Are you one of the millions who spent the day trying to win Gold running Internet Track and Field via the animated Google Doodle on the Google home page? Well if so, today you get another chance at Gold with Google’s animated doodle featuring Olympic Basketball.
I can’t wait to total up this weeks hours spent by millions on the Google home page. Leave to Google to come up with a fun creative idea that drives people to their site and entertains them at the same time =) Gotta love Google!
Yes, I’m A Google Doodle junkie, I admit to checking out their home page daily to see if there is a special Google Doodle! Today they have an animated themed tribute to the opening of the first ever drive in movie theater!
Today’s animated Google Doodle on their home page leads directly to a listing for the opening of the first drive-in theater in New Jersey, 79 years ago today.
Included below for just a bit of additional history on the drive in movie theater is a drawing from the first patent for the Drive-In Theater (United States Patent# 1,909,537) which was issued on May 16, 1933. The first drive in movie patent was issues to Richard Hollingshead. Richard Hollingshead, then a young sales manager at his dad’s Whiz Auto Products, opened the first drive-in on Tuesday, June 6, 1933, at a location on Crescent Boulevard in Camden, New Jersey. The price of admission was 25 cents for the car and 25 cents per person. Have you ever been to a drive in movie theater? If not, you should go at least once. Today’s drive in movie theaters usually show a very popular movie at a great price (Usually $5.00 to $10 per car) instead of the standard $9+ per ticket for the indoor movie theater tickets.
Check out a few screenshots of today’s Google Doodle animation below.
Yes, Google has succeeded in capturing our attention today by once again changing up the world’s most versatile logo into yet another creative doodle that has us all unzipping. Today Google honors Gideon Sundback, the man that invented what we now know as the zipper. Today’s Google doodle honors Gideon Sundback on this 132nd birthday.
Google Gideon Sundback’s 132nd birthday doodle gives the appearance of a jacket front that has the Google logo embroidered on it and a zipper runs through the middle of the Google logo, separating the second ‘o’ in Google. Users can either click on the logo or better yet, actually unzip the zipper to reveal what Google has hidden beneath! I admit, I was more than curious to see what would be underneath!
In 1914 Gideon Sundback filed for the patent and in 1917 Gideon Sundback obtained US Pat No 1219881 for a “Separable Fastener” and designed a manufacturing machine for the fastener. The popular name came from the B.F. Goodrich Company which used the name for a new type of rubber boots or galoshes and renamed the Sundback fastener the “zipper”. In the 1930’s the zipper came into widespread usage when the fashion industry adopted it for garments, handbags, and other items and continues to be widely used throughout the world today.
Designs from Sundback’s patent application are shown below:
Here are the US Patent Drawings for Gideon Sundback’s Invention the Zipper
Cudo’s to Google for creativity ! I continue to be amazed with the fun ways Google succeeds in driving us all to their homepage time and time again with their famous Google Logo Doodles! No brand in existence today even comes close to using their logo in such a creative way.
For those of you who want to play the pacman doodle, you can go here and play a game or two for fun ! Google Pacman Doodle
Today’s zipper doodle is one of my favorites ever, but the Pacman Doodle remains my all time favorite! What are your favorites?
Users visit Google and enter their search terms or “keywords” and in half a second Google displays the results. Sounds super easy doesn’t it? Behind the scenes a whole lot more is happening to give you the best results possible. On Monday, Google launched a video to help explain how the massive search engine actually works.
Matt Cutts, an all around great guy and software engineer head of Google’s web spam team, details in the YouTube video shown below how the search engine giant scours the web on a daily basis to provide the most accurate and up-to-date results to users.
Google does a great job in my opinion, better than most. I know Google has it’s haters, but I’m a true long time fan ! Take a moment someday and search Bing, Yahoo and Google for the same keywords and see which search engine returns the best results.
Here is a transcript of the Matt Cutts video shown above.
Hi, everybody. We got a really interesting and very expansive question from RobertvH in Munich. RobertvH wants to know–
Hi Matt, could you please explain how Google’s ranking and website evaluation process works starting with the crawling and analysis of a site, crawling time lines,frequencies, priorities, indexing and filtering processes within the databases, et cetera?
So that’s basically just like, tell me everything about Google. Right?
That’s a really expansive question. It covers a lot of different ground. And in fact, I have given orientation lectures to engineers when they come in. And I can talk for an hour about all those different topics, and even talk for an hour about a very small subset of those topics. So let me talk for a while and see how much of a feel I can give you for how the Google infrastructure works, how it all fits together, how our crawling and indexing and serving pipeline works. Let’s dive right in.
So there’s three things that you really want to do well if you want to be the world’s best search engine. You want to crawl the web comprehensively and deeply. You want to index those pages. and then you want to rank or serve those pages and return the most relevant ones first. Crawling is actually more difficult than you might think.
