To foster the study of the structure and dynamics of Web traffic networks, Indiana University has made available a large dataset (‘Click Dataset’) of about 53.5 billion HTTP requests made by users at Indiana University. Gathering anonymized requests directly from the network rather than relying on server logs and browser instrumentation allows one to examine large volumes of traffic data while minimizing biases associated with other data sources. It also provides one with valuable referrer information to reconstruct the subset of the Web graph actually traversed by users. The goal is to develop a better understanding of user behavior online and create more realistic models of Web traffic. The potential applications of this data include improved designs for networks, sites, and server software; more accurate forecasting of traffic trends; classification of sites based on the patterns of activity they inspire; and improved ranking algorithms for search results.
The data was generated by applying a Berkeley Packet Filter to a mirror of the traffic passing through the border router of Indiana University. This filter matched all traffic destined for TCP port 80. A long-running collection process used the pcap library to gather these packets, then applied a small set of regular expressions to their payloads to determine whether they contained HTTP GET requests. If a packet did contain a request, the collection system logged a record with the following fields:
the requested URL
the referring URL
a boolean classification of the user agent (browser or bot)
a boolean flag for whether the request was generated inside or outside IU.
Some important notes:
Traffic generated outside IU only includes requests from outside IU for pages inside IU. Traffic generated inside IU only includes requests from people at IU (about 100,000 users) for resources outside IU. These two sets of requests have very different sampling biases.
No distinguishing information about the client system was retained: no MAC or IP addresses nor any unique index were ever recorded.
There was no attempt at stream reassembly, and server responses were not analyzed.
During collection, the system generated data at a rate of about 60 million requests per day, or about 30 GB/day of raw data. The data was collected between Sep 2006 and May 2010. Data is missing for about 275 days. The dataset has two collections:
raw: About 25 billion requests, where only the host name of the referrer is retained. Collected between 26 Sep 2006 and 3 Mar 2008; missing 98 days of data, including the entire month of Jun 2007. Approximately 0.85 TB, compressed.
raw-url: About 28.6 billion requests, where the full referrer URL is retained. Collected between 3 Mar 2008 and 31 May 2010; missing 179 days of data, including the entire months of Dec 2008, Jan 2009, and Feb 2009. Approximately 1.5 TB, compressed.
The dataset is broken into hourly files. The initial line of each file has a set of flags that can be ignored. Each record looks like this:
XXXXADreferrer host path
where XXXX is the timestamp (32-bit Unix epoch in seconds, in little endian order), A is the user-agent flag (“B” for browser or “?” for other, including bots), D is the direction flag (“O” for external traffic to IU, “I” for internal traffic to outside IU), referrer is the referrer hostname or URL (terminated by newline), host is the target hostname (terminated by newline), and path is the target path (terminated by newline).
The Click Dataset is large (~2.5 TB compressed), which requires that it be transferred on a physical hard drive. You will have to provide the drive as well as pre-paid return shipment. Additionally, the dataset might potentially contain bits of stray personal data. Therefore you will have to sign a data security agreement. Indiana University require that you follow these instructions to request the data.
View agency activity clustering on geography in Excel using Excel Data Mining Add-ins
By Don Krapohl
1. Ensure you have downloaded the Excel Data Mining Add-ins from Microsoft at http://www.microsoft.com/en-us/download/details.aspx?id=35578 . The article assumes you have a working version of the DM Addins and a default Analysis Services (SSAS) instance defined. Search for getting started with SQL Server Data Mining Add-ins for Excel if you are not familiar with this process.
4. In the Home tab on the ribbon in the Styles section select “Format as Table”. Pick any format you wish.
5. A new tab will appear on the ribbon for Table Tools with menus for Analyze and Design as below.
6. On the Analyze menu, select “Detect Categories”. This is will group (cluster) your information on common attributes, particular commonalities that are not obvious or immediately observable.
7. Deselect all checkboxes except the following:
a. Dollars Obligated
b. Award Type
c. Contract Pricing
d. Funding Agency
e. Product Or Service Code
8. Click ‘run’
9. The output will show you categories of information showing strong affinities. Explore the model by filtering the charts and tables by the category/ies generated. Do this by selecting the filter icon (funnel) next to Category on the table or the Category label at the lower left of the graph.
10. Interesting information may be derived from the groups with fewer rows that may show particularly interesting correlations for a targeted campaign. For example, filter the table and chart on Category 6. This group indicates a group affinity for the attribute values ProductOrServiceCode = “REFRIGERATION AND AIR CONDITIONING COMPONENTS”, fundingAgency = “Veterans Affairs, Department Of”, and a contract award value of $61,148 to $1,173,695 as shown below:
For my organization’s business development activities, if I am in the heating and air business I may elect to focus efforts on medium-sized contracts with Veterans Affairs.