich use of visual tools to support abstraction in the development toolset such as for relations in a data model, business logic flows, and process flows. 

Refer: http://sdtimes.com/analyst-watch-12-characteristics-modern-application-development/

Ad Serving Technology - well-written book for beginners in Ad-tech industry

A clear and concise 400 page introduction to the world of ad serving and digital advertising technology. From an introduction to the entities and infrastructure that enables digital advertising to full coverage of the roles, responsibilities and processes  involved in Media Planning, Buying, Procurement, Creative development, Campaign Setup, Trafficking, Campaign execution, Report Analysis and Optimization.
Buy it here 
http://www.amazon.com/Serving-Technology-Understand-revelation-commercialized/dp/1484867572/ref=mc2ads.com

Do not put all stupid banner ads on my screen !

http://www.mc2ads.com/2014/12/how-does-reactive-marketing-really-work.html

The Future of Content: The Death of Organic and the Rise of Paid and Programmatic - DBS, 12/7/14 from Digiday

How to solve  it ?

Content Marketing + Reactive Analytics + Native Ad => Native Promoted Content

Media Consumer Report - Vietnam

Nielsen South East Asia - New Media Consumer from Trieu Nguyen

How does Reactive Marketing really work in practice ?

Reactive Analytics for data-driven business

Building your data driven business with reactive marketing technology from Trieu Nguyen

How we would apply reactive analytics in practice ? 

I think the marketing in e-commerce would be got lots of benefits

1. you can recommend best matched items for user in real-time, 
2. you can know the interest of user and send to them a email about new promotions with personalized information. 
3. The most important, you can create a new trend, which is easily viral in all social media. 

Test case with Lazada.vn

Simple demo with Lazada and visualizing user's heat-map data

The architecture of Real-time Reactive Marketing platform

How our Lambda Architecture could fail ?

After coming to the meetup event,  http://www.grokkingengineering.org, it's very informative event and lots of useful information. Then I have some new ideas , that could solve problems I get during development and deployment using Lambda Architecture in 1 year production at FPT Online, where tons of data hit the server and all system have to make decisions, checking, booking, mining , bidding in near-real-time.
If you miss this talk, you can check this link for more details http://engineering.viki.com/blog/2014/data-warehouse-and-analytics-infrastructure-at-viki/

Some main issues when deploying and implementing the lambda arch in practice:

not Agile enough "The problem with the Lambda Architecture is that maintaining code that needs to produce the same result in two complex distributed systems is exactly as painful as it seems like it would be. I don’t think this problem is fixable."

Refer link http://radar.oreilly.com/2014/07/questioning-the-lambda-architecture.html

=> Keep it simple as possible, but not simpler

We are in the dawn of "Fast Data"
https://www.google.com/events/io/schedule/session/793fe228-89c5-e311-b297-00155d5066d7
http://radar.oreilly.com/2014/12/fast-data-calls-for-new-ways-to-manage-its-flow.html
http://www.infoworld.com/article/2608040/big-data/fast-data--the-next-step-after-big-data.html
http://www.wired.com/2013/04/big-data-fast-data-smart-data/

Using User Tracking with real-time decisions
http://www.technologyreview.com/view/532556/new-technology-for-tracking-consumers-across-devices-grows-results/
http://en.wikipedia.org/wiki/Real-time_business_intelligence

=> Fast data is really popular in practice, real-time marketing and marketing automation is the new trend 

I made simple diagram here, for easy understanding, combines cool ideas from http://www.reactive-streams.org with Lambda Architecture to create a new system that reacts faster with 2 types of new data:

• behavioral data
 (game, interests, sessions, touch, ...)