Whenever Google started, whenever I joined back in 2000, we didn’t manage to crawl the web for something like three or four months. And we had to have a war room. But a good way to think about the mental model is we basically take page rank as the primary determinant. And the more page rank you have– that is, the more people who link to you and the more reputable those people are– the more likely it is we’re going to discover your page relatively early in the crawl.
In fact, you could imagine crawling in strict page rank order, and you’d get the CNNs of the world and The New York Times of the world and really very high page rank sites. And if you think about how things used to be, we used to crawl for 30 days. So we’d crawl for several weeks. And then we would index for about a week. And then we would push that data out. And that would take about a week. And so that was what the Google dance was.
Sometimes you’d hit one data center that had old data. And sometimes you’d hit a data center that had new data. Now there’s various interesting tricks that you can do. For example, after you’ve crawled for 30 days, you can imagine re-crawling the high page rank guys so you can see if there’s anything new or important that’s hit on the CNN home page.
But for the most part, this is not fantastic. Right? Because if you’re trying to crawl the web and it takes you 30 days, you’re going to be out-of-date. So eventually, in 2003, I believe, we switched as part of an update called Update Fritz to crawling a fairly interesting significant chunk of the web every day.
And so if you imagine breaking the web into a certain number of segments, you could imagine crawling that part of the web and refreshing it every night. And so at any given point, your main base index would only be so out of date. Because then you’d loop back around and you’d refresh that. And that works very, very well.
Instead of waiting for everything to finish, you’re incrementally updating your index. And we’ve gotten even better over time. So at this point, we can get very, very fresh. Any time we see updates, we can usually find them very quickly. And in the old days, you would have not just a main or a base index, but you could have what were called supplemental results, or the supplemental index. And that was something that we wouldn’t crawl and refresh quite as often. But it was a lot more documents.
And so you could almost imagine having really fresh content, a layer of our main index, and then more documents that are not refreshed quite as often, but there’s a lot more of them. So that’s just a little bit about the crawl and how to crawl comprehensively. What you do then is you pass things around. And you basically say, OK, I have crawled a large fraction of the web. And within that web you have, for example, one document. And indexing is basically taking things in word order.
Well, let’s just work through an example.
Suppose you say Katy Perry. In a document, Katy Perry appears right next to each other. But what you want in an index is which documents does the word Katy appear in, and which documents does the word Perry appear in? So you might say Katy appears in documents 1, and 2, and 89,and 555, and 789.
And Perry might appear in documents number 2, and 8, and 73, and 555, and 1,000. And so the whole process of doing the index is reversing,so that instead of having the documents in word order, you have the words, and they have it in document order. So it’s, OK, these are all the documents that a word appears in.
Now when someone comes to Google and they type in Katy Perry, you want to say, OK, what documents might match Katy Perry? Well, document one has Katy, but it doesn’t have Perry. So it’s out. Document number two has both Katy and Perry, so that’s a possibility. Document eight has Perry but not Katy. 89 and 73 are out because they don’t have the right combination of words. 555 has both Katy and Perry. And then these two are also out.
And so when someone comes to Google and they type in Chicken Little, Britney Spears, Matt Cutts, Katy Perry, whatever it is, we find the documents that we believe have those words, either on the page or maybe in back links, in anchor text pointing to that document. Once you’ve done what’s called document selection, you try to figure out, how should you rank those? And that’s really tricky.
We use page rank as well as over 200 other factors in our rankings to try to say, OK, maybe this document is really authoritative. It has a lot of reputation because it has a lot of page rank. But it only has the word Perry once. And it just happens to have the word Katy somewhere else on the page. Whereas here is a document that has the word Katy and Perry right next to each other, so there’s proximity. And it’s got a lot of reputation. It’s got a lot of links pointing to it.
So we try to balance that off. You want to find reputable documents that are also about what the user typed in. And that’s kind of the secret sauce, trying to figure out a way to combine those 200 different ranking signals in order to find the most relevant document. So at any given time, hundreds of millions of times a day, someone comes to Google. We try to find the closest data center to them. They type in something like Katy Perry.
We send that query out to hundreds of different machines all at once, which look through their little tiny fraction of the web that we’ve indexed. And we find, OK, these are the documents that we think best match. All those machines return their matches. And we say, OK, what’s the creme de la creme? What’s the needle in the haystack? What’s the best page that matches this query across our entire index? And then we take that page and we try to show it with a useful snippet. So you show the key words in the context of the document. And you get it all back in under half a second. So that’s probably about as long as we can go on without straining YouTube.
But that just gives you a little bit of a feel about how the crawling system works, how we index documents, how things get returned in under half a second through that massive parallelization.
I hope that helps. And if you want to know more, there’s a whole bunch of articles and academic papers about Google, and page rank, and how Google works. But you can also apply to there’s firstname.lastname@example.org, I think, or google.com/jobs, if you’re interested in learning a lot more about how search engines work. OK. Thanks very much.
“The Anatomy of a Large-Scale Hypertextual Web Search Engine”: http://research.google.com/pubs/archive/334.pdf