A Domain Intelligence Tool

A new domain intelligence tool is available now. http://www.minerazzi.com/tools/mdm/mdm.php
This tool checks if a brand, product, service, subdomain, initials, or keywords have been registered as a domain name. It helps you to secure the Web presence of your intellectual property while helping you to identify cybersquatters, domain brokers, and domainers.
Update: Minor glitches fixed today. Have fun :)

IP Intelligence on Reddit Banned Domains

One useful application of the Minerazzi’s URL Scoring Tool we just launched consists in doing some IP intelligence on a list of banned domains. Usually, those with a similar or common IP are in a shared hosting environment or have a common ownership, or both. This can be another piece of information that could help you identify those behind a set of domain names.

To do this, just google [banned domains] and follow a result that points to a list of domain names banned by a web property. Then paste the list in the MUST textarea and submit it. You may want to be sure the URLs are carriage return delimited (crd). In general, you could do the same analysis for lists of parked domains, hacker sites, registrar companies, affiliate program urls, etc.

Here is the result of checking this old list of Banned Domains by Reddit. To properly interpret the results, visit  our tool’s page.

 

Status Extracted URL IP Tested URL
http://xeducation.info xeducation.info
http://funny-on-youtube.com funny-on-youtube.com
200 http://echomon.co.uk 108.160.150.154 echomon.co.uk
200 http://www.imagetwist.com 162.159.240.244 http://www.imagetwist.com
200 http://www.imageporter.com 162.159.243.13 http://www.imageporter.com
200 http://blogs.discovermagazine.com 173.226.48.205 blogs.discovermagazine.com
200 http://news.discovery.com 206.190.79.225 news.discovery.com
200 http://www.sciencedaily.com 23.21.113.171 http://www.sciencedaily.com
200 http://imgflash.com/ 66.175.214.67 http://www.imgflash.com
200 http://www.globalpost.com 68.177.32.26 http://www.globalpost.com
200 http://www.businessweek.com 68.177.32.75 http://www.businessweek.com
405 http://bit.ly/ 69.58.188.39 bit.ly/
200 http://medicalxpress.com/ 69.9.167.166 http://www.medicalxpress.com
200 http://phys.org/ 69.9.167.167 http://www.phys.org
200 http://www.theatlantic.com 72.21.91.54 http://www.theatlantic.com
200 http://www.theatlanticcities.com 93.184.215.223 http://www.theatlanticcities.com
200 http://www.thewire.com/ 93.184.215.223 http://www.theatlanticwire.com

Quantum Database Searches: The Next Frontier

Learning about Grover’s Algorithm: Quantum Database Search.

Useful References:

  1. Grover, L. K. (1996). A fast quantum mechanical algorithm for database search.
  2. Grover, L. K. (1997). Quantum Mechanics helps in searching for a needle in a haystack Phys. Rev. Lett. 79 (1997) 325.
  3. Lavor, C., Manssur, L.R.U., and Portugal, R. (2008). Grover’s Algorithm: Quantum Database Search.
  4. Wikipedia. Grover’s algorithm.

XOR and XNOR Searches: Applications to IR, Search Marketing, and Web Mining

This is a follow up on the Beauty of XOR and XNOR searches post, describing possible applications of these search modes to Information Retrieval, Search Marketing, and Web Mining. The post is a snippet taken from http://www.minerazzi.com/help/xor-xnor.php

Enjoy it.

An IR researcher can test the performance of an LSI algorithm with a sample of documents retrieved through XOR and XNOR searches. Said sample should be rich in co-occurrence cases. Using a similar procedure, search marketers or Web intelligence specialists can identify sets of documents that emphasize keywords somehow related through different co-occurrence paths.

An interesting application consists in extracting all the unique terms (or just the high frequency ones) from a text source and constructing an XOR query with these. We may refer to this as XORing a text source. This should help one identify a network of co-occurrence paths over a collection and which documents might be relevant to specific combination of terms from the original source.

The text source can be a title, description, abstract, or paragraph of a document, or even an entire document. However, XORing a large document might be computer-intensive.

A similar exercise can be done by XNORing a text source. In both cases, the resultant output can be used to identify prospective competitors; i.e., documents relevant to similar concepts or belonging to companies within the same business space.

We are currently testing the XOR and XNOR search modes as a query disambiguation strategy.

PS. Today, 1-9-2014, we added new material that discusses these search modes for disambiguation and clustering. :)