Web data mining is quite frequently unreliable, inefficient, and just frankly turns into a painful series of hacks and patches to perform the most basic of scraping tasks. The root problem is that the web is full of semantically and structurally incorrect data. What we’re dealing with is a junkyard of data that will lead us down the wrong path as we search for the hidden gems.
To create a truly efficient web data mining architecture, several key factors need to be taken into account:
* Creating data redundancy principles around weighted key content identifiers to ensure consistent data returns.
* Understanding the horrible practices that are standard in the industry and used at the peril of whoever has to touch that code.
* Using content caching at the domain level to improve performance along with specific page modifiers to preserve unique page-specific qualities.
Once those principles are in place, we will have the basis for creating a highly scalable, efficient, and effective web data mining architecture, allowing us to create semantic value from any site with any content.
This is a talk about using CoffeeScript's native language constructs to apply what we have learned from frameworks and tools like Backbone and Bootstrap. Solutions that promise us a better experience, retain relevance and clarity in our code, and respond more easily to change.
by Val Head
25th–27th April 2013