• context data 
(location, time, referrer, lead ,...)

http://www.mindmeister.com/450339578

Apply Marketing Automation with RFX framework

Reading List: Ad, Data-Driven Marketing, Analytic, Big Data

BEST PRACTICES FOR Advertising

https://retargeter.com/blog/retargeting/8-best-practices-for-running-a-retargeting-campaign

https://retargeter.com/blog/banner-best-practices/3-tips-banner-ad-design

https://retargeter.com/blog/retargeting/a-brief-introduction

Vietnam and Big Data

http://businesstoday.intoday.in/story/lessons-in-big-data-vietnam-apac-big-data-and-cloud-summit/1/197954.html

Data-Driven Marketing + Advertising + Big data

http://www.emarketer.com/Article/Using-Big-Data-Still-Challenge-Marketers/1010710

http://www.telegraph.co.uk/sponsored/technology/4g-mobile/engaging-customers/11070094/attract-right-audience-big-data.html

http://www.slideshare.net/tantrieuf31/machine-learning-on-big-data-for-personalized-internet-advertising

Data-driven for brand marketing

http://www.forbes.com/sites/work-in-progress/2012/12/18/10-brand-marketing-trends-that-should-dominate-2013/

http://thenextweb.com/socialmedia/2014/09/11/emotion-vs-data-driven-marketing-biggest-challenges/

Advertising 2.0 and Trends

http://www.slideshare.net/damjanov/future-of-adland-futureofdigital-2014

https://hbr.org/2013/03/advertising-analytics-20

A/B Split testing for Maximize ROI Ad

http://blog.wishpond.com/post/64393992717/how-to-a-b-split-test-your-facebook-ads-to

Apply Data-driven Marketing for Retails

http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html

http://www.retailcustomerexperience.com/articles/how-retailers-can-turn-big-data-into-real-insights/

Intro PROGRAMMATIC Advertising

https://retargeter.com/blog/strategy-2/programmatic-buying-intro

Programmatic buying and native advertising

http://marketingland.com/programmatic-native-content-data-driven-marketing-can-co-exist-80026

HTML5 for universal mobile Advertising

http://mashable.com/2010/09/24/html5-mobile-advertising/

Big Data for traditional media: TVs, Internet TV

http://www.theguardian.com/media-network/media-network-blog/2014/aug/29/tv-advertising-big-data

What is reactive marketing ? And why ?

We would begin with the basic concept, what is marketing ? 
Marketing is the methodology of communicating the value of a product or service to customers, for the purpose of selling that product or service.

Marketing techniques include choosing target markets through market analysis and market segmentation, as well as understanding consumer behavior and advertising a product's value to the customer. From a societal point of view, marketing is the link between a society's material requirements and its economic patterns of response. Marketing satisfies these needs and wants through exchange processes and building long term relationships. Marketing blends art and applied science (such as behavioural sciences) and makes use of information technology.

But how we can do "communicating the value of a product or service to customers" ?
We must understand customer, see the needs, feel the user experience and create something better, to satisfy them. The important step, you send the right message at right context (time, location, ..) to the right customer.

Some methods, such as market analysis, market segmentation and user experience research could be used.

So what is exactly reactive marketing ?
As we know, we live in the age of "fast data", it means we should react immediately when we find potential customers.
2 refer links here:
http://venturebeat.com/2014/06/25/the-next-big-disruption-in-big-data/
https://www.youtube.com/watch?v=TnLiEWglqHk

Reactive just as "showing a response to a stimulus".  We have data as event stream, analyzing as patterns and context (I call them "the Function X", the computing object that contains both data, context and behavioural functions ) and reactive to that pattern immediately.
The idea of Function X comes from http://en.wikipedia.org/wiki/Actor_model, and the framework implementation is Akka, http://akka.io/
Reactive Function X is my implementation for core ideas in practice. http://www.mc2ads.com/p/rfx-for-big-data-developer.html

This picture could be useful.

