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05 Sep 09

Ning.com building a platform of social networks

An interesting event based monitoring system that caries around lots of useful details for each request

www.infoq.com/...ning-social-network-platform - Preview

architecture highscalability monitoring

12 Aug 09

Scalability Best Practices | AKF Partners Blog

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architecture scalability highscalability

01 Jul 09

Web Squared: Web 2.0 Five Years On

Tim O'Reilly's unique perspective on the future of the internet

www.web2summit.com/...10194 - Preview

web2.0 architecture webservices visualization

"the network as platform" means far more than just offering old applications via the network ("software as a service"); it means building applications that literally get better the more people use them, harnessing network effects not only to acquire users, but also to learn from them and build on their contributions.
Collective intelligence applications depend on managing, understanding, and responding to massive amounts of user-generated data in real time.
21 more annotations...
- Data is the "Intel Inside" of the next generation of computer applications.
- Many people now understand this idea in the sense of "crowdsourcing," namely that a large group of people can create a collective work whose value far exceeds that provided by any of the individual participants.
- But is this really what we mean by collective intelligence? Isn’t one definition of intelligence, after all, that characteristic that allows an organism to learn from and respond to its environment?
- Coordinating speech recognition and search, search results and location, is similar to the "hand-eye" coordination the baby gradually acquires. The Web is growing up, and we are all its collective parents.
- "data is the Intel Inside" of the next generation of computer applications. That is, if a company has control over a unique source of data that is required for applications to function, they will be able to extract monopoly rents from the use of that data. In particular, if a database is generated by user contribution, market leaders will see increasing returns as the size and value of their database grows more quickly than that of any new entrants.
- We see the era of Web 2.0, therefore, as a race to acquire and control data assets.
- machine learning techniques apply to far more than just sensor data. For example, Google’s ad auction is a learning system, in which optimal ad placement and pricing is generated in real time by machine learning algorithms.
- Some of the most fundamental and useful services on the Web have been constructed in this way, by recognizing and then teaching the overlooked regularity of what at first appears to be unstructured data.
- Ti Kan, Steve Scherf, and Graham Toal, the creators of CDDB, realized that the sequence of track lengths on a CD formed a unique signature that could be correlated with artist, album, and song names. Larry Page and Sergey Brin realized that a link is a vote. Marc Hedlund at Wesabe realized that every credit card swipe is also a vote, that there is hidden meaning in repeated visits to the same merchant. Mark Zuckerberg at Facebook realized that friend relationships online actually constitute a generalized social graph. They thus turn what at first appeared to be unstructured into structured data. And all of them used both machines and humans to do it.
- Key takeaway: A key competency of the Web 2.0 era is discovering implied metadata, and then building a database to capture that metadata and/or foster an ecosystem around it.
- Today’s smartphones contain microphones, cameras, motion sensors, proximity sensors, and location sensors (GPS, cell-tower triangulation, and even in some cases, a compass).
- mobile applications are connected applications. The fundamental lessons of Web 2.0 apply to any network application, whether web- or mobile phone-based (and the lines between the two are increasingly blurred). Sensor-based applications can be designed to get better the more people use them, collecting data that creates a virtuous feedback loop that creates more usage. Speech recognition in Google Mobile App is one such application. New internet-connected GPS applications also have built-in feedback loops, reporting your speed and using it to estimate arrival time based on its knowledge of traffic ahead of you. Today, traffic patterns are largely estimated; increasingly, they will be measured in real time.
- It’s the first taste of an "augmented reality" future.
- real world objects have "information shadows" in cyberspace. For instance, a book has information shadows on Amazon, on Google Book Search, on Goodreads, Shelfari, and LibraryThing, on eBay and on BookMooch, on Twitter, and in a thousand blogs.
  
  A song has information shadows on iTunes, on Amazon, on Rhapsody, on MySpace, or Facebook. A person has information shadows in a host of emails, instant messages, phone calls, tweets, blog postings, photographs, videos, and government documents. A product on the supermarket shelf, a car on a dealer’s lot, a pallet of newly mined boron sitting on a loading dock, a storefront on a small town’s main street — all have information shadows now.
  
  In many cases, these information shadows are linked with their real world analogues by unique identifiers: an ISBN or ASIN, a part number, or getting more individual, a social security number, a vehicle identification number, or a serial number. Other identifiers are looser, but identity can be triangulated: a name plus an address or phone number, a name plus a photograph, a phone call from a particular location undermining what once would have been a rock-solid alibi.
- What the Web 2.0 sensibility tells us is that we’ll get to the Internet of Things via a hodgepodge of sensor data contributing, bottom-up, to machine-learning applications that gradually make more and more sense of the data that is handed to them.
- As the information shadows become thicker, more substantial, the need for explicit metadata diminishes.
- Data analysis, visualization, and other techniques for seeing patterns in data are going to be an increasingly valuable skillset. Employers take notice.
- Mapping from unstructured data to structured data sets will be a key Web Squared competency.
- With services like Twitter and Facebook’s status updates, a new data source has been added to the Web – realtime indications of what is on our collective mind.
- Real-time is not limited to social media or mobile. Much as Google realized that a link is a vote, WalMart realized that a customer purchasing an item is a vote, and the cash register is a sensor counting that vote. Real-time feedback loops drive inventory. WalMart may not be a Web 2.0 company, but they are without doubt a Web Squared company: one whose operations are so infused with IT, so innately driven by data from their customers, that it provides them immense competitive advantage.
- Businesses must learn to harness real-time data as key signals that inform a far more efficient feedback loop for product development, customer service, and resource allocation.

05 Jun 09

CouchDB: The Definitive Guide

books.couchdb.org/relax - Preview

couchdb architecture scalability highscalability

26 May 09

Guy's Blog: The Achilles heel of the CAP theorem

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architecture CAP ACID BASE

Guy's Blog: My take on CAP

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architecture CAP BASE ACID

Facebook | Engineering @ Facebook's Notes: Haystack

Needle in a haystack: efficient storage of billions of photos

www.facebook.com/note.php - Preview

architecture highscalability Facebook

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Alex Popescu's Library tagged architecture → View Popular

Ning.com building a platform of social networks

Scalability Best Practices | AKF Partners Blog

Web Squared: Web 2.0 Five Years On

CouchDB: The Definitive Guide

Guy's Blog: The Achilles heel of the CAP theorem

Guy's Blog: My take on CAP

Facebook | Engineering @ Facebook's Notes: Haystack

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