Alexa Web Search Platform Debuts

I’m currently working on an article for D-Lib Magazine explaining in greater depth how some of my tools that use search engine APIs work (such as the Syllabus Finder and H-Bot). These APIs, such as the services from Google and Yahoo, allow somewhat more direct access to mammoth web databases than you can get through these companies’ more public web interfaces. I thought it would be helpful for the article to discuss some of the advantages and drawbacks of these services, and was just outlining one of my major disappointments with their programming interfaces—namely, that you can’t run sophisticated text analysis on their servers, but have to do post-processing on your own server once you get a set of results back—when it was announced that Alexa released its Web Search Platform. The AWSP allows you to do just what I’ve been wanting to do on an extremely large (4 billion web page) corpus: scan through it in the same way that employees at Yahoo and Google can do, using advanced algorithms and manipulating as large a set of results as you can handle, rather than mere dozens of relevant pages. Here’s what’s notable about AWSP for researchers and digital humanists.

  • Yahoo and Google hobble their APIs by only including a subset of their total web crawl. They seem leery of giving the entire 8 billion pages (in the case of the Google index) to developers. My calculation is that only about 1 in 5 pages in the main Google index makes it into their API index. AWSP provides access to the full crawl on their servers, plus the prior crawl and any crawl in progress. This means that AWSP probably provides the largest dataset researchers can presently access, about 3 times larger than Google or Yahoo (my rough guess from using their APIs is that those datasets are only about 1.5 billion pages, versus about 4 billion for AWSP). It seems ridiculous that this could make a difference (do I really need 250 terabytes of text rather than 75?), but when you’re searching for low-ranking documents like syllabi it could make a big difference. Moreover, with at least two versions of every webpage, it’s conceivable you could write a vertical search engine to compare differences across time on the web.
  • They seem to be using a similar setup to the Ning web application environment to allow nonprogrammers to quickly create a specialized search by cloning a similar search that someone else has already developed. No deep knowledge of a programming language needed (possibly…stay tuned).
  • You can download entire datasets, no matter how large, something that’s impossible on Yahoo and Google. So rather than doing my own crawl for 600,000 syllabi—which broke our relatively high-powered server—you can have AWSP do it for you and then grab the dataset.
  • You can also have AWSP host any search engine you create, which removes a lot of the hassle of setting up a search engine (database software, spider, scripting languages, etc.).
  • OK, now the big drawback. As economists say, there’s no such thing as a free lunch. In the case of AWSP, their business model differs from the Google and Yahoo APIs. Google and Yahoo are trying to give developers just enough so that they create new and interesting applications that rely on but don’t compete directly with Google and Yahoo. AWSP charges (unlike Google and Yahoo) for use, though the charges seem modest for a digital humanities application. While a serious new search engine that would data-mine the entire web might cost in the thousands of dollars, my back of the envelope calculation is that it would cost less than $100 (that is, paid to Alexa, aside from the programming time) to reproduce the Syllabus Finder, plus about $100 per year to provide it to users on their server.

I’ll report more details and thoughts as I test the service further.

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