Why ?
The benefits are expected to include analyzing more data, faster—helping to find “insights that would otherwise have been missed,” as well as other improved efficiencies.
The sale team and marketing team should adopt new techs, react to sophisticated shopper insights, gain more customers and faster than competitors 

How we do it ? Some simple diagrams, here

Refer links:
http://www.emarketer.com/Article/Using-Big-Data-Still-Challenge-Marketers/1010710
http://en.wikipedia.org/wiki/Marketing
https://hbr.org/2013/03/advertising-analytics-20

Reactive Function X (RFX) vs Apache Storm

RFX is designed to be simple as possible for most event-processing problems, but no simpler :)

RFX is designed for event-processing problems

Simple code and auto scale this level

Simple functor that compute the trending data from Facebook

http://en.wikipedia.org/wiki/Stream_processing
http://en.wikipedia.org/wiki/Directed_acyclic_graph
http://cva.stanford.edu/publications/2003/ieeecomputer_stream.pdf

http://paginas.fe.up.pt/~prodei/dsie12/papers/paper_17.pdf
http://cpettitt.github.io/project/dagre-d3/latest/demo/interactive-demo.html

Big Data và vai trò của nó trong truyền thông thế kỉ 21(Reactive Big Data and the age of "Social journalism")

Tóm tắt tiếng Việt như sau:
Vai trò của media (truyền thông) ngày càng quan trọng và sự phát triển của công nghệ để hỉểu hơn về bản chất dữ liệu (Insights) như một điều tất yếu.
Các câu chuyên của thế hệ truyền thông thế hệ kế tiếp cần tạo ra giá trị nhiều hơn, đặc biệt cần ứng dụng analytics để data có thể tìm đến đến đúng đối tuợng cần nó (reactive analytics) và truyền tải thông điệp trực quan hơn (Visual Insights).

1 Ý tuởng mới từ sự kết hợp các concepts khác nhau: 
xây dựng 1 big data platform cho nhà hoạt động truyền thông , các nhà báo chuyên nghiệp và không chuyên có thể tạo ra các content có giá trị hơn và ảnh huởng lớn hơn với xã hội.

Vài core concepts:

What is the "Insight" ?
 is the understanding of a specific cause and effect in a specific context.
http://en.wikipedia.org/wiki/Insight

Visual Insight ? 
Not everyone understands a chart !
http://cns.iu.edu/docs/presentations/2013-borner-visualinsights-cs10k.pdf

Reactive Big Data is the term, which describes how data actively is built for connecting people around the Internet to create new community and make true core value for people.
I have developed both the theory and the technology for this concept near 7 years, you can get more idea at http://www.mc2ads.com/p/rfx-for-big-data-developer.html

Social journalism is a media model consisting of a hybrid of professional journalism, contributor and reader content.[1] It is similar to open publishing platforms, like Twitter and WordPress.com, except that some or most content is also created and/or screened by professional journalists. http://en.wikipedia.org/wiki/Social_journalism

Data-driven journalism
Data-driven journalism, often shortened to "ddj", is a term in use since 2009/2010, to describe a journalistic process based on analyzing and filtering large data sets for the purpose of creating a news story. Main drivers for this process are newly available resources such as "open source" software and "open data"
http://en.wikipedia.org/wiki/Data-driven_journalism

Question: Can we use reactive big data to build better city and community ?
(Chúng ta có thể sử dụng công nghệ reactive big data để xây dụng 1 thành phố và cộng đồng tốt hơn ? )
Đây là 1 câu hỏi khá xa vời và trừu tuợng, nhưng nó là 1 nhu cầu thực sự, mọi nguời ngày nay ngày càng về các thành phố lơn để sinh sống và học tập. Sẽ như thế nào nếu bạn không có thông tin về chỗ bạn ở có an tòan hay không ? Chi phí sinh hoạt ? các loạt dịch vụ khác nhau ? ...
Bạn có thể Google từ Internet nhưng việc đánh giá mức độ chính xác của thông tin về mặt "bản địa hóa" (localization) và văn hóa thì Google vẫn chưa làm tốt ? nó không update thuờng xuyên và cá nhân hóa cho từng nhóm đối tuợng.

Tại sao là "Data-driven journalism" ? 
Sự thật là những thông tin tốt và có tính cập nhật thuờng xuyên thì không phải lúc nào cũng free !
Vì vậy, những nguời cung cấp tin cần đuợc trả công cho công việc cung cấp thông tin có giá trị, vai trò của báo chí nghiệp dư cần đuợc sử dụng để tạo vòng loop của nền kinh tế thông tin, tính update liên tục. Các công cụ data-driven cho các nhà báo nghiệp dư sẽ xây dựng để giúp quá trình tạo information dễ dàng và chính xác hơn.
http://datajournalismhandbook.org/1.0/en/understanding_data_7.html

Native advertising ? http://en.wikipedia.org/wiki/Native_advertising
Native advertising is a form of online advertising that matches the form and function of the platform on which it appears. The word "native" refers to the content's coherence with other media on the platform.
Example: http://www.copyblogger.com/examples-of-native-ads/
Refer: http://www.adexchanger.com/data-driven-thinking/how-is-data-science-changing-your-ad-business-let-us-count-the-ways/

Liệu chúng ta có thể kết hợp quảng cáo tự nhiên (native advertising) vào Data-driven journalism ?
Quảng cáo về bản chất là một hoạt động có tính xây dựng sự tuơng tác về mặt ý thức giữa nhà sản xuất và nguời tiêu dùng. Ở đây, cần hiểu nhà sản xuất chính là các content creator, và nguời tiêu dùng chính những nguời có nhu cầu về thông tin cho các nhu cầu cá nhân của họ (VD: thông tin tham khảo khi mua xe, mua smartphone, đi ăn, đi du lịch, đi chơi,...).
Tóm lại là các nhóm ngành dịch vụ ở 1 thành phố lớn, sẽ cần 1 chu trình kinh tế giữa bên bán và bên mua. Giá trị kinh tế nằm ở khả năng trao đổi, không phải ở số tiền trong nhà bank.
Refer: http://blog.revenue.com/native-advertising-2/how-big-data-can-save-newspapers/

Hiện tại  vẫn là ý tuởng từ vài cuốn sách, mô hình kinh doanh và công nghệ vẫn cần đuợc xây dựng thêm, 1 số sách mới mua và mình vẫn đang research .
Bạn thấy hứng thú và muốn tham gia: contact me at tantrieuf31@gmail.com, sẵn sàng share sách để cùng học hỏi và xây dựng :)

Data Mining Topics for Digital Marketing

• Mining for Ad Relevance and Ranking

o Ad relevance measurement

o Ad ranking algorithms

o Ad text creation and evaluation

o CTR and conversion rate prediction

o Real-time bidding optimization

• Audience Intelligence & User Modeling

o User Tracking

o Understanding user intent

o Modeling online user behaviors for targeted advertisement

o User segmentation and profiling

o Demographics & location prediction

o Personalized advertising

• Content Understanding and Content Marketing

o Content-targeted advertising

o Opinion/sentiment mining

o Web scale information extraction for online advertisement

o Text mining techniques such as named entity extraction, query classification, keyword extraction, and other topics

o Understanding multimedia content for online advertisement

• Social, Mobile Advertising, and new advertising channels and formats

o Advertising through social networks and microblogging (such as Facebook and Twitter)

o Advertising through deals (such as Groupon and LivingSocial)

o Advertising on new channels such as mobile devices

o Mobile advertising

o Video advertising

o Native advertising

o Viral marketing

• Advertising Ecosystems

o Auction theory in online advertising

o Demand and supply volume prediction

o Measurement of online advertising effectiveness

o Search Engine Marketing, Optimization (SEMs, SEOs)

o Systems and technologies in ad exchange and RTB

• Trust and Privacy

o Consumer privacy and data use policy

o Privacy preserving data mining approaches

o Fraud and spam detection & prevention in online advertisements

The future of programmatic advertising for mobile eCommerce

 As mobile advertisers strive to capture the attention of interested audiences, performing effective targeting relies on the accurate analysis of a variety of signals. One of those signals, a deep understanding of the content being consumed, is an important part of building an overall model of a mobile device user’s behavior.

