The Google Group for Maps is great, lots of information and the API docs are detailed.

Below is a screen shot. If you need it, give it a shot.

With high confidence (no Apriori pun indented) I can say that periodically Shoppers Drug Mart mines that data for interesting association, like perhaps:

- Males between the ages of 25 and 45, between the hours of 7:00pm to 12:00am purchase toilet paper and Mach 3 razors.

What would Shoppers do? In the evenings they would set up and isle display of expensive toilet paper. At the front of that isle (as the customer is walking to the cashier) they would set up another display of Mach 3 razors…10 packs. They’ll do this because they’ve found associations, which tell them with high confidence; males will purchase these two items together.

For a data mining project this past semester, one of the deliverables we created was a web based version the Apriori algorithm. The algorithm was initially developed by Agrawal et al (Agrawal 93, Agrawal 94) and is used to find association rules from a dataset. This online version of the Apriori algorithm is based off an implementation by Christian Borgelt’s.

The URL is http://apriori.sematopia.com. Aside from simply executing the Apriori implementation by Christian, the web-based application does a series of pre-processing on the data. To begin, all missing values in a given numeric column are filled in with the average of that attribute. Then categorization occurs on all numeric attributes to place each column value in a bin. This is based off of the Category Granularity (how many categories to make in a given column) and the Categorization Threshold (if the number of unique values in the column are < this number, then categorization will not be performed). There are many options (besides Category Granularity & Categorization Threshold) available that can be set to adjust the results Apriori returns; these include the max/min support, the min confidence, min sets per rules, etc.

The file uploaded must be in CSV format with the first row as headings, and each subsequent row being observations. The max file size that can be uploaded is 200,000 bytes. After the algorithm finishes (which should take less than 3 seconds) the first 100 association rules will be shown. You can download the full output from the website.

The associations produced will look like this:

verbal_sat=c3_g7(442,469) <- math_sat=c2_g7(500,530) act=c4_g6(21,23) (36.1, 93.0)

c3 means column 3
g7(442,469) means group (bin) 7 were this specific bin was between 442 and 469
(36.1, 93.0) at the end of each rule, the support and confidence is given

Each IP address will be allowed to run 5 datasets per day.
Good luck, happy mining.
http://apriori.sematopia.com

Features of the code include:

• A single control loop (as opposed to multiple threads and listeners)
• A great ’stay straight’ algorithm which keeps the robot centered in the network, through 2 light sensors
• Transmission to and from the host component for tasks and logging information
• A task driven control structure

You can download the code here. To learn more about our final product go here.

Update: Proof of Concept 3 pictures can be seen here.
Update: Proof of Concept 2 pictures can be seen here.
Update: Read about the final product here.
Update: Videos of the robot can be seen here.

Robot Pic1 – Robot Pic2 – Robot Pic3 – Robot Pic4 – Robot Pic5

There were 2 parts to the project. The first is the graphical user interface, which uses a map of the network to determine execution paths, trasmit/receive with the robot, etc. The second part is designing the actual robot. Both components had a lot of effort put into them, and collectively we are very proud of the final product.

Our company name was MBI (MicroBot Initiative) and the product name was nexSAR (Next Generation Search and Rescue). Highlights of the finished product?

• Capable of rescuing 3 eggs in under 25 seconds (using the network provided).
• Custom designed ’stay straight’ algorithm, which polls light sensors to remain centered in the hallway.
• Upon exiting the network, the robot uploads its log, and the GUI creates a VRML (3D) log re-enactment. This is complete with victim voices, which get louder as the robot nears.
• Ubuntu Live CD. Just put the CD in the CD drive, reboot, and all the software you need is ready to go. We have a live CD for both PowerPC and for Intel.
• USB Rescue Key. Capable of booting to DSL (Damn Small Linux) straight from a USB drive. This also has all the software pre-loaded and ready to go.
• Complete GUI with map editor and path generation.

Here are the slides that we used in our final presentation.
The slides use 3 videos of the Robot in action – Video1 – Video2 – Video3 – (sorry 2 of the videos are large ~50mb).

Update: Final photos of the Robot (taken March 28/06) can be seen here.
Update: The Robot Java code can be downloaded here (April 4/06).
Update: The videos above have been uploaded to Google Video, you can see them here (July 2/06).

Here is a screen shot of the GUI:

Here is a screen shot of the VRML log re-enactment:

LogFormat “%v %h %l %u %t \”%r\” %>s %b \”%{Referer}i\” \”%{User-Agent}i\”" comonvhost
LogFormat “%h %l %u %t \”%r\” %>s %b \”%{Referer}i\” \”%{User-Agent}i\”" combined
LogFormat “%h %l %u %t \”%r\” %>s %b” common
LogFormat “%{Referer}i -> %U” referer
LogFormat “%{User-agent}i” agent

Adding this LogFormat line, lets Apache distinguish between the various virtual hosts when logging. To learn more about what the options mean (i.e. %u, etc.) you can look here. Each virtual host was given its own log file, so in the VirtualHost directive, the following line was added using CustomLog:

ServerName www.sematopia.com
DocumentRoot /var/www/sematopia.com
ServerAlias sematopia.com *.sematopia.com sematopia.net *.sematopia.net
CustomLog /var/log/apache2/sematopia/access.log comonvhost

Then for all other VirtualHost directives, a similar line was added. Once that’s done, delete or move the old logs in /var/log/apache2. Then run:

apache2 –k restart

to restart Apache. Remember, any time you delete your log files or change your apache2.conf file restart apache.

Using a batch emailing method, first off, allows for an inherent logging ability built into the process. Every time the application wants to send an email, it would write the email (XML) to send.xml. Periodically, from an internal or external server, the send.xml file is processed, and then automatically backed up. This can be extended to eventually gzip the XML files, etc.

Secondly, if your site sends many emails, you may not want to consume resources (in real-time) sending them on demand. Instead, sending all emails every say 5 minutes, could possibly improve server performance.

A typical example code would be:

$mailObj = new xmlMail();
$mailObj->getHandle();
$mailObj->writeEmail($to,$cc,$bcc,$from,$from,$subject,$body);
$mailObj->closeHandle();

See the PHP Class here for more details.