Digital Advertising Trends—Programmatic, Big Data, Native, Viewability from eMarketer

Making sense of content is difficult: it requires machine intelligence that is accurate, fast, and flexible. That’s why mobile providers are increasingly relying on a technology known as deep learning — a new class of machine learning algorithms that are skilled at gleaning insights from raw data and are highly adaptable and accurate. While the usage of machine learning to improve ad targeting is not a recent development, newly discovered deep learning algorithms are particularly adept at building hierarchical representations of input data. This richer ability to model input signals has enabled significant advances in a computer’s ability to understand text, images, video, and speech.

Geoff Hinton - Recent Developments in Deep Learning

The mind-map for Big Data Platform - The model of 9D

Full mindmap:
http://www.mindmeister.com/450339578/big-data-platform

Data Mining
http://web.stanford.edu/class/cs246/

Data Visualization
http://datajournalismhandbook.org/1.0/en/understanding_data_7.html

Data-driven Marketing
http://www.gartner.com/marketing/digital/research/data-driven/

Big Data - from Simple Ideas to Advanced Concepts

Basic ideas:

Big Data - explained in a fun and easy way

5 V of Big Data

Basic Principles of Big Data System

More information at refer links:

http://www.slideshare.net/FriedBroekhof/big-data-solutions-explained-for-marketeers-business-executives

https://dl.dropboxusercontent.com/u/4074962/ebooks/Big-Data-in-Big-Companies.pdf

Advanced concepts:

We, the human see data (structured), create new data (unstructured and structured) and the demand is finding the relationship inside new data. That's why big data was born !

https://dl.dropboxusercontent.com/u/4074962/ebooks/BigData_NoMoreSecrets.pdf

How ?

https://github.com/josephmisiti/awesome-machine-learning
https://github.com/onurakpolat/awesome-bigdata

Emerging Trends in Big Data Technologies

Emerging Trends in Big Data Technologies

• Storm: Apache Storm is an open source distributed real-time computation system. Storm makes it easy to process streams of data, doing for real-time processing what Hadoop did for batch processing.
• Spark: Spark is an in-memory data-processing platform that is compatible with Hadoop data sources but runs much faster than Hadoop MapReduce. It’s well suited for machine learning jobs, as well as interactive data queries, and is easier for many developers because it includes APIs in Scala, Python and Java.
• Apache Hive: Apache Hive facilitates querying and managing large datasets residing in distributed storage. It also allows the map reduce programmers to plug in custom mappers and reducers.
• Apache Tajo: Apache Tajo is a big data relational and distributed data warehouse system for Apache Hadoop. Tajo is designed for low-latency and scalable ad-hoc queries, online aggregation, and ETL (extract-transform-load process) on large-data sets stored on HDFS (Hadoop Distributed File System) and other data sources.
• Twitter's Summingbird

Full report: http://www.infoq.com/research/big-data-emerging-trends

Khoa học dữ liệu, triết học và bóng đá (How Big Data Helped Germany in the World Cup 2014)

Tóm tắt bằng Vietnamese như sau:
Sau thất bại (2 lần hạng 3 thế giới, 2 lần vào chung kết Euro) từ 2006 (cuộc cách mạng bóng đá Đức do Jürgen Klinsmann đề xuất), đội tuyển Đức đã kết hợp giữa khoa học và thể thao một cách chặt chẽ nhằm tìm kiếm  danh hiệu thứ 4 (vô địch World Cup 2014).
Theo như trợ lý huấn luyện Hansi Flick, trong 2 năm qua, các sinh viên ở truờng đại học thể thao Cologne đã phát triển một hệ thống gồm cơ sở dữ liệu về cầu thủ, cách sử dụng chiến thuật và cách đá, đi bóng của từng câu thủ.

Vì vậy, dữ liệu của 736 cầu thủ đá ỏ World Cup (gồm luôn tuyển Đức) đã đuợc số hóa để giúp ban huấn luyện đề ra từng chiến thuật cụ thể cho từng trận đấu ở giải kỳ này.
=> trận hòa Ghana chắc do thiếu data #_#

Câu hỏi: Với mỗi trận đấu, sẽ tồn tại ít nhất một cách để phá lối đá của đối phuơng và phát huy tối đa sức tấn công để ghi bàn ?

chờ đợi trân chung kết để xem điều này đúng hay sai :)

The data can be analysed in real-time by data experts - and training schedules can be adapted

NSA , a super analytic in football , is developed at Germany for this World Cup 2014

“The sports students in Cologne have been studying in great detail our opponent and put every play they’ve run, every newspaper article on them, and everything about them out there under the microscope and made all that data available to us,” 

Read more: http://www.dailystar.com.lb/Sports/Football/2014/Jul-08/263019-university-boffins-steer-german-tactics.ashx#ixzz379AoFIWj
(The Daily Star :: Lebanon News :: http://www.dailystar.com.lb) 

big data is about capturing the "moment". The German team was able to capture and analyze each and every moment about the Brazilian team including the passes they play, how they react upon pressure, and even every quote about them in newspapers. They were able to analyze hidden team behaviors and strategy information.
From https://www.linkedin.com/today/post/article/20140709143632-54591340-how-big-data-helped-germany-break-brazil-s-hearts-in-the-world-cup

Big Data is about capturing every single “moment” throughout the human life

https://www.linkedin.com/today/post/article/20140627113503-54591340-big-data-utopia

http://blogs.bridgei2i.com/2014/06/25/for-the-love-of-soccer-and-data-analytics/

Tìm kiếm các giải pháp công nghệ khí hậu (tài trợ từ InfoDev và World Bank)

Tóm tắt: Cuộc thi Chứng Minh Khái Niệm – Proof of Concept (PoC) do chương Trình Công Nghệ Khí Hậu InfoDev của Ngân Hàng Thế Giới (World Bank) phối hợp với Ngân Hàng Phát Triển Châu Á (ADB) được tổ chức tại Việt Nam dành cho các doanh nghiệp biến đổi khí hậu.

Với mục đích tìm kiếm và khuyến khích các đổi mới và các giải pháp công nghệ khí hậu nhằm giúp Việt Nam thích ứng với biến đổi khí hậu, giảm thiểu phát thải, đáp ứng nhu cầu năng lượng, tăng năng suất, duy trì khả năng cạnh tranh, và giảm thiểu sự phụ thuộc vào nhập khẩu nhiên liệu hóa thạch. cuộc thi PoC đặc biệt quan tâm đến những doanh nghiệp hoạt động trong các lĩnh vực:

• Hiệu quả năng lượng;
• Nhiên liệu sinh học và sinh khối;
• Nông nghiệp bền vững;
• Công nghệ thích ứng;
• Công nghệ vận chuyển:
• Công nghệ năng lượng tái tạo;
• Quản lý và lọc nước;
• Các ngành công nghệ khí hậu khác.

Khoản tài trợ của cuộc thi lên tới 1 tỷ đồng. Số tiền này được dùng cho việc phát triển, triển khai và/hoặc mở rộng một sản phẩm hay dịch vụ. Ngoài ra, 30 ứng viên xuất sắc nhất là các doanh nghiệp, dự án, nhóm nghiên sẽ được ưu tiên tiếp cận chương trình của Trung tâm sáng tạo khí hậu Việt Nam (CIC Việt Nam) và toàn bộ các dịch vụ tư vấn của chương trình (đào tạo, cố vấn, cơ sở vật chất và đầu tư).
more info từ Links:
http://www.twenty.vn/cong-dong/proof-concept-tim-kiem-cac-giai-phap-cong-nghe-khi-hau
http://www.vietnamcic.org/

Mình có vài ý tưởng từ framework Rfx, viết nháp ra như sau, bạn nào muốn làm thì mình có thể tư vấn giúp, (connect at https://www.facebook.com/tantrieuf31 )
chủ yếu tập trung chính:
• Nông nghiệp bền vững;
• Công nghệ thích ứng;

ý tưởng công nghệ:
http://www.mc2ads.com/2014/01/from-big-data-to-disaster-response.html
http://www.mc2ads.com/2013/12/palantir-big-data-technologies-tu-quy.html

Kiến trúc chính